++

api_llm_generate/api_llm_generate.py

@@ -219,7 +219,7 @@ def call_llm_generate_with_anthropic(task_data, max_retries=3):
                 # 调用 API（使用流式传输，因为操作可能超过 10 分钟）
                 generated_content = ""
                 with client.messages.stream(
-                    model="MiniMax-M2.7",
+                    model="MiniMax-M2.7-highspeed",
                     system=system_prompt if system_prompt else "You are a helpful assistant.",
                     messages=messages,
                     max_tokens=32768

simple72/Tranformer/ace_lib.py

@@ -75,6 +75,11 @@ def expand_dict_columns(data: pd.DataFrame) -> pd.DataFrame:
         pandas.DataFrame: A new DataFrame with expanded columns.
     """
     dict_columns = list(filter(lambda x: isinstance(data[x].iloc[0], dict), data.columns))
+    
+    if not dict_columns:
+        # 没有字典列，直接返回原数据
+        return data
+    
     new_columns = pd.concat(
         [data[col].apply(pd.Series).rename(columns=lambda x: f"{col}_{x}") for col in dict_columns],
         axis=1,

simple72/Tranformer/output/Alpha_candidates.json

@@ -0,0 +1,274 @@
+{
+    "regression_neut(divide(<data_field/>, sqrt(<data_field/>)), log(cap))": {
+        "template_explanation": "A direct variation of the seed alpha that transforms a generic investment\u2011quality metric using a square\u2011root compression and then removes any linear size effect via regression neutralization against log market cap. This isolates the residual quality signal that is orthogonal to firm size.",
+        "seed_alpha_settings": {
+            "instrumentType": "EQUITY",
+            "region": "IND",
+            "universe": "TOP500",
+            "delay": 1,
+            "decay": 6,
+            "neutralization": "NONE",
+            "truncation": 0.02,
+            "pasteurization": "ON",
+            "unitHandling": "VERIFY",
+            "nanHandling": "ON",
+            "maxTrade": "ON",
+            "maxPosition": "OFF",
+            "language": "FASTEXPR",
+            "visualization": false,
+            "startDate": "2014-01-01",
+            "endDate": "2023-12-31"
+        },
+        "placeholder_candidates": {
+            "<data_field/>": {
+                "type": "data_field",
+                "candidates": []
+            }
+        }
+    },
+    "group_neutralize(ts_rank(<data_field/>, <integer_parameter/>), bucket(rank(cap), range=\"0,1,0.1\"))": {
+        "template_explanation": "Constructs a time\u2011series rank of a fundamental metric over a short window, then removes the systematic size bias by neutralising against market\u2011cap buckets. The result is a size\u2011adjusted relative strength signal that can be compared across the universe.",
+        "seed_alpha_settings": {
+            "instrumentType": "EQUITY",
+            "region": "IND",
+            "universe": "TOP500",
+            "delay": 1,
+            "decay": 6,
+            "neutralization": "NONE",
+            "truncation": 0.02,
+            "pasteurization": "ON",
+            "unitHandling": "VERIFY",
+            "nanHandling": "ON",
+            "maxTrade": "ON",
+            "maxPosition": "OFF",
+            "language": "FASTEXPR",
+            "visualization": false,
+            "startDate": "2014-01-01",
+            "endDate": "2023-12-31"
+        },
+        "placeholder_candidates": {
+            "<data_field/>": {
+                "type": "data_field",
+                "candidates": []
+            },
+            "<integer_parameter/>": {
+                "type": "integer_parameter",
+                "candidates": [
+                    {
+                        "value": 5
+                    },
+                    {
+                        "value": 10
+                    },
+                    {
+                        "value": 20
+                    },
+                    {
+                        "value": 60
+                    },
+                    {
+                        "value": 120
+                    }
+                ]
+            }
+        }
+    },
+    "regression_neut(signed_power(ts_zscore(<data_field/>, <integer_parameter/>), <float_parameter/>), log(cap))": {
+        "template_explanation": "Standardises the investment metric with a rolling Z\u2011score, applies a signed power transformation to capture non\u2011linear relationships, and finally neutralises the effect of log market cap. This approach enhances sensitivity to extreme values while controlling for size.",
+        "seed_alpha_settings": {
+            "instrumentType": "EQUITY",
+            "region": "IND",
+            "universe": "TOP500",
+            "delay": 1,
+            "decay": 6,
+            "neutralization": "NONE",
+            "truncation": 0.02,
+            "pasteurization": "ON",
+            "unitHandling": "VERIFY",
+            "nanHandling": "ON",
+            "maxTrade": "ON",
+            "maxPosition": "OFF",
+            "language": "FASTEXPR",
+            "visualization": false,
+            "startDate": "2014-01-01",
+            "endDate": "2023-12-31"
+        },
+        "placeholder_candidates": {
+            "<data_field/>": {
+                "type": "data_field",
+                "candidates": []
+            },
+            "<integer_parameter/>": {
+                "type": "integer_parameter",
+                "candidates": [
+                    {
+                        "value": 5
+                    },
+                    {
+                        "value": 20
+                    },
+                    {
+                        "value": 60
+                    },
+                    {
+                        "value": 120
+                    },
+                    {
+                        "value": 252
+                    }
+                ]
+            },
+            "<float_parameter/>": {
+                "type": "float_parameter",
+                "candidates": [
+                    {
+                        "value": 0.25
+                    },
+                    {
+                        "value": 0.5
+                    },
+                    {
+                        "value": 1.0
+                    },
+                    {
+                        "value": 2.0
+                    },
+                    {
+                        "value": 3.0
+                    }
+                ]
+            }
+        }
+    },
+    "ts_zscore(ts_delta(<data_field/>, <integer_parameter/>), <integer_parameter/>) - regression_neut(<data_field/>, log(cap))": {
+        "template_explanation": "Combines the short\u2011term change of a metric (captured by its rolling delta and Z\u2011score) with the size\u2011neutralised level of the metric. The difference isolates momentum in the metric that is not explained by firm size.",
+        "seed_alpha_settings": {
+            "instrumentType": "EQUITY",
+            "region": "IND",
+            "universe": "TOP500",
+            "delay": 1,
+            "decay": 6,
+            "neutralization": "NONE",
+            "truncation": 0.02,
+            "pasteurization": "ON",
+            "unitHandling": "VERIFY",
+            "nanHandling": "ON",
+            "maxTrade": "ON",
+            "maxPosition": "OFF",
+            "language": "FASTEXPR",
+            "visualization": false,
+            "startDate": "2014-01-01",
+            "endDate": "2023-12-31"
+        },
+        "placeholder_candidates": {
+            "<data_field/>": {
+                "type": "data_field",
+                "candidates": [
+                    {
+                        "id": "anl39_atanbvps",
+                        "description": "Book value (tangible) per share - most recent fiscal year"
+                    },
+                    {
+                        "id": "anl39_qtanbvps",
+                        "description": "Book value (tangible) per share - most recent quarter"
+                    },
+                    {
+                        "id": "anl39_spvba",
+                        "description": "Book value (Common Equity) per share - most recent fiscal year"
+                    },
+                    {
+                        "id": "anl39_spvbq",
+                        "description": "Book value (Common Equity) per share - most recent quarter"
+                    },
+                    {
+                        "id": "anl4_bvps_high",
+                        "description": "Book value - the highest estimation, per share"
+                    },
+                    {
+                        "id": "anl4_bvps_low",
+                        "description": "Book value - the lowest estimation, per share"
+                    },
+                    {
+                        "id": "anl4_bvps_median",
+                        "description": "Book value per share - Median value among forecasts"
+                    },
+                    {
+                        "id": "anl4_bvps_number",
+                        "description": "Book value per share - number of estimations"
+                    },
+                    {
+                        "id": "est_bookvalue_ps",
+                        "description": "Book value per share - average of estimations"
+                    }
+                ]
+            },
+            "<integer_parameter/>": {
+                "type": "integer_parameter",
+                "candidates": [
+                    {
+                        "value": 5
+                    },
+                    {
+                        "value": 10
+                    },
+                    {
+                        "value": 20
+                    },
+                    {
+                        "value": 60
+                    },
+                    {
+                        "value": 120
+                    }
+                ]
+            }
+        }
+    },
+    "group_rank(ts_rank(<data_field/>, <integer_parameter/>), industry)": {
+        "template_explanation": "First ranks the metric temporally within each stock, then applies a cross\u2011sectional industry ranking. This double\u2011ranking approach extracts the industry\u2011relative performance trend while abstracting from absolute magnitude.",
+        "seed_alpha_settings": {
+            "instrumentType": "EQUITY",
+            "region": "IND",
+            "universe": "TOP500",
+            "delay": 1,
+            "decay": 6,
+            "neutralization": "NONE",
+            "truncation": 0.02,
+            "pasteurization": "ON",
+            "unitHandling": "VERIFY",
+            "nanHandling": "ON",
+            "maxTrade": "ON",
+            "maxPosition": "OFF",
+            "language": "FASTEXPR",
+            "visualization": false,
+            "startDate": "2014-01-01",
+            "endDate": "2023-12-31"
+        },
+        "placeholder_candidates": {
+            "<data_field/>": {
+                "type": "data_field",
+                "candidates": []
+            },
+            "<integer_parameter/>": {
+                "type": "integer_parameter",
+                "candidates": [
+                    {
+                        "value": 10
+                    },
+                    {
+                        "value": 20
+                    },
+                    {
+                        "value": 60
+                    },
+                    {
+                        "value": 120
+                    },
+                    {
+                        "value": 252
+                    }
+                ]
+            }
+        }
+    }
+}

simple72/Tranformer/output/Alpha_generated_expressions_error.json

@@ -0,0 +1 @@
+[]

simple72/Tranformer/output/Alpha_generated_expressions_success.json

@@ -0,0 +1,47 @@
+[
+  "ts_zscore(ts_delta(est_bookvalue_ps, 10), 10) - regression_neut(est_bookvalue_ps, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_number, 120), 120) - regression_neut(anl4_bvps_number, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_low, 60), 60) - regression_neut(anl4_bvps_low, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvba, 20), 20) - regression_neut(anl39_spvba, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvbq, 20), 20) - regression_neut(anl39_spvbq, log(cap))",
+  "ts_zscore(ts_delta(anl39_atanbvps, 120), 120) - regression_neut(anl39_atanbvps, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvbq, 120), 120) - regression_neut(anl39_spvbq, log(cap))",
+  "ts_zscore(ts_delta(anl39_atanbvps, 10), 10) - regression_neut(anl39_atanbvps, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvba, 5), 5) - regression_neut(anl39_spvba, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_high, 120), 120) - regression_neut(anl4_bvps_high, log(cap))",
+  "ts_zscore(ts_delta(anl39_qtanbvps, 20), 20) - regression_neut(anl39_qtanbvps, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_median, 5), 5) - regression_neut(anl4_bvps_median, log(cap))",
+  "ts_zscore(ts_delta(anl39_atanbvps, 20), 20) - regression_neut(anl39_atanbvps, log(cap))",
+  "ts_zscore(ts_delta(est_bookvalue_ps, 5), 5) - regression_neut(est_bookvalue_ps, log(cap))",
+  "ts_zscore(ts_delta(anl39_qtanbvps, 120), 120) - regression_neut(anl39_qtanbvps, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_high, 5), 5) - regression_neut(anl4_bvps_high, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_high, 20), 20) - regression_neut(anl4_bvps_high, log(cap))",
+  "ts_zscore(ts_delta(anl39_atanbvps, 5), 5) - regression_neut(anl39_atanbvps, log(cap))",
+  "ts_zscore(ts_delta(est_bookvalue_ps, 20), 20) - regression_neut(est_bookvalue_ps, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_median, 20), 20) - regression_neut(anl4_bvps_median, log(cap))",
+  "ts_zscore(ts_delta(est_bookvalue_ps, 60), 60) - regression_neut(est_bookvalue_ps, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvbq, 60), 60) - regression_neut(anl39_spvbq, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvba, 10), 10) - regression_neut(anl39_spvba, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvba, 60), 60) - regression_neut(anl39_spvba, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvbq, 10), 10) - regression_neut(anl39_spvbq, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_high, 60), 60) - regression_neut(anl4_bvps_high, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvba, 120), 120) - regression_neut(anl39_spvba, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_low, 120), 120) - regression_neut(anl4_bvps_low, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_median, 60), 60) - regression_neut(anl4_bvps_median, log(cap))",
+  "ts_zscore(ts_delta(anl39_atanbvps, 60), 60) - regression_neut(anl39_atanbvps, log(cap))",
+  "ts_zscore(ts_delta(est_bookvalue_ps, 120), 120) - regression_neut(est_bookvalue_ps, log(cap))",
+  "ts_zscore(ts_delta(anl39_qtanbvps, 60), 60) - regression_neut(anl39_qtanbvps, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_number, 60), 60) - regression_neut(anl4_bvps_number, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_low, 10), 10) - regression_neut(anl4_bvps_low, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_low, 5), 5) - regression_neut(anl4_bvps_low, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_median, 120), 120) - regression_neut(anl4_bvps_median, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_number, 10), 10) - regression_neut(anl4_bvps_number, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_number, 5), 5) - regression_neut(anl4_bvps_number, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_low, 20), 20) - regression_neut(anl4_bvps_low, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_number, 20), 20) - regression_neut(anl4_bvps_number, log(cap))",
+  "ts_zscore(ts_delta(anl39_spvbq, 5), 5) - regression_neut(anl39_spvbq, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_median, 10), 10) - regression_neut(anl4_bvps_median, log(cap))",
+  "ts_zscore(ts_delta(anl4_bvps_high, 10), 10) - regression_neut(anl4_bvps_high, log(cap))",
+  "ts_zscore(ts_delta(anl39_qtanbvps, 5), 5) - regression_neut(anl39_qtanbvps, log(cap))",
+  "ts_zscore(ts_delta(anl39_qtanbvps, 10), 10) - regression_neut(anl39_qtanbvps, log(cap))"
+]

simple72/Tranformer/test_config.json

@@ -1,5 +1,5 @@
 {
-  "LLM_model_name": "MiniMax-M2.7",
+  "LLM_model_name": "MiniMax-M2.7-highspeed",
   "LLM_API_KEY": "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4",
   "llm_base_url": "https://api.minimaxi.com/v1",
   "username": "jack0210_@hotmail.com",

simple72/config.json

@@ -6,11 +6,11 @@
   "llm": {
     "api_key": "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4",
     "base_url": "https://api.minimaxi.com/v1",
-    "model": "MiniMax-M2.7"
+    "model": "MiniMax-M2.7-highspeed"
   },
   "transformer": {
     "top_n_datafield": 30,
     "data_type": "MATRIX",
-    "alpha_id": "pw13nJlo"
+    "alpha_id": "akWG7Lnx"
   }
 }

simple72/config_minimax.json

@@ -6,7 +6,7 @@
   "llm": {
     "api_key": "sk-cp-l_as8mjqPhsOIny9IFKZ8jzA92z1c0eRwchldhEf4KzQjs9cjVknV2o7VNCcvYUXsXFq7uF4aSgp2RxxmUHLXwPGKgIvzedM70_XUIXiBB3gu_UmLDQLfh4",
     "base_url": "https://api.minimaxi.com/v1",
-    "model": "MiniMax-M2.7"
+    "model": "MiniMax-M2.7-highspeed"
   },
   "transformer": {
     "top_n_datafield": 30,

simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv

@@ -0,0 +1,144 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+anl39_2_curfperiodend,End date of the current fiscal period as of the point in time,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,291,849,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_2_curfyearend,Current fiscal year-end month as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,165,304,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_2_curperiodnum,"Number of the current fiscal period within the fiscal year (e.g., quarter 1–4)","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,96,160,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_2_cursharesoutstanding,Current shares outstanding for the security as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.6696,136,247,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_2curfperiodend,End date of the current fiscal period as of the PIT date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7442,102,173,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_2curfyearend,"Fiscal year-end for the entity as of the current point in time (e.g., month of fiscal year end)","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7442,93,140,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_2cursharesoutstanding,Shares outstanding for the security as of the current point in time,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.6723,117,186,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_aepsinclxo,EPS including extraordinary items - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9792,323,722,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_agrosmgn,Gross Margin - 1st historical fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8688,209,455,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_agrosmgn2,Gross Margin - 2nd historical fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8694,124,180,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_analyststart,Point-in-time effective start date when an analyst begins coverage of a given security or company,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,1.0,167,486,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_atanbvps,Book value (tangible) per share - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9841,222,422,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_curfperiodend,Calendar date of the current fiscal period end as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,61,93,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_curfyearend,Fiscal year-end month for the entity as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7415,65,98,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_cursharesoutstanding,Shares outstanding for the security as of the point-in-time date,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.6696,81,117,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_epschngin,EPS Change % - prior quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9161,143,253,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_ghcspea,EPS Change % - year over year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9716,216,456,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_ghcspemtt,"Percent change in EPS, trailing 12 months versus the prior trailing 12 months","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.852,123,192,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_grosmgn5yr,Gross Margin 5-year average,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7857,135,194,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_ptmepsincx,Earnings per share including extraordinary items for the prior trailing 12-month period,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8697,126,217,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_qepsinclxo,EPS including extraordinary items - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9415,161,253,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_qgrosmgn,Gross Margin - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.814,123,267,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_qtanbvps,Book value (tangible) per share - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8196,70,89,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_qtotd2eq,Total debt to total equity ratio for the most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8012,56,71,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_qtotd2eq2,"Total debt/total equity - most recent quarter, 1 year ago","{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.7525,51,66,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_rasv2_atotd2eq,Total debt to total equity ratio for the most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.968,241,691,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_roxlcxspea,EPS excluding extraordinary items - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9792,129,264,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_roxlcxspeq,EPS excluding extraordinary items - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9415,205,579,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_rygnhcspe,EPS Change % - most recent quarter 1 year ago,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9039,168,522,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_spvba,Book value (Common Equity) per share - most recent fiscal year,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9841,154,263,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_spvbq,Book value (Common Equity) per share - most recent quarter,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8196,79,98,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_ttmepsincx,EPS including extraordinary items - trailing 12 month,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9261,302,577,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_ttmgrosmgn,Gross Margin - trailing 12 month,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8059,119,221,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_xlcxspemtp,Earnings per share excluding extraordinary items for the prior trailing 12-month period,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.8697,90,149,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl39_xlcxspemtt,EPS excluding extraordinary items - trailing 12 month,"{'id': 'analyst39', 'name': 'Analyst estimates & financial ratios'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-esg', 'name': 'ESG'}",IND,1,TOP500,MATRIX,0.9507,0.9261,239,483,1.5,[],analyst39,Analyst estimates & financial ratios,analyst,Analyst,analyst-esg,ESG
+anl4_afv4_cfps_high,Cash Flow Per Share - The highest estimation for the annual forecast,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,144,295,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_cfps_low,Cash Flow Per Share - The lowest estimation for the upcoming fiscal year,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,71,97,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_cfps_mean,Cash Flow Per Share - average of estimations for the annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,79,105,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_cfps_median,Cash Flow Per Share - Median value among forecasts for the annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,68,103,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_cfps_number,Cash Flow Per Share - number of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5056,76,99,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_div_high,Dividend per share - The highest estimation for the annual forecast.,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,353,2247,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_div_low,Dividend - The lowest estimation for the annual forecast,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,267,1052,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_div_mean,Dividend per share - average of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,345,1496,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_div_median,Dividend per share - Median value among forecasts,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,325,3689,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_div_number,Number of estimations for Dividend per share - annually,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9249,150,404,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_div_std,Dividend per share - standard deviation of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6713,134,211,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_dts_spe,Earnings per share - standard deviation of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6542,147,475,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_eps_high,Earnings per share - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,756,6633,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_eps_low,Earnings per share - The lowest estimation for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,280,1314,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_eps_mean,Earnings per share - mean of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,892,7471,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_eps_number,Earnings per share - number of estimations for annual frequency,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,320,2451,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_afv4_median_eps,Earnings per share - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9687,543,5292,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_bvps_high,"Book value - the highest estimation, per share","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,49,75,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_bvps_low,"Book value - the lowest estimation, per share","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,51,76,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_bvps_median,Book value per share - Median value among forecasts,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,55,69,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_bvps_number,Book value per share - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,48,62,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_dts_ptp,Pretax income- std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.527,65,92,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebit_high,Earnings before interest and taxes - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,313,2012,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebit_low,Earnings before interest and taxes - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,319,2270,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebit_median,Earnings before interest and taxes - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,341,2150,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebit_number,Earnings before interest and taxes - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,105,160,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebit_std,Earnings before interest and taxes - std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4586,66,81,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebitda_high,"Earnings before interest, taxes, depreciation and amortization - The highest estimation","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,158,565,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebitda_low,"Earnings before interest, taxes, depreciation and amortization - The lowest estimation","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,200,876,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebitda_number,"Earnings before interest, taxes, depreciation and amortization - number of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,84,133,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ebitda_std,"Earnings before interest, taxes, depreciation and amortization - std of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4949,59,83,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsa_high,Earnings per share adjusted by excluding extraordinary items and stock option expenses - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,67,99,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsa_low,Earnings per share adjusted by excluding extraordinary items and stock option expenses - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,79,143,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsa_median,Earnings per share adjusted by excluding extraordinary items and stock option expenses - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,83,152,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsa_number,Earnings per share adjusted by excluding extraordinary items and stock option expenses - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,97,190,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsr_high,GAAP Earnings per share - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,60,99,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsr_low,GAAP Earnings per share - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,54,72,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_epsr_number,GAAP Earnings per share - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,54,77,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_gric_high,Gross income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,45,69,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_gric_low,Gross income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,50,69,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_gric_median,Gross income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,42,58,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_gric_number,Gross income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,117,332,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_median_epsreported,GAAP Earnings per share - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,72,107,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_medianepsbfam,"Earnings before interest, taxes, depreciation and amortization - median of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,159,755,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofit_high,Net profit- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,368,2829,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofit_low,Net profit- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,279,1511,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofit_median,Net profit- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,309,1021,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofit_number,Net profit- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,89,175,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofit_std,Net profit- std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5654,45,68,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofita_high,Adjusted net income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,209,659,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofita_low,Adjusted net income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,290,2070,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofita_median,Adjusted net income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,217,910,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_netprofita_number,Adjusted net income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,87,165,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptp_high,Pretax income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,155,473,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptp_low,Pretax income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,157,490,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptp_median,Pretax income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,167,791,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptp_number,Pretax income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,76,138,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptpr_high,Reported Pretax income- The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,114,582,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptpr_low,Reported Pretax income- The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,68,142,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptpr_median,Reported Pretax income- median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,58,89,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_ptpr_number,Reported Pretax income- number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,74,170,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_qfv4_dts_spe,Earnings per share - standard deviation of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5408,54,68,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_qfv4_eps_high,Earnings per share - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,258,1124,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_qfv4_eps_low,Earnings per share - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,533,6028,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_qfv4_eps_mean,Earnings per share - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,253,1098,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_qfv4_eps_number,Earnings per share - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,115,395,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_qfv4_median_eps,Earnings per share - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9424,422,5384,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_totassets_high,Total Assets - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,38,68,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_totassets_low,Total Assets - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,30,36,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_totassets_median,Total Assets - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,26,38,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl4_totassets_number,Total Assets - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,34,51,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+earnings_per_share_adjusted_estimate_standard_deviation,Earnings per share adjusted by excluding extraordinary items and stock option expenses - std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.3898,47,57,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_bookvalue_ps,Book value per share - average of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4719,71,176,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_ebit,Earnings before interest and taxes - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9106,476,4982,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_ebitda,"Earnings before interest, taxes, depreciation and amortization - mean of estimations","{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8721,284,2635,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_epsa,Earnings per share adjusted by excluding extraordinary items and stock option expenses - average of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.7992,131,409,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_epsr,GAAP Earnings per share - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.651,94,276,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_grossincome,Gross income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5829,90,177,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_netprofit,Net profit- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9623,464,6015,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_netprofit_adj,Adjusted net income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.8995,399,3908,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_ptp,Pretax income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.95,196,803,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_ptpr,Reported Pretax income- mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6358,95,236,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+est_tot_assets,Total Assets - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4853,54,94,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+highest_sales_estimate,Sales - The highest estimation for the annual period,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,172,454,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+lowest_sales_estimate,Sales - The lowest estimation for the annual period,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,109,233,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+median_sales_estimate,Sales - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,150,555,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+net_income_adjusted_estimate_standard_deviation,Adjusted net income- std of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.4938,55,75,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+reporting_currency_code_9,Home currency of instrument,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,1.0,145,801,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_average_annual,Sales - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,208,813,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_average_quarterly,Sales - mean of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,123,249,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_count_2,Number of Sales estimates,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9672,81,151,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_count_quarterly,Sales - number of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,79,138,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_dispersion,Standard deviation of Sales estimations for the annual period.,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.6465,89,150,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_maximum_quarterly,Sales - The highest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,132,434,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_median_quarterly,Sales - median of estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,132,661,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_minimum_quarterly,Sales - The lowest estimation,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.9613,237,2093,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+sales_estimate_stddev_quarterly,Standard deviation of Sales estimations,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.561,61,110,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+shareholders_equity_estimate_average_qf,Average value of broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,37,57,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+shareholders_equity_estimate_count_qf,Number of broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,42,49,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+shareholders_equity_estimate_maximum_qf,Highest value among broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,50,67,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+shareholders_equity_estimate_median_qf,Median value of broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,32,38,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+shareholders_equity_estimate_minimum_qf,Lowest value among broker estimates for shareholders' equity (quarterly frequency).,"{'id': 'analyst4', 'name': 'Analyst Estimate Data for Equity'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,1.0,0.5119,40,51,1.5,[],analyst4,Analyst Estimate Data for Equity,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates
+anl81_class,"Creditwrothiness class indices in integers: 9(AAA), 5(AA), 4(A), 1(BBB), 2(BB), 3(B), 7(CCC), 8(CC), 6(C), 10(D)","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,1.0,0.2609,79,129,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings
+anl81_confidence_level_percent,"Confidence level of the assessment, measuring data completeness and reliability as a percentage from 0 to 100","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2609,57,77,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings
+anl81_probability_of_default_percent,"Estimated probability of default over the rating horizon, expressed as a percentage from 0 to 100","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2609,31,41,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings
+default_likelihood_percent,"Previous-period probability of default estimated by mode_finance, expressed as a percentage from 0 to 100","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2711,24,27,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings
+financial_data_completeness_percent,"Confidence level for the lagged assessment, 0–100%, indicating completeness/reliability of available data","{'id': 'analyst81', 'name': 'Creditworthiness model'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-ratings', 'name': 'Analyst Ratings'}",IND,1,TOP500,MATRIX,0.9507,0.2711,32,47,1.5,[],analyst81,Creditworthiness model,analyst,Analyst,analyst-analyst-ratings,Analyst Ratings
+anl9_estanalystmap,Human-readable name/description of the analyst corresponding to estimateAnalystIx,"{'id': 'analyst9', 'name': 'Analyst Estimate Daily Data'}","{'id': 'analyst', 'name': 'Analyst'}","{'id': 'analyst-analyst-estimates', 'name': 'Analyst Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.8467,92,159,1.5,[],analyst9,Analyst Estimate Daily Data,analyst,Analyst,analyst-analyst-estimates,Analyst Estimates

simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv

@@ -0,0 +1,9 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+ern3_all_delay_1_next_interval,"Number of trading days before the next earnings report date for all Asia (ASI) equities, according to the 1-day delayed earnings calendar","{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7091,353,601,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_all_delay_1_next_reptime,Scheduled time of the next upcoming earnings report announcement,"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7091,254,430,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_all_delay_1_pre_interval,"Number of trading days before the last earnings report date (should be negative or zero), reflecting timing relative to previous earnings for all Asia (ASI) equities, in the 1-day delayed calendar","{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.872,514,1065,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_all_delay_1_pre_reptime,Time of the most recent previous earnings report announcement,"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.872,323,573,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_next_interval,Number of trading days until the next scheduled earnings report date,"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7094,316,558,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_next_reptime,Scheduled time for the next earnings report (in HHMM or session code),"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.7094,203,341,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_pre_interval,Number of trading days elapsed since the previous earnings report date (always non-positive),"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.8721,467,939,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates
+ern3_pre_reptime,Scheduled or actual event time of the previous earnings report (in HHMM or session code),"{'id': 'earnings3', 'name': 'Earnings Date Data'}","{'id': 'earnings', 'name': 'Earnings'}","{'id': 'earnings-earnings-estimates', 'name': 'Earnings Estimates'}",IND,1,TOP500,MATRIX,0.9507,0.8721,349,627,1.2,[],earnings3,Earnings Date Data,earnings,Earnings,earnings-earnings-estimates,Earnings Estimates

simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv

@@ -0,0 +1,689 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+enterprise_value_to_ebitda_current,Current enterprise value divided by EBITDA ratio,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9452,681,2580,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+financial_reporting_currency_code_3,Reporting currency used in the company’s financial statements,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,39,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_1_reptoprcexrate,Reporting Currency to base/pricing currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,248,1076,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_1_usdtorepexrate,Exchange rate from reporting currency to USD,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,188,1012,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2_reptoprcexrate,Reporting Currency to base currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,140,853,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2_usdtorepexrate,Reporting Currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,77,144,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2anrhsfcq,Cash Flow per share - most recent quarter - 1 (not annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8691,238,1121,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2qe2dtlq,Long-term debt to equity ratio for the most recent quarter one year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7431,78,102,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2rhsfca,Cash flow per share for the prior fiscal year (as defined by the dataset; divided by average shares outstanding),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9656,299,1391,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2rhsfcq,"Cash Flow per share - most recent quarter, 1 year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8501,123,576,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_2tcpngmpoa,Operating Margin - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9643,337,1550,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_3_reptoprcexrate,Exchange rate from the company’s reporting currency to the dataset’s base price currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,38,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_3_usdtorepexrate,Exchange rate from USD to reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,47,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_4_reptoprcexrate,Exchange rate from reporting currency to base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,19,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_4_usdtorepexrate,Reporting currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_5_reptoprcexrate,Reporting Currency to base currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_5_usdtorepexrate,Reporting currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,25,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_6_reptoprcexrate,Exchange rate from reporting currency to base/price currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,31,55,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_6_usdtorepexrate,Exchange rate from reporting currency to USD,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,41,58,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_7_reptoprcexrate,Exchange rate from the company’s reporting currency to the dataset’s base/pricing currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_7_usdtorepexrate,Exchange rate from the reporting currency to USD,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,23,44,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_8_reptoprcexrate,Reporting Currency to base currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,22,33,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_8_usdtorepexrate,Reporting currency to USD exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,30,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_a2netmrgn,Net Profit Margin % - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9651,62,76,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aastturn,Asset turnover - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9078,301,1625,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_abepsxclxo,Basic earnings per share excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,205,1018,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_acapspps,Capital Spending per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9443,66,79,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_acogs,Cost of goods sold for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8727,51,83,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_acurast,Total current assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8585,52,72,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_acurliab,Current liabilities - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8585,57,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_acurratio,Current ratio for the most recent fiscal year (current assets divided by current liabilities),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8583,186,856,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_adebteps,Debt service to earnings per share ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7178,38,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_adepexp,Depreciation expense from statement of cash flows - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9419,43,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_adeprescfz,"Accumulated depreciation, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9042,33,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_adiv5yavg,Dividend per share 5-year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6901,114,607,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_adivchg,Year-over-year percentage change in annual dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.713,108,154,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_adivshr,Dividend per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8202,238,1387,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aebit,Earnings before interest and taxes for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,53,88,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aebitd,EBITDA for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9674,54,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aebitd2,EBITDA for the previous fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9665,49,70,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aebitd5yr,EBITD Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.889,63,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aebitdmg,EBITD Margin - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9666,75,99,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aebtnorm,Normalized earnings before taxes for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,40,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aepsinclxo,Earnings per share including extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,50,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aepsnorm,EPS normalized - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,91,143,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_agrosmgn,Gross Margin - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8599,53,64,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_agrosmgn2,Gross Margin - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8606,28,34,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aintcov,"Interest coverage ratio for the most recent fiscal year, calculated as EBIT divided by interest expense","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6589,27,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aintexpz,Total interest expense for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8088,24,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ainventory,Inventory - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7992,41,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ainvturn,Inventory turnover - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.782,24,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_altd2ast,Long-term debt to total assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9116,27,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_altd2cap,Long-term Debt to Total Capital: long-term debt divided by total capital (short-term debt + current portion of long-term debt + long-term debt + capitalized leases + shareholders’ equity),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.911,25,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_altd2eq,Long-term Debt to Equity: long-term debt divided by total shareholders’ equity,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9561,35,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_altdps,Long-term debt per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9117,25,27,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ani,Net income (earnings after taxes) for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,46,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aniac,Net income available to common shareholders for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,30,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aniacinclx,Net income available to common including extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,19,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aniacnorm,Normalized net income available to common (excludes unusual/one-time items) for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,39,68,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_anichg,Year-over-year percentage change in net income,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9602,50,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aniexclxor,Net income excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,17,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aniinclxor,Net income including extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,19,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aninorm,Net income after taxes excluding unusual or one-time items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,33,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aniperemp,Net income per employee for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7994,18,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_annperiods,Number of historical periods - Annual,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,31,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_anrhsfcq,"Cash flow per share for the most recent quarter, not annualized","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8698,32,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_apayratio,Payout ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8654,51,75,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_apayratio2,Payout ratio for the prior fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8672,38,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_apeexclxor,Price-to-earnings ratio excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8852,78,134,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_apenorm,Normalized price-to-earnings ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.889,110,199,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_apr2rev,Price to sales - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9671,97,149,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_apr2tanbk,Price to Tangible Book - most fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9315,59,91,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aprice2bk,Price-to-book ratio for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9574,135,325,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aptmgnpct,Pretax margin - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9671,28,33,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aptmgnpct2,Pretax Margin - 2nd historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9651,25,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aquickrati,Quick ratio for the most recent fiscal year: (cash + short-term investments + accounts receivable) divided by current liabilities,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7934,46,58,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arecturn,Receivables turnover - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.89,57,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arecvbl,Receivables - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.906,23,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arev,Total revenue (sales) for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,27,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arevchg,Year-over-year percentage change in revenue,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9586,50,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arevperemp,Revenue per employee - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7991,23,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arevps,Revenue per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,45,58,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_arevstrt,"Reinvestment rate for the most recent fiscal year, defined as 100 minus the payout ratio (percent of earnings retained)","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8862,33,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aroa5yavg,Five-year average return on average assets,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8909,41,54,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aroapct,Return on average assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9664,55,98,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aroe5yavg,Five-year average return on average equity,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8791,35,59,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aroepct,Return on average equity for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9523,27,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aroi5yravg,Five-year average return on investment,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8204,26,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_aroipct,Return on investment for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8936,17,23,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_asga2rev,SG&A expenses / net sales - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9079,34,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ata,Total assets - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,33,48,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atachg,Year-over-year percentage change in total assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9663,31,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atanbvdolr,"Tangible book value in dollars, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,33,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atanbvps,Tangible book value per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,49,60,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ataxpd,Income taxes paid in the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9675,28,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ataxrat5yr,Five-year average effective tax rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8186,30,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ataxrate,Effective tax rate for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8899,41,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ataxrate2,Effective tax rate for the prior fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8838,42,47,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atmtt,"Total assets, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.748,22,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotce,Common equity (book value) - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,32,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotd,Total debt - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9712,19,21,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotd2ast,Total debt to total assets for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9712,29,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotd2cap,"Total debt to total capital percentage, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9118,30,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotd2eq,Total debt to shareholders’ equity for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.957,54,75,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotltd,Long-term debt (total) - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9704,8,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atotse,Shareholders' equity - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,32,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_atq,Total assets - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,15,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_awcappspr,"Working capital per share divided by price, most recent fiscal year","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8583,44,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_beta,Beta,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.863,36,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_beta_down,Downside beta calculated during down-market periods,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.909,62,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_beta_up,Upside beta calculated during up-market periods,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9299,38,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_bvtrendgr,Five-year compound annual growth rate of book value per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8716,38,48,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_cainmtt,Net income available to common shareholders over the trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,118,800,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_cainq,Net income available to common shareholders for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,30,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_cftrendgr,5-year compound annual growth rate of cash flow,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8059,34,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_chpctpricemtd,Month-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,45,63,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_chpctpriceqtd,Quarter-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9718,68,112,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_chpctpricewtd,Week-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,59,114,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_csptrendgr,Five year compound annual growth rate of capital spending,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8494,18,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_dai,"Dividend rate, indicated annual","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7901,73,100,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_debtcap_a,Debt-to-capitalization ratio for the last fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.913,33,36,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_debtcap_i,Debt-to-capitalization ratio for the latest interim period,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7573,9,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_div,Dividends paid from the statement of cash flows - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.799,32,34,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_divgrpct,Three-year compound annual growth rate of dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6326,64,103,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_divnq,Dividend - next quarterly declared,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8009,55,100,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_divnqpdt,Dividend - next quarterly pay date,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7788,69,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_divnqxdt,Dividend - next quarterly ex-date,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7909,125,197,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_divtrend10,Ten-year compound annual growth rate of dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5005,44,54,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_divtrendgr,Five-year compound annual growth rate of dividends per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5946,38,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_dvolshsout,Daily trading volume as a percentage of shares outstanding,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,27,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ebitda2ev,"EBITDA divided by Enterprise Value, current","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9665,206,462,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ebitda2ev_a,EBITDA to enterprise value using last fiscal year EBITDA and current enterprise value,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9631,254,1450,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ebitda2ev_ttm,Ratio of EBITDA to enterprise value for the trailing twelve months (current),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8669,58,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_epschngin,Percent change in EPS from the prior quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9062,23,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_epstrend10,Ten-year compound annual growth rate of earnings per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.622,28,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_epstrendgr,EPS growth rate over the past 5 years,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.754,52,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ev2ebitda_cur,"Enterprise value divided by EBITDA, current","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9452,292,1702,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ev2rev_cur,"Enterprise value divided by revenue, current","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8989,91,156,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ev_cur,Current Enterprise Value (market capitalization plus net debt),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,106,163,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_fcf1a,Free cash flow - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9533,54,67,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_fcfmtt,Free cash flow - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2714,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_fcfq,Free cash flow for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5027,6,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ghcspea,Year-over-year percent change in annual earnings per share compared to the previous fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9602,42,70,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ghcspemtt,Percent change in trailing-twelve-month EPS versus the prior trailing-twelve-month period,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8434,29,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_gihn,12-month high price,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,74,115,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_gmntrendgr,Five-year growth rate of gross margin,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7887,20,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_grosmgn5yr,Gross Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7779,11,15,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_gvary5ep,Five-year average P/E excluding extraordinary items,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7262,37,44,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_gvay5dly,Dividend yield - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6365,44,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_histrelpe,Historical relative price-to-earnings ratio,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7512,58,71,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_hsca,Cash and cash equivalents at fiscal year-end,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9727,45,52,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_inmtt,Net income after taxes over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,54,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_inq,Net income (earnings after taxes) for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,38,55,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_lbvcerq,Receivables - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7495,11,11,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_lcxspebmtp,EPS Basic excluding extraordinary items - prior trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,9,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_lcxspebmtt,EPS Basic excluding extraordinary items - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,62,85,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_lta,Total liabilities - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,26,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ltq,"Total liabilities, most recent quarter","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,11,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_margin5yr,Net Profit Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8909,23,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_mvolshsout,Monthly trading volume as a percentage of shares outstanding,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,32,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_netdebt_a,Net debt (total debt minus cash and equivalents) for the last fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,26,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_netdebt_i,Net debt (total debt minus cash and equivalents) for the latest interim period,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8103,13,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ngmpnmtp,Net Profit Margin % - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8599,20,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ngmpnmtt,Net Profit Margin % - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9153,25,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ngmtpmtt,Pretax margin over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9153,52,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_nichngin,Percent change in net income versus the prior quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9063,17,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_nichngyr,Percent change in net income for the most recent quarter versus the same quarter a year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8948,128,937,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_nigrpct,Growth rate % - net income,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7977,24,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_nitrendgr,Five-year compound annual growth rate of net income,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7542,25,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_nlow,12-month low price,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,43,61,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_npmtrendgr,Five-year growth rate of net profit margin,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7507,28,33,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_nprice,Closing price or last bid,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,57,82,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_oxlcxebepp,"Basic P/E excluding extraordinary items, prior trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7729,17,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_oxlcxspebq,Basic earnings per share excluding extraordinary items for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,28,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_payout5yr,Five-year average payout ratio,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7576,26,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_peexclxor,"P/E excluding extraordinary items, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8433,55,84,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pehigh,High value of P/E excluding extraordinary items,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8707,19,20,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pelow,Low value of P/E excluding extraordinary items,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8707,30,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ppeexclxor,"P/E excluding extraordinary items, prior trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7729,16,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pr1dayprc,Percent change in the stock price for the most recent trading day,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,50,115,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pr2tanbk,Price to Tangible Book - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7768,12,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pr5dayprc,Percent change in the stock price over the last 5 trading days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,77,184,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_price2bk,Price-to-book ratio based on the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7994,12,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_price2bk2,"Price to Book - most recent quarter, 1 year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7229,8,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_priceavg150day,Average price of the last 150 days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.963,90,132,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_priceavg200day,Average price of the last 200 days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9567,67,102,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_priceavg50day,Average price of the last 50 days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.972,58,80,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_prytdpct,Year-to-date price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9625,39,64,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_prytdpctr,Relative (S&P500) price percent change - Year to Date,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9625,58,99,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ptmepsincx,EPS including extraordinary items - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,11,15,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ptmintcov,"Interest coverage for the prior trailing 12 months, defined as EBIT divided by interest expense","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6945,5,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ptmpr2rev,Price to sales - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8344,15,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ptmrev,Revenue for the prior trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,14,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ptmroepct,Return on average equity for the prior trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6641,6,6,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pxepedmtt,Depreciation expense from statement of cash flows - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.265,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_pxepedq,Depreciation expense from the statement of cash flows - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4903,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcapspps,Capital Spending per share - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4933,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcash,Cash and cash equivalents for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,11,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcogs,Cost of goods sold for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8134,15,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcurast,Current assets - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7119,7,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcurliab,Current liabilities - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7115,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcurratio,Current ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7115,12,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qcurratio2,"Current ratio - most recent quarter, 1 year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6634,7,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qdebteps,Debt service to earnings per share ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.721,19,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qe2dtlq,Long-term debt to shareholder equity ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.785,14,21,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qebit,Earnings before interest and taxes for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,22,28,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qebitd,EBITDA for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8799,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qepsinclxo,Earnings per share including extraordinary items for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,35,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qgrosmgn,Gross Margin - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8064,38,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qinventory,Inventory for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6405,5,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qltd2ast,Long-term debt to total assets for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7486,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qltd2cap,"Long-term debt to total capital ratio for the most recent quarter (total capital includes short-term debt, current portion of long-term debt, long-term debt, capitalized leases, and shareholders’ equity)","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7482,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qniperemp,Net Income per employee - most recent quarter (annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7878,9,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qpayratio,Payout ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7464,17,22,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qpr2rev,Price to sales - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9286,51,65,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qquickrat2,Quick ratio for the most recent quarter one year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5937,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qquickrati,Quick ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6362,7,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qrev,Revenue for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,16,24,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qrevperemp,Revenue per employee - most recent quarter (annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7878,11,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qrevps,Revenue/share - most recent quarter (annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,22,34,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qrevpsna,Revenue/share - most recent quarter (not annualized),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,19,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qrevstrt,"Reinvestment rate for the most recent quarter; 100 minus payout ratio, percent of earnings retained","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8724,16,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qsga2rev,SG&A expenses / net sales - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8624,29,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtachg,Year over year percent change in total assets as of one year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.748,3,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtanbvdolr,"Tangible book value in dollars, most recent quarter","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,5,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtanbvps,Tangible book value per share for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,15,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtaxpd,Income taxes paid in the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9306,37,54,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtaxrate,Effective tax rate for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.873,44,73,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotce,Common equity (book value) - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,4,6,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotce2,"Common equity (book value), most recent quarter one year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7572,6,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotd,Total debt - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8027,5,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotd2ast,Total debt to total assets for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8026,7,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotd2cap,Ratio of total debt to total capital for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7552,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotd2eq,Total debt to shareholder equity ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.792,10,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotd2eq2,"Total debt to shareholder equity ratio for the most recent quarter, measured one year ago","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7447,9,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotltd,"Long-term debt (total), most recent quarter","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7957,6,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtotse,Shareholders' equity - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,8,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qtrperiods,Number of historical quarterly periods available for the company,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,12,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_qwcappspr,Working capital per share divided by current price for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7115,7,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_revchngin,Percentage change in revenue versus the immediately preceding quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9056,25,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_revchngyr,Percent change in revenue for the most recent quarter versus the same quarter a year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8939,36,45,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_revgrpct,Three-year revenue growth rate (percent),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9333,19,23,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_revps5ygr,Five-year growth rate of revenue per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8804,23,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_revtrend10,Ten-year revenue growth rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7186,28,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_revtrendgr,Five-year revenue growth rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8804,24,27,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rhsfca,Cash flow per share for the most recent fiscal year (as defined by the dataset; divided by average shares outstanding),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9668,62,88,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rhsfcf1a,Free cash flow per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9533,36,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rhsfcf2a,Free cash flow per share for the fiscal year prior to the most recent (FCF divided by average shares outstanding),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9665,30,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rhsfcmtp,Cash Flow per share - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8082,7,11,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rhsfcmtt,Cash Flow per share - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8591,28,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rhsfcq,"Cash flow per share for the most recent quarter, annualized","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8698,26,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_roxlcniep,"P/E including extraordinary items, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8433,63,126,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_roxlcxebep,"Basic P/E excluding extraordinary items, trailing twelve months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8382,18,21,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_roxlcxspea,Earnings per share excluding extraordinary items for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,23,27,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_roxlcxspeq,Earnings per share excluding extraordinary items for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,29,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rtcpkw25rp,Relative (S&P500) price percent change - 52 week,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9395,51,63,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rtcpkw31rp,Relative (S&P500) price percent change - 13 week,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9718,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rtcpkw40rp,Four-week price percent change relative to the S&P 500,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,46,60,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rtcpkw62rp,Relative (S&P500) price percent change - 26 week,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9606,19,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ry5ngmpo,Operating Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8893,22,35,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ry5ngmtp,Pretax Margin - 5 year average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8909,18,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_rygnhcspe,Year-over-year percent change in EPS for the most recent quarter compared to the same quarter last year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8948,28,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spdq,Dividend - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9245,56,72,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spdtlq,LT debt/share - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7486,9,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spfcfrpa,Price to Free Cash Flow per Share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6076,40,47,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spfcrpa,Price to Cash Flow per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9231,134,272,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spfcrpmtp,Price to Cash Flow per share - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7539,11,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spfcrpmtt,Price to Cash Flow per share - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8262,21,25,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spfcrpq,Price to Cash Flow per share - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8387,50,65,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_sphsca,Cash per share - most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9727,35,49,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_sphscq,Cash and equivalents per share for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,9,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spvba,Book value of common equity per share for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,37,51,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_spvbq,Book value (common equity) per share for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8102,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tbea,Earnings before taxes for the most recent fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9679,29,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tbemtt,Earnings before taxes over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,75,109,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tbeq,Earnings before taxes for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9308,53,76,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpaormtp,Return on average assets for the prior trailing twelve months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6735,4,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpaormtt,Return on average assets over the trailing 12 months (percentage),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7459,5,5,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpkw25rp,Percent change in the stock price over the last 52 weeks,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9395,84,173,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpkw31rp,Percent change in the stock price over the last 13 weeks,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9718,35,50,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpkw40rp,Four-week price percent change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9721,65,94,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpkw62rp,Percent change in the stock price over the last 26 weeks,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9606,51,111,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpngmpna,Net Profit Margin % - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9671,26,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpngmpnq,Net Profit Margin % - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9286,31,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpngmpoa,Operating margin - 1st historical fiscal year,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9667,18,25,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpngmpoq,Operating margin - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.926,23,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcpngmtpq,Pretax margin - most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9286,26,37,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_tcprgspe,Three-year compound annual growth rate of earnings per share,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7974,12,20,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmastturn,Asset turnover - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6996,12,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmcapspps,Capital Spending per share - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.266,3,3,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmcogs,Cost of goods sold over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8041,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmdebteps,Debt Service to EPS - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6978,15,18,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmdivshr,Dividends per share - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9151,35,46,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmebit,EBIT over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,30,62,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmebitd,EBITDA over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8673,7,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmebitdmg,EBITD Margin - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8667,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmebitdps,EBITD per share - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8673,32,40,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmepsincx,EPS including extraordinary items - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,89,173,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmgrosmgn,Gross margin over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7986,19,23,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmintcov,Interest coverage ratio (EBIT divided by interest expense) over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7424,14,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttminvturn,Inventory turnover - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5781,8,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmnichg,Percent change in trailing 12-month net income versus the prior TTM,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8434,19,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmniperem,Net Income per employee - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7731,8,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmopmgn,Operating margin over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9127,44,61,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmpayrat,"Payout ratio over the trailing 12 months, defined as cash dividends as a percent of earnings","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8414,19,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmpehigh,"P/E excluding extraordinary items high, trailing 12 months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6193,8,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmpelow,"P/E excluding extraordinary items low, trailing 12 months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6193,6,6,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmpr2rev,Price to sales - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9153,48,65,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmrecturn,Receivables turnover - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6834,12,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmrev,Revenue over the trailing 12 months; units: millions,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,21,25,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmrevchg,"Percent change in revenue, trailing 12 months over trailing 12 months","{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8428,19,19,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmrevpere,Revenue per employee - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7732,11,11,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmrevps,Revenue/share - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,36,43,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmrevstrt,Reinvestment rate over the trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8446,20,24,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmroepct,Return on average equity - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7357,12,14,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmroipct,Return on investment - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6889,7,9,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmsga2rev,SG&A expenses / net sales - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8521,14,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmtaxpd,Taxes paid over the trailing 12 months; units: millions,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,62,78,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_ttmtaxrate,Tax rate - trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.8475,35,41,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_voctniq,Interest coverage ratio for the most recent quarter,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7615,20,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_vol10davg,Volume - avg. trading volume for the last ten days,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,31,39,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_vol1davg,Volume - 1 Day Average,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,21,24,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_vol1dprc,Volume - 1 Day % Change,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,25,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_vol3mavg,Volume - avg. trading volume for the last 3 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9728,21,42,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_xlcxspemtp,EPS excluding extraordinary items - prior trailing 12 month,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.861,22,47,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_xlcxspemtt,EPS excluding extraordinary items - trailing 12 months,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.916,48,75,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_yield,Dividend yield - indicated annual dividend divided by closing price,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7901,229,493,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd17_yragoprc1,Price - year ago,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9395,29,38,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+gross_margin_trailing_twelve_months,Gross margin over the trailing 12 months (percentage),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7986,21,32,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_4,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,5,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras1,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,14,26,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras2,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,8,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras3,Base currency used for pricing in this dataset,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,5,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras4,Base currency used for price or market values,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,11,13,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras5,Base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,10,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras6,base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,16,30,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+pricing_currency_code_ras8,base currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,8,8,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+quarterly_free_cash_flow_value,Free Cash Flow — most recent quarter (units: millions),"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5027,4,4,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_11,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,7,7,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras1,Company’s reporting currency code,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,13,15,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras2,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,13,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras3,Reporting currency code,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,20,29,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras4,Company’s financial reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,8,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras5,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,7,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras6,Reporting currency of the company,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,6,12,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_currency_code_ras8,Reporting currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,9,10,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_to_pricing_currency_fx,Exchange rate from the company’s reporting currency to the dataset’s base (price) currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,13,17,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+reporting_to_pricing_currency_fx_rate,Exchange rate from reporting currency to the base (pricing) currency,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,11,16,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+usd_to_reporting_currency_fx_rate,The exchange rate used to convert US dollars to the reporting currency for financial statements.,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,17,20,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+usd_to_reporting_fx_rate_2,USD to reporting currency exchange rate,"{'id': 'fundamental17', 'name': 'Direct Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9731,21,31,1.4,[],fundamental17,Direct Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+accounts_receivable_current_assets,Accounts receivable included in current assets for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8387,143,184,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+annual_fiscal_month_number,The fiscal month number corresponding to the end of the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9799,40,48,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+annual_fiscal_year_number,The fiscal year associated with the annual reporting period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9799,57,70,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+annual_net_income_value,Net income reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9891,145,214,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+annual_reporting_currency_code,The currency code in which annual financial values are presented after conversion.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9799,15,16,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+currency_of_converted_financials,Currency in which the financial values have been converted for reporting.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,15,21,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd23_pamonper,maps each rep identifier to an instrument on a given day.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.3255,16,23,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd23_repnotickermap,maps each rep to a ticker on a given day.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,1.0,50,64,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+interim_fiscal_month,The fiscal month corresponding to the interim reporting period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,32,39,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+interim_fiscal_year,The fiscal year corresponding to the interim reporting period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,42,46,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+interim_period_sequence_number_2,Sequence number of the interim period within the fiscal year.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8745,43,55,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+long_term_collateral_debt,Long-term collateralized debt reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8785,63,75,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+long_term_contract_liabilities,Long-term contract liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9087,35,45,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+long_term_debt_total,Total long-term debt reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9233,64,96,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+long_term_loans_liabilities,Long-term loans and liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9887,52,60,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+long_term_trade_debt,Long-term trade debt reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9878,93,160,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_accumulated_reserves,Other accumulated reserves reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9049,45,62,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_appropriations_balance,Other appropriations balance reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.85,49,55,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_interest_liabilities,Other interest-bearing liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.7976,118,168,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_long_term_obligations,Other long-term obligations reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9858,61,97,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_net_equity_total,Total of other net equity items for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9766,112,198,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_off_balance_liabilities,Other off-balance sheet liabilities for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8219,40,45,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+other_operating_assets,Other operating assets reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.8365,27,36,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+quasi_equity_contracts,Quasi-equity contracts reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.988,29,35,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+quasi_equity_liabilities,Quasi-equity liabilities reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9887,35,54,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+retained_earnings_liabilities,Retained earnings classified as liabilities for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9254,19,23,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+securities_deferred_net_income,Deferred net income related to securities for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9894,36,50,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+share_capital_extraordinary,Extraordinary share capital reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9829,42,47,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+share_capital_ordinary,Ordinary share capital reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9058,31,35,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+share_capital_subscribed,Share capital subscribed reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9155,113,187,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+share_deposit_equity,Share deposit equity reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9766,20,21,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+share_dividend_equity,Share dividend equity reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9766,30,41,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+statement_of_cash_flows,Statement of cash flows for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9863,56,77,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+total_assets_annual_atot,Total assets reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9885,87,135,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+total_current_assets_4,Total current assets reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9086,37,42,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+valuation_market_adjustment_amount,Amount of market adjustment made to valuation.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.341,14,17,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+voting_amortized_investments,Voting amortized investments reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9011,72,92,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+voting_beneficial_equity_shares,Voting beneficial equity shares reported for the annual period.,"{'id': 'fundamental23', 'name': 'Fundamental Point in Time Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.9906,181,281,1.4,[],fundamental23,Fundamental Point in Time Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd4_chinascope_secmap,"Internal numeric security identifier used by Chinascope for cross-referencing securities within datasets, assigned per security per day","{'id': 'fundamental4', 'name': 'Hong Kong Fundamental Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3737,31,44,1.4,[],fundamental4,Hong Kong Fundamental Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_average_score,"Overall composite score aggregating all pillars, scaled as deciles 1–10 with higher being better","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9739,591,1934,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_earnings_score,"Earnings pillar score (1–10 decile, higher is better), reflecting earnings quality, revisions, and growth","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7632,360,981,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_fundamental_score,"Fundamental score on a 1–10 decile scale, higher indicates stronger fundamental quality","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9737,260,607,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_insider_trading_score,Insider trading score,"{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,1.0,0.0,39,90,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_price_momentum_score,"Price momentum score on a 1–10 decile scale, higher indicates stronger recent price trend","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9686,275,889,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_relative_valuation_score,"Relative valuation score on a 1–10 decile scale, higher indicates more attractive valuation versus peers in the region","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9456,269,759,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd86_risk_score,"Risk pillar score, integer decile 1–10 where higher indicates a more favorable risk profile","{'id': 'fundamental86', 'name': 'Stock Reports Plus'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.9755,271,639,1.4,[],fundamental86,Stock Reports Plus,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_41_11,Current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4158,130,423,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_41_12,Current asset accruals divided by time-series average total assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4156,90,403,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_41_21,Current asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4159,81,390,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_41_22,Current asset accruals divided by time-series average current assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4159,147,569,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_41_31,Current asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3538,80,252,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_42_11,Current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2833,16,17,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_42_12,Current asset accruals divided by time-series average total assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2832,20,27,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_42_21,Current asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2833,17,23,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_42_22,Current asset accruals divided by time-series average current assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2833,94,329,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_43_11,Current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3981,20,22,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_43_12,Current asset accruals divided by time-series average total assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.398,12,18,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_43_21,Current asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3982,6,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_43_22,Current asset accruals divided by time-series average current assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3982,10,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_43_31,Current asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3399,8,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_51_11,Non-current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,19,24,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_51_12,Non-current asset accruals divided by time-series average total assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,10,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_51_21,Non-current asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,15,18,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_51_22,Non-current asset accruals divided by time-series average current assets (method 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,69,208,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_51_31,Non-current asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3753,14,14,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_52_11,Non-current asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3072,96,234,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_52_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3072,8,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_52_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3072,8,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_52_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3072,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_52_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2645,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_53_11,Non-current Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_53_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,10,12,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_53_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_53_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.439,5,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_53_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3753,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_54_11,Non-current Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.407,9,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_54_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.407,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_54_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.407,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_54_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.407,7,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_54_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.363,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_55_11,Non-current Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3128,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_55_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3128,9,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_55_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2764,5,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_55_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2764,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_55_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.264,8,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_56_11,Non-current Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.312,14,15,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_56_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.312,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_56_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2767,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_56_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2767,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_56_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2638,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_57_11,Non-current Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.312,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_57_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.312,4,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_57_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2767,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_57_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2767,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_57_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2638,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_58_11,Non-current Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.427,8,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_58_12,Non-current Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4268,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_58_21,Non-current Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.427,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_58_22,Non-current Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.427,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_58_31,Non-current Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3663,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_61_11,Financial Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4597,12,15,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_61_12,Financial Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4596,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_61_21,Financial Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4139,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_61_22,Financial Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4138,6,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_61_31,Financial Asset Accruals / Sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.394,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_62_11,Financial Asset Accruals / Total Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4597,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_62_12,Financial Asset Accruals / Time Series Average Total Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4596,6,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_62_21,Financial Asset Accruals / Current Assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4139,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_62_22,Financial Asset Accruals / Time Series Average Current Assets 1,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4138,2,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_62_31,Financial asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.394,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_63_11,Financial asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4597,10,15,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_63_12,Financial asset accruals divided by time-series average total assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4596,10,11,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_63_21,Financial asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4139,4,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_63_22,Financial asset accruals divided by time-series average current assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4138,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_63_31,Financial asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.394,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_64_11,Financial asset accruals divided by total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4019,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_64_12,Financial asset accruals divided by time-series average total assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4018,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_64_21,Financial asset accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3657,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_64_22,Financial asset accruals divided by time-series average current assets (variant 1),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3656,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_64_31,Financial asset accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3496,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_asset_d1_asset_change,Change in asset,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5007,18,22,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best11_d1_asset_double_history,"Indicator (0/1) that the company’s assets have at some point doubled in its history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best11_d1_asset_half_history,"Indicator (0/1) that the company’s assets have at some point halved in its history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,5,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best11_d1_length,Number of available accruals ratio observations used in the historical/rolling window for these statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,6,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_accum_4q,Sum of the firm’s DescBest21 accruals ratio over the last four fiscal quarters,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5803,6,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_asset_double_history,"Indicator (1/0) that the company’s total assets have at some point doubled relative to a prior level in its observable history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,9,17,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_asset_half_history,"Indicator (1/0) that the company’s total assets have at some point halved relative to a prior level in its observable history, signaling asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,9,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_current_t,"Current t-stat/z-score of the accruals ratio, computed as (current accruals ratio − historical mean) / historical standard deviation","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3642,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_dts,Historical standard deviation of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6035,8,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_length,Number of available accrual ratio observations used in the historical statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,9,11,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_max,Historical maximum of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,10,12,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_mean,Historical mean of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,7,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_best21_d1_min,Historical minimum of the accruals ratio over the available history up to this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_major_21,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4822,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_major_22,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3163,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_major_31,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4751,2,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_major_32,"R-squared value of the cross-sectional ratio of an asset-related balance-sheet field, scaled by average total assets, computed across the entire universe (All)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3134,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_other_11,R-squared of the cross-sectional comprehensive accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_other_12,R-squared of the cross-sectional operating accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5535,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_other_13,R-squared of the cross-sectional financial accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5535,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_other_14,R-squared of the cross-sectional working capital accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4822,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_all_r2_other_15,R-squared of the cross-sectional long-term operating accruals ratio (scaled by average total assets) computed across the All-universe group in Asia,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.482,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjones_d1_asset_change,Change in asset,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.376,8,9,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesallmajor13_d1_asset_double_history,"0/1 indicator that the company’s total assets have at some point at least doubled in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,1,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesallmajor13_d1_asset_half_history,"0/1 indicator that the company’s total assets have at some point at least halved in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesallmajor13_d1_length,Number of historical accruals-ratio observations used in the descriptive statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesallother11_d1_asset_double_history,"Indicator (1/0) whether the company’s assets have doubled at any point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,4,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesallother11_d1_asset_half_history,"Indicator (1/0) whether the company’s assets have halved at any point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesallother11_d1_length,Number of accruals ratio observations available in the historical window used for the descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesindustrymajor13_d1_asset_double_history,"Indicator (1/0) that the company’s assets have doubled at some point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesindustrymajor13_d1_asset_half_history,"Indicator (1/0) that the company’s assets have halved at some point in its history, used as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesindustrymajor13_d1_length,Number of available accrual ratio observations in the historical window used for the descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesindustryother11_d1_asset_double_history,"Indicator 1 or 0 showing whether the company’s total assets have doubled at any point in its history, as a volatility flag","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesindustryother11_d1_asset_half_history,"Indicator 1 or 0 showing whether the company’s total assets have halved at any point in its history, as a volatility flag","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_csjonesindustryother11_d1_length,Count of accrual ratio observations available in the historical window used for the descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5594,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_fixed1313_d1_asset_double_history,"0/1 indicator whether the company’s assets have ever doubled in its history, indicating asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_fixed1313_d1_asset_half_history,"0/1 indicator whether the company’s assets have ever halved in its history, indicating asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_fixed1313_d1_length,Number of available accruals ratio observations in the historical window (sample size),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,50,129,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_fixed1513_d1_asset_double_history,"Indicator (0/1) whether the company’s assets have doubled at any point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_fixed1513_d1_asset_half_history,"Indicator (0/1) whether the company’s assets have halved at any point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_fixed1513_d1_length,Number of available accruals-ratio observations used at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,3,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_major_21,"Percentage share of discretionary asset-related accruals (MAJOR group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_major_22,"Percentage share of discretionary asset-related accruals (MAJOR group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4221,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_major_31,"Percentage share of discretionary asset-related accruals (MAJOR group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4334,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_major_32,"Percentage of discretionary accruals for asset-related (balance sheet) items, scaled by average total assets, as a share of total accruals within the MAJOR overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4042,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_other_11,"Percentage of discretionary accruals for comprehensive accruals, scaled by average total assets, as a share of total accruals within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,6,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_other_12,"Percentage of discretionary accruals for operating accruals, scaled by average total assets, as a share of total accruals within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_other_13,"Percentage of discretionary accruals for financial accruals, scaled by average total assets, as a share of total accruals within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.491,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_other_14,"Percentage share of discretionary working capital accruals (OTHER group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_pct_other_15,"Percentage share of discretionary long-term operating accruals (OTHER group), scaled by average total assets, out of total accruals","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_major_21,"R-squared of the Jones-model time series accrual ratio for the MAJOR group based on asset-related balance sheet items, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_major_22,"R-squared of the time-series accrual ratio for asset-related (balance sheet) items, scaled by average total assets, within the MAJOR overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4221,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_major_31,"R-squared of the Jones-model time series accrual ratio for the MAJOR group based on asset-related balance sheet items, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4333,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_major_32,"R-squared of the Jones-model time series accrual ratio for the MAJOR group based on asset-related balance sheet items, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4042,0,0,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_other_11,"R-squared of the Jones-model time series accrual ratio for the OTHER group based on comprehensive accruals, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_other_12,"R-squared of the Jones-model time series accrual ratio for the OTHER group based on operating accruals, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_other_13,"R-squared of the Jones-model time series accrual ratio for the OTHER group based on financial accruals, scaled by average total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_other_14,"R-squared of the time-series accrual ratio for working capital accruals, scaled by average total assets, within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_accrual_r2_other_15,"R-squared of the time-series accrual ratio for long-term operating accruals, scaled by average total assets, within the OTHER overlay","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4424,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jones_d1_asset_change,Change in asset,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3244,2,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor13_d1_asset_double_history,"Indicator (1/0) that the company’s assets have doubled at some point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor13_d1_asset_half_history,"Indicator (1/0) that the company’s assets have halved at some point in its history, as a volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor13_d1_length,Number of historical periods available for the accrual ratio used to compute these descriptive statistics,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor31_d1_asset_double_history,"Indicator (1/0) whether the company’s total assets have doubled at any point in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor31_d1_asset_half_history,"Indicator (1/0) whether the company’s total assets have halved at any point in its available history, used as an asset-volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor31_d1_length,Number of historical accruals-ratio observations available and used to compute the descriptive statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor32_d1_asset_double_history,"0 or 1 indicator whether the company’s assets have ever doubled in its history, as a proxy for asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor32_d1_asset_half_history,"0 or 1 indicator whether the company’s assets have ever halved in its history, as a proxy for asset volatility","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesmajor32_d1_length,Count of available accruals ratio observations used in the historical window/statistics at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_accum_4q,Accumulated (4-quarter) value of the accrual ratio over the last four fiscal quarters,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5803,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_asset_double_history,Indicator (1/0) whether the company’s assets have ever doubled in its history (asset volatility diagnostic),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,4,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_asset_half_history,Indicator (1/0) whether the company’s assets have ever halved in its history (asset volatility diagnostic),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,4,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_current_t,Current t/z-score of the accrual ratio: (current value − historical mean) / historical standard deviation,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3642,3,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_dts,Historical standard deviation of the Jones “Other” accrual ratio (scaled by total assets),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6035,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_length,Number of historical observations used to compute the descriptive statistics for the accrual ratio at this date,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7716,41,150,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_max,Historical maximum of the Jones “Other” accrual ratio (scaled by total assets),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,7,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_mean,Historical mean of the Jones “Other” accrual ratio (scaled by total assets) over the available history,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother11_d1_min,Historical minimum of the Jones “Other” accrual ratio (scaled by total assets),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6266,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother14_d1_asset_double_history,Indicator (0/1) that the company’s assets have at some point doubled over its available history; used as an asset volatility diagnostic,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother14_d1_asset_half_history,Indicator (0/1) that the company’s assets have at some point halved over its available history; used as an asset volatility diagnostic,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother14_d1_length,Number of historical observations available for the accruals ratio,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother15_d1_asset_double_history,"Indicator (1/0) that the company’s assets have doubled at some point in its history, as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,2,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother15_d1_asset_half_history,"Indicator (1/0) that the company’s assets have halved at some point in its history, as an asset volatility diagnostic","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_jonesother15_d1_length,Number of available accrual ratio observations used in the historical calculation,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4948,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_21_11,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3938,7,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_21_12,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by average total assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3937,9,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_21_21,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_21_22,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by average current assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_21_31,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt), scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3356,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_31_11,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3294,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_31_12,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by average total assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3293,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_31_21,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3295,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_31_22,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by average current assets computed as (current + 4 quarters ago)/2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3295,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_31_31,"(change in current assets − change in cash and short-term investments) − (change in current liabilities − change in short-term debt) − depreciation and amortization expense, scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3292,1,1,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_major_d1_asset_change,Period-over-period change in total assets (Major overlay),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5007,15,18,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_11_11,"Change in common shareholders’ equity minus change in cash, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4636,7,10,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_11_12,"Change in common shareholders’ equity minus change in cash, divided by two-quarter average total assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4635,8,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_11_21,"Change in common shareholders’ equity minus change in cash, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4158,4,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_11_22,"Change in common shareholders’ equity minus change in cash, divided by two-quarter average current assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4157,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_11_31,"Change in common shareholders’ equity minus change in cash, divided by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3963,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_12_11,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4532,4,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_12_12,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by two-point average total assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4532,11,13,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_12_21,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4067,12,14,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_12_22,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by two-point average current assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4066,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_12_31,"Change in total assets excluding cash and long-term investments and advances, minus change in total liabilities excluding debt, scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3882,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_13_11,"Comprehensive accruals minus operating accruals, divided by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.453,11,12,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_13_12,"Comprehensive accruals minus operating accruals, divided by two-quarter average total assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.453,6,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_13_21,"Comprehensive accruals minus operating accruals, divided by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4065,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_13_22,"Comprehensive accruals minus operating accruals, divided by two-quarter average current assets (current and 4 quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4064,7,8,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_13_31,"Comprehensive accruals minus operating accruals, divided by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3882,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_14_11,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3938,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_14_12,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by two-point average total assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3937,4,4,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_14_21,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by current assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,3,3,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_14_22,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by two-point average current assets (current and four quarters earlier)","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3939,2,2,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_14_31,"Change in current assets excluding cash and short-term investments minus change in current liabilities excluding short-term debt, scaled by sales","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3356,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_15_11,"Operating accruals minus working capital accruals, divided by total assets","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,7,16,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_15_12,"Operating accruals – working capital accruals divided by average total assets, where average total assets = (total assets at the current quarter + total assets 4 quarters earlier) / 2","{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_15_21,Working capital accruals divided by current assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,5,7,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_15_22,Working capital accruals divided by average current assets (average of current and the same quarter four quarters earlier),"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3936,5,5,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_15_31,Working capital accruals divided by sales,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3355,5,6,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd89_other_d1_asset_change,Period-over-period change in total assets,"{'id': 'fundamental89', 'name': 'Accrual based Earnings Model'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5007,76,197,1.4,[],fundamental89,Accrual based Earnings Model,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd90_game_accruals_vol,"The volatility of accounting accruals, measuring variability in accruals over time; higher values indicate greater manipulation or lower earnings quality","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.573,48,70,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_an_cov_chng,"Period-over-period change in the number of financial analysts covering the company, positive values indicate increasing analyst attention","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5611,91,138,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_asset_turnover_chg,"The change in asset turnover ratio (typically sales/assets), reflecting improvement or deterioration in capital utilization over time","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8257,77,127,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_capex_dep,"Ratio of capital expenditures to depreciation, with higher values indicating more aggressive reinvestment relative to asset aging","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7553,44,57,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_capex_sale,"Ratio of capital expenditures to sales, indicating investment intensity compared to revenue generation","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8239,92,148,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_cash_flow_coverage,A ratio measuring how well a company's operating cash flow covers its obligations (likely total debt or interest); higher values signal stronger ability to service liabilities,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.701,65,95,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_contingent_liab,"The ratio of contingent liabilities (potential future obligations) to equity, indicating off-balance-sheet or unrecognized risk; higher values mean higher risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.486,32,37,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_deferred_sal_chg,"The period-over-period change in deferred revenue (sales received but not yet recognized), which may reflect aggressive revenue recognition practices; positive changes may warrant scrutiny","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5082,41,70,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_deferred_tax_asset_chg,"The period-over-period change in the amount of deferred tax assets on the balance sheet, reflecting adjustments or aggressive accounting in tax accruals or recognition timing","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6129,47,78,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_deferred_tax_liab_chg,"The period-over-period change in deferred tax liabilities, indicating shifts in future tax obligations possibly due to accounting policies or estimates","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6283,45,61,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_depreciation_chg,"The change in the depreciation rate (either method or level), which can indicate changes in accounting estimate or potential earnings management","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7578,43,79,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_earning_smooth,"A measure of how stable or ""smooth"" reported earnings are over time, potentially capturing both persistent profitability or possible earnings management/smoothing practices","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7041,61,99,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_eff_interest_rate,"The effective interest rate experienced by the company, representing the cost of debt factoring in all forms of financing; higher rates signal more financial leverage risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5542,18,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_eps_sur_vol,"Volatility of analyst EPS (earnings per share) forecast errors, reflecting the unpredictability or low quality of reported earnings versus market expectations","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6827,26,42,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_eps_vol,"Volatility (standard deviation) of earnings per share over a period, with higher values indicating unstable profitability","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7875,40,43,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_equity_dilution,"An indicator of new equity issuance or dilution, such as through new share issuance or conversion of convertibles, reflecting risk of ownership dilution to existing shareholders","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8298,74,129,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_esop_grant,ESOP grants [ ] - [Quality.Capital structure],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.3124,16,19,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_finished_goods,"Ratio of finished goods to total inventory, indicating the proportion of inventory in final form; higher values may signal slow sales, overproduction, or potential risk in working capital management","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5529,19,21,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_gma_chg,"Change in gross margin (as a percentage or ratio) from one period to the next, capturing improvement or deterioration in efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7719,41,45,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_gross_margin_vol,"Volatility in gross margin over time; high volatility signals instability or unpredictability in profitability, indicating potential business risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.789,50,59,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_inc_cash_tax,"Ratio of tax expense per income statement to actual cash taxes paid, designed to highlight aggressive tax recognition or earnings quality red flags","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8226,50,75,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_interest_investment_inc,"The amount of income earned from interest and investments, as opposed to operating earnings, which might distort quality of earnings if high relative to core business profit","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.672,24,27,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_inventory_chg,"Change in company inventory levels over a specific period, typically quarter-over-quarter or year-over-year, reflecting working capital changes and operational efficiency; higher changes may indicate risk of inventory buildup or fluctuations in demand","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6559,40,46,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_inventory_days,"Number of days inventory remains on hand before being sold, calculated as inventory divided by average daily sales; a higher value suggests slower inventory turnover and potential working capital inefficiency or sales weakness","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6559,17,18,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_labor_efficiency_chg,"Period-to-period change in labor efficiency (e.g., revenue or profit per employee), with increases reflecting greater efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5813,22,29,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_minority_interest,The ratio of minority interest (equity attributable to non-controlling shareholders in subsidiaries) to total equity; higher values may reflect more complex ownership structures and potential earnings quality concerns,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5186,21,23,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_noa,"Net Operating Assets; represents the company's operating assets minus operating liabilities, indicating capital employed in operations","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.792,21,30,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_non_oper_inc,"Non-operating income as a share of EBITDA, highlighting the proportion of earnings derived from sources outside the core business; higher ratios may imply lower earnings quality or potential one-off items","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.685,12,12,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_non_production_asset,"Amount or ratio of company assets not directly tied to production or core operations (such as investments, property held for sale); higher values may indicate inefficient capital utilization or non-core area exposure","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6593,29,34,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_optimism_gma,Analyst Optimism Gross Margin [ Descending] - [Quality.Profitability],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5365,21,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_optimism_sale,Analyst Optimism Sales [ Descending] - [Quality.Profitability],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6952,25,32,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_other_asset_gr,Other Asset Growth [ Descending] - [Quality.Capital utilization],"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8289,27,29,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_payble_days,"Average number of days the company takes to pay its suppliers (accounts payable days); a higher value means slower payments, which could indicate cash flow management or payment risk to suppliers","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.771,22,25,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_pension_debt_equi_pbo,Value of the company's net pension benefit obligation (PBO) expressed as debt equivalent; a higher value indicates greater pension-related financial risk relative to debt,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8267,22,31,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_pension_discount_rate,The discount rate used to calculate the present value of pension obligations; higher values may indicate more aggressive accounting practices (lowering reported liabilities),"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8031,20,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_pension_ev,Ratio of pension and other post-employment benefit obligations to total enterprise value; higher values mean greater pension-related liability risks relative to the company's market worth,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6727,22,33,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_pension_expense,"Pension-related expense scaled by EBITDA, where higher values indicate greater pension-related liability risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8309,32,45,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_pension_interest,"Interest cost component of pension expense, representing the increase in projected pension liability due to the passage of time","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7941,23,25,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_pension_pbo,"Ratio of projected pension benefit obligations (pension liabilities) to total enterprise value, indicating pension-related financial risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8267,114,194,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_prepaid_expense,"Ratio of prepaid expenses to sales, indicating the extent of accounting items paid in advance relative to revenue, which may signal aggressive accounting practices","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5922,14,19,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_receivable_chg,"Change in receivables (typically quarter-over-quarter or year-over-year), showing how much accounts receivable increased or decreased, reflecting shifts in working capital","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8107,30,39,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_receivable_days,"Number of days, on average, it takes a company to collect receivables from customers; a measure of working capital efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8107,18,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_relative_rtn_vol,"Volatility of a security’s returns relative to a benchmark or peers, representing relative risk or instability of stock returns","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,25,29,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_serial_special,"Total of special (non-recurring/one-off) accounting items as a proportion of sales, highlighting the prevalence of such adjustments and potential earnings manipulation","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8279,24,31,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_sg_a_cost_ratio,"Selling, General, & Administrative (SG&A) expenses as a ratio to a base (likely sales or revenue), measuring company efficiency in overhead spending","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8256,22,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_sg_a_inefficiency,"Level of inefficiency in SG&A (Selling, General & Administrative) expenses; higher values suggest less efficient management of overhead costs","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8287,18,19,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_sg_a_vol,"Volatility or variability in SG&A expenses over time, with higher values indicating instability or unpredictability in overhead costs, which could be a risk factor for efficiency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7988,16,16,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_short_term_debt,"Ratio of short-term (due within one year) debt to a base (likely total assets or capitalization), indicating near-term financial leverage or refinancing risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6488,22,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_tax_rate,"Effective tax rate, with lower rates possibly indicating aggressive accounting or tax minimization strategies","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8242,30,39,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_game_total_debt_chg,"Change in total debt balance (period-over-period), measuring increases or decreases in company leverage","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6438,22,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_cashflow_var,"Variability (volatility) of operating cashflows; higher values indicate more unpredictable or volatile cashflow generation, which means higher risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.819,26,28,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_chs_default,"Standardized probability of bankruptcy/default (z-score) based on the Campbell, Hilscher, Szilagyi model; higher values = greater default risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7663,62,76,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_cscore,"Montier’s C-score, an integer-based composite designed to flag accounting manipulation or fraud risk (higher is more risk)","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,22,26,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_earn_var,"Earnings variability, typically representing the standard deviation or volatility of a company's earnings over a period; higher values indicate less stable or more unpredictable earnings, signaling higher risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.814,31,35,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_earnigs_timeliness,"Z-score measuring the timeliness of a company's earnings releases, where higher values typically indicate quicker (more timely) earnings reporting, which may reflect better governance or information transparency","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.5639,27,36,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_grossprofit_asset,"Gross profit divided by total assets, a quality/profitability metric where higher values indicate better profitability","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.7751,17,18,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_icfroe_1y,Incremental Cash Flow Return on Equity over the past 1 year; higher values indicate stronger cash generation relative to equity for the recent year,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6985,10,11,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_icfroe_3y,Incremental Cash Flow Return on Equity over the past 3 years; higher values indicate stronger multi-year cash flow returns relative to equity,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.676,21,24,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_icfroe_5y,Incremental Cash Flow Return on Equity over the past 5 years; higher values indicate sustained cash flow return performance relative to equity over a longer period,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.6493,10,10,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_mohanram,Mohanram “G-score” assessing risk of earnings manipulation in growth stocks (higher indicates greater fraud or earnings risk),"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,43,54,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_negative_earnings,Frequency or indicator of negative earnings occurrences for the entity; higher numbers suggest more frequent negative earnings results,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8322,31,34,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_ohlson,"Ohlson O-score, a bankruptcy prediction model score; higher scores indicate higher risk of bankruptcy for a company","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8285,46,66,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_pscore,"Piotroski F-score (0–9), a composite accounting quality score based on fundamental signals; higher values indicate better accounting quality and financial health","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8325,61,81,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd90_qes_gamef_score,SCORE,"{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8142,49,64,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+nonliquid_asset_ratio,"Ratio of illiquid assets (such as plant, equipment, intangibles, or other hard-to-sell assets) to total assets, indicating potential capital utilization or liquidity risk","{'id': 'fundamental90', 'name': 'Governance, Accounting, Management, and Equality'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-models', 'name': 'Fundamental Models'}",IND,1,TOP500,MATRIX,0.9507,0.8318,32,49,1.4,[],fundamental90,"Governance, Accounting, Management, and Equality",fundamental,Fundamental,fundamental-fundamental-models,Fundamental Models
+fnd93_accruals_d1_length,Count of available accruals ratio observations within the trailing twelve-month (LTM) window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.76,72,110,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_accrualsratio_d1_asset_change,"Asset change ratio = latest total assets divided by asset_ltm (e.g., Asset_2018Q3 / Asset_2017Q3)","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4956,39,49,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_11,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5727,22,31,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_12,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5722,22,27,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_21,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5018,12,17,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_22,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5011,21,30,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_31,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5723,23,27,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_41,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5195,16,17,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_expense_51,"There are 7 ratio fields with naming pattern ""DEFERRED_TAX_EXPENSE_XX"", where XX denotes the index of denominator. We use (Net deferred tax liability - Net deferred tax liability) as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5224,22,32,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_11,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.455,18,22,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_12,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.455,10,10,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_13,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3536,17,20,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_21,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4032,9,13,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_22,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4031,9,11,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_23,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.2886,9,9,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_31,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.388,5,6,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_41,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3569,6,7,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_deferred_tax_liability_51,"There are 9 ratio fields with naming pattern ""DEFERRED_TAX_LIABILITY_XX"", where XX denotes the index of denominator. We use deferred tax expense as numerator and different groups of denominators. E.g. ""DEFERRED_TAX_LIABILITY_XX""  indicates use of the 2nd candidate of denominators group 1.","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3607,10,10,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_asset_change,"YoY asset change ratio: latest total assets divided by total assets 12 months earlier (e.g., Asset_2018Q3 / Asset_2017Q3)","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5841,16,18,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_current_t,"Z-score (t-statistic) of the current deferred tax expense ratio relative to the LTM window, computed as (current ratio − mean) / std","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4983,22,22,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_dts,Standard deviation of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6756,16,19,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_length,Number of available deferred tax expense ratio observations over the LTM (last twelve months) window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7381,25,33,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_max,Maximum value of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7181,22,25,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_mean,Mean of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7181,23,31,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_expense_d1_min,Minimum value of the deferred tax expense ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7181,16,22,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_asset_change,"Ratio of latest total assets to asset_ltm (e.g., Assets_current quarter / Assets_same quarter prior year)","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.4954,22,25,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_current_t,"Z-score of the current liability ratio versus the LTM window, computed as (current ratio − mean) / std","{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.3428,7,8,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_dts,Standard deviation of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.5769,9,10,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_length,Number of deferred tax liability ratio observations available in the trailing 12-month (LTM) window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.7594,26,30,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_max,Maximum value of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6177,12,15,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_mean,Mean of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6175,8,8,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data
+fnd93_liability_d1_min,Minimum value of the deferred tax liability ratio over the LTM window,"{'id': 'fundamental93', 'name': 'Earnings Tax Data'}","{'id': 'fundamental', 'name': 'Fundamental'}","{'id': 'fundamental-fundamental-data', 'name': 'Fundamental Data'}",IND,1,TOP500,MATRIX,0.9507,0.6175,10,12,1.4,[],fundamental93,Earnings Tax Data,fundamental,Fundamental,fundamental-fundamental-data,Fundamental Data

simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv

@@ -0,0 +1,3 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+imb5_mktcap,Market capitalization of security in regional currency units,"{'id': 'imbalance5', 'name': 'Oil Price Resilience Scores'}","{'id': 'imbalance', 'name': 'Imbalance'}","{'id': 'imbalance-imbalance-models', 'name': 'Imbalance Models'}",IND,1,TOP500,MATRIX,0.9507,0.7876,869,2509,1.0,[],imbalance5,Oil Price Resilience Scores,imbalance,Imbalance,imbalance-imbalance-models,Imbalance Models
+imb5_score,SHIELD-OIL composite score (0-1) indicating resilience/advantage in oil shock regimes,"{'id': 'imbalance5', 'name': 'Oil Price Resilience Scores'}","{'id': 'imbalance', 'name': 'Imbalance'}","{'id': 'imbalance-imbalance-models', 'name': 'Imbalance Models'}",IND,1,TOP500,MATRIX,0.9507,0.7876,1546,9502,1.0,[],imbalance5,Oil Price Resilience Scores,imbalance,Imbalance,imbalance-imbalance-models,Imbalance Models

simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv

@@ -0,0 +1,12 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+aggregate_equity_value_all_owners,"Aggregate dollar value of shares of the security held by all owners, with duplication between child and parent owners removed","{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,438,794,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+aggregate_equity_value_institutions,Aggregate dollar value of shares of the security currently held by all institutional investors at the reporting date,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,343,594,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+aggregate_share_count_all_owners,"Aggregate number of shares of the security held by all owners, with duplication between child and parent owners removed","{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,319,527,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+aggregate_share_count_institutions,Total number of shares of the security currently held by all institutional investors at the reporting date,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,276,501,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+count_institutional_buyers_security,Number of institutional investors who purchased shares of the security during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,517,1210,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+count_institutional_holders_security,Number of institutional investors currently holding shares (with holdings greater than zero) for the security at the reporting date,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,340,580,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+count_institutional_sellers_security,Number of institutional investors who sold shares of the security during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,323,576,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+market_value_institutional_shares_acquired,Aggregate dollar value of shares of the security purchased by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,592,1334,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+market_value_institutional_shares_disposed,Aggregate dollar value of shares of the security sold by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.9915,255,414,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+quantity_institutional_shares_acquired,Total number of shares of the security purchased by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,445,873,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models
+quantity_institutional_shares_disposed,Total number of shares of the security sold by institutional investors during the reporting period,"{'id': 'institutions6', 'name': 'Institutions and Beneficial Stake Ownership'}","{'id': 'institutions', 'name': 'Institutions'}","{'id': 'institutions-ownership-models', 'name': 'Ownership Models'}",IND,1,TOP500,MATRIX,0.9507,0.992,294,484,1.0,[],institutions6,Institutions and Beneficial Stake Ownership,institutions,Institutions,institutions-ownership-models,Ownership Models

simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv

@@ -0,0 +1,2 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+mcr63_membership,Indicates the membership status of stocks in the WisdomTree WTIDJH Index,"{'id': 'macro63', 'name': 'Index Reconstitution Data'}","{'id': 'macro', 'name': 'Macro'}","{'id': 'macro-macroeconomic-activities', 'name': 'Macroeconomic Activities'}",IND,1,TOP500,MATRIX,0.9507,0.9968,325,1177,1.0,[],macro63,Index Reconstitution Data,macro,Macro,macro-macroeconomic-activities,Macroeconomic Activities

simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv

@@ -0,0 +1,73 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+estimated_next_shareholder_meeting_date,"Tentative or estimated date (as an integer date, e.g., YYYYMMDD) for the next shareholder annual meeting, provided with next-trading-day (D1) availability for the USA region","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.6319,125,161,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+event_frequency_label,Integer count of StreetEvents mentions or hits for the company during the represented period or date,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2625,13,16,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+next_earnings_call_date,"Next scheduled or confirmed earnings conference call date for the US region module, encoded as an integer date (typically YYYYMMDD); data available with D1 (next trading day) delay","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.4176,32,43,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+next_earnings_estimate_date,"Estimated next earnings release date when the actual date is not yet confirmed (D1 module; next-day availability), stored as an integer date (e.g., YYYYMMDD)","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.5964,75,99,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+next_ex_dividend_event_date,"The next confirmed ex-dividend date for the company in the USA module, delivered with next-day (D1) timeliness and encoded as an integer date","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3362,65,124,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+next_shareholder_meeting_date,"Confirmed next scheduled date (as an integer date, e.g., YYYYMMDD) for the upcoming shareholder annual meeting as of today, provided with next-trading-day (D1) availability for the USA region","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2696,24,26,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+nws21_tonexiguome1_tone_sc,(number of positive words - number of negative words) divided by (number of positive words + number of negative words),"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,4,4,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+nws21_xiguo_me1_event_freq,number of events,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,5,5,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+nws21_xiguo_me1_neg_sc,number of negative words divided by number of all words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,4,6,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+nws21_xiguo_me1_pos_sc,number of positive words divided by number of all words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,2,2,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+nws21_xiguo_me1_qerf_gen,number of negative words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,3,3,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+nws21_xiguo_me1_qerf_sop,number of positive words,"{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2938,3,3,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+previous_corporate_presentation_date,"Most recent past corporate presentation date before today (for a China-listed company), available on a next-trading-day delay, stored as an integer date (e.g., YYYYMMDD)","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.2785,13,13,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+previous_earnings_call_date,"Most recent past earnings conference call date prior to today for the US region module, encoded as an integer date (typically YYYYMMDD); data available with D1 delay","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.5211,73,123,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+previous_ex_dividend_event_date,"The most recent past ex-dividend date before today for the company in the USA module, encoded as an integer date","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.547,87,110,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+previous_shareholder_meeting_date,"Most recent past date (as an integer date, e.g., YYYYMMDD) when the shareholder annual meeting last occurred before today, provided with next-trading-day (D1) availability for the USA region","{'id': 'news21', 'name': 'Macro Economic Event Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.4653,26,30,1.5,[],news21,Macro Economic Event Data,news,News,news-news,News
+mws52_chars_in_presentation,Number of characters in the presentation section,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,18,22,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_chars_in_qa,Number of characters in the Q&A section,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,17,33,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_conference_call_participants,"Number of non-corporate participants (analysts, third parties) in the conference call","{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,12,38,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_corporate_participants,Number of representatives from the company (issuer) participating in the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,14,22,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_eventtype,"Type/category of the conference call event (e.g., earnings call, guidance update, etc.)","{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,12,30,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_expirationtime,Time when the event or record is considered expired or no longer current,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,14,30,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_lastuptime,Time when the record was last updated,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,9,17,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_paragraphs_in_presentation,Number of paragraphs in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,13,31,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_pparagraphs_in_qa,Number of paragraphs in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,14,20,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_questions_in_presentation,Number of questions asked during the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,12,15,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_questions_in_qa,Number of questions asked in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,7,10,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_sentences_in_presentation,Number of sentences in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,10,19,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_sentences_in_qa,Number of sentences in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,13,25,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_starttime,Time the conference call event started,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,11,16,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_talks_in_presentation,Number of talks (distinct speaker turns or interventions) in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,18,36,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_talks_in_qa,Number of individual talks in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,10,13,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_words_in_presentation,Number of words in the presentation section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,10,19,1.5,[],news52,Conference call data,news,News,news-news,News
+mws52_words_in_qa,Number of words in the Q&A section of the conference call,"{'id': 'news52', 'name': 'Conference call data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.291,18,39,1.5,[],news52,Conference call data,news,News,news-news,News
+headline_mention_count,Total number of news items for the security/date in the dataset,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,54,80,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_mention_count_2,Number of news headlines for the given security and date,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,94,200,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_negative_mention_count,Count of negative words in headlines across all news for a security/date,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,26,43,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_negative_mention_count_2,Total count of negative words appearing in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,69,135,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_negative_tone_score,"Average proportion of negative words in news headlines, calculated as negative words divided by total words, across all news for the security/date","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,30,46,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_negative_tone_score_2,The average ratio of negative words to all words in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,55,84,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_overall_tone_score,average tone score calculated as (positive word count - negative word count) divided by (positive word count + negative word count) for headlines of news stories,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,31,43,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_overall_tone_score_2,"average normalized headline tone score across all news, calculated as (positive words - negative words) divided by (positive words + negative words) per headline","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,57,98,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_positive_mention_count,Count of positive words in headlines across all news for a security/date,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,14,21,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_positive_mention_count_2,Total count of positive words appearing in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,35,57,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_positive_tone_score,"Average proportion of positive words in news headlines, calculated as positive words divided by total words, across all news for the security/date","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3527,18,32,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+headline_positive_tone_score_2,The average ratio of positive words to all words in all news headlines for the day,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9759,34,45,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+news_mention_frequency_1,"Count of news stories per security and date in the EUR region for the real-time (D0) module, excluding stories containing the keyword ""insider""","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9755,45,96,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+news_mention_frequency_d0,The number of news events or articles detected for each security and date in the Asia region on a real-time (intraday) basis,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9761,44,74,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+news_mention_frequency_d0_twn,"Count of news events per Taiwan-listed security on a given date, captured at real-time/intraday (D0) timing within the Asia module","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9761,27,45,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+news_mention_frequency_d0_twn_alt,Total number of news stories per security and date for Taiwan-listed companies in the Asia news frequency module at real-time/intraday (D0) delay,"{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9757,46,57,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+news_mention_frequency_simple,"Count of news articles related to a security on a given date in China, excluding those containing ""insider"" keywords","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.9755,43,79,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+news_sales_mention_count,"Daily count of news articles for European securities that mention sales-related keywords (e.g., sale, sales) or earnings, aggregated per security per date","{'id': 'news7', 'name': 'Real Time News Feed Data'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news', 'name': 'News'}",IND,1,TOP500,MATRIX,0.9507,0.3677,23,40,1.5,[],news7,Real Time News Feed Data,news,News,news-news,News
+max_primary_sentiment_score_transfer,Daily maximum of variant-1 sentiment scores for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,51,66,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+max_secondary_sentiment_score_transfer,"Daily maximum of variant 2 sentiment across PR events; scale [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,27,37,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+max_sentiment_score_transfer,Daily maximum of base sentiment scores across PR events mapped to the stock,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6973,20,35,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+mean_primary_sentiment_score_transfer,Daily mean of sentiment1 scores across PR events (may be weighted by article importance),"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,13,15,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+mean_secondary_sentiment_score_transfer,Daily mean of sentiment2 scores across PR events (may be weighted by article importance),"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,9,12,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+mean_sentiment_score_transfer,Daily mean of base sentiment scores across PR events (often weighted by article importance),"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,10,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+median_primary_sentiment_score_transfer,Daily median of sentiment1 scores across PR events,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,14,17,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+median_secondary_sentiment_score_transfer,"Median of the TRNA-style sentiment2 scores from all RavenPack press-release events linked to the stock on that day (USA region, D1 delay)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,10,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+median_sentiment_score_transfer,Daily median of event-level sentiment scores for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,10,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+min_primary_sentiment_score_transfer,"Daily minimum of sentiment1 scores across PR events mapped to the stock; range [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,12,18,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+min_secondary_sentiment_score_transfer,"Daily minimum of variant 2 sentiment across PR events; scale [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,18,21,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+min_sentiment_score_transfer,"Daily minimum of main sentiment across PR events; scale [-1, 1]","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,9,10,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+news_item_count_transfer,"Total number of PR headlines mapped to the stock on that day (USA, D1)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,27,28,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+normalized_news_item_count_transfer_2,"Normalized daily news article count (unitless), scaled by 90-day rolling mean for cross-sectional comparability","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,17,20,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+skew_primary_sentiment_score_transfer,The skewness of the primary transferred sentiment score distribution for the period.,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,11,14,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+skew_secondary_sentiment_score_transfer,"Skewness of the distribution of TRNA-style sentiment2 scores across all RavenPack press-release events linked to the stock on that day (USA region, D1 delay)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,11,18,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+skew_sentiment_score_transfer,Skewness of the distribution of event-level sentiment scores for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,18,49,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+sum_primary_sentiment_score_transfer,The sum of all primary transferred sentiment scores for the period.,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,24,28,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+sum_secondary_sentiment_score_transfer,Daily sum of variant-2 sentiment scores across all PR events for the stock-day,"{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6974,11,11,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment
+sum_sentiment_score_transfer,"Sum of the main TRNA-style sentiment scores across all RavenPack press-release events linked to the stock on that day (USA region, D1 delay)","{'id': 'news84', 'name': 'Headline Sentiment Analysis using DNN'}","{'id': 'news', 'name': 'News'}","{'id': 'news-news-sentiment', 'name': 'News Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.6973,15,30,1.5,[],news84,Headline Sentiment Analysis using DNN,news,News,news-news-sentiment,News Sentiment

simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv

@@ -0,0 +1,31 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+oth571_views14d,Total Wikipedia company page views in the USA module over the trailing 14-day window ending 14 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4536,65,97,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1y,Total Wikipedia company page views in the USA module over the trailing 365-day window (366 in leap years) ending 364 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,14,15,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1yafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,17,17,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1ydesktop,Total Wikipedia company page views in the USA module via desktop devices on the day 364 days ago (desktop web; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,15,30,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1yevening,Wikipedia company page views in the USA module from 18:00 to 23:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,6,6,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1ymobile,Total Wikipedia company page views in the USA module via mobile devices on the day 364 days ago (mobile web and apps; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,33,51,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1ymorning,Wikipedia company page views in the USA module from 06:00 to 11:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,19,24,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views1ynight,Wikipedia company page views in the USA module from 00:00 to 05:59 on the day 364 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4021,5,6,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28d,Total Wikipedia company page views in the USA module over the trailing 28-day window ending 28 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,17,19,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28dafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,16,22,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28ddesktop,Total Wikipedia company page views in the USA module via desktop devices on the day 28 days ago (desktop web; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,23,46,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28devening,Wikipedia company page views in the USA module from 18:00 to 23:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,18,24,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28dmobile,Total Wikipedia company page views in the USA module via mobile devices on the day 28 days ago (mobile web and apps; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,12,12,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28dmorning,Wikipedia company page views in the USA module from 06:00 to 11:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,15,19,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views28dnight,Wikipedia company page views in the USA module from 00:00 to 05:59 on the day 28 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4512,10,10,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7d,Total Wikipedia page views over the past 7 natural days.,"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,18,20,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7dafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,13,16,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7ddesktop,Wikipedia page views from desktop devices over the past 7 natural days.,"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,11,15,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7devening,Wikipedia company page views in the USA module from 18:00 to 23:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,48,66,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7dmobile,Wikipedia page views from mobile devices over the past 7 natural days.,"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,23,32,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7dmorning,Wikipedia company page views in the USA module from 06:00 to 11:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,9,9,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views7dnight,Wikipedia company page views in the USA module from 00:00 to 05:59 on the day 7 days ago (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4549,8,8,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_views84d,Total Wikipedia company page views in the USA module over the trailing 84-day window ending 84 days ago (inclusive; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4426,12,28,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstoday,Total Wikipedia company page views in the USA module today (calendar day; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,10,10,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstodayafternoon,Wikipedia company page views in the USA module from 12:00 to 17:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,5,5,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstodaydesktop,Total Wikipedia company page views in the USA module via desktop devices today (desktop web; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,10,10,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstodayevening,Wikipedia company page views in the USA module from 18:00 to 23:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,16,19,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstodaymobile,Total Wikipedia company page views in the USA module via mobile devices today (mobile web and apps; module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,21,26,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstodaymorning,Wikipedia company page views in the USA module from 06:00 to 11:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,10,13,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models
+oth571_viewstodaynight,Wikipedia company page views in the USA module from 00:00 to 05:59 today (module timezone),"{'id': 'other571', 'name': 'Wikipedia Viewing Data'}","{'id': 'other', 'name': 'Other'}","{'id': 'other-analyst-models', 'name': 'Analyst Models'}",IND,1,TOP500,MATRIX,0.9507,0.4492,6,6,1.4,[],other571,Wikipedia Viewing Data,other,Other,other-analyst-models,Analyst Models

simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv

@@ -0,0 +1,525 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+adjfactor,Adjustment factor applied to historical prices and dividends to account for splits and other corporate actions,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,0,0,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+adv20,Average daily volume in past 20 days,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,807,5362,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+cap,Daily market capitalization (in millions),"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1343,13996,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+close,Daily close price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1979,35257,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+dividend,Dividend,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,282,3636,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+high,Daily high price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,718,6168,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+low,Daily low price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,559,5053,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+open,Daily open price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,917,16295,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+returns,Daily returns,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1929,33747,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+sharesout,Daily outstanding shares (in millions),"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,372,3302,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+split,Stock split ratio,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,316,3390,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+volume,Daily volume,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1217,8816,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+vwap,Daily volume weighted average price,"{'id': 'pv1', 'name': 'Price Volume Data for Equity'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,715,5610,1.2,[],pv1,Price Volume Data for Equity,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_final_trade_price,Last trade price recorded at the end of the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,339,1068,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_initial_trade_price,First trade price recorded at the start of the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,236,726,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_market_value,Aggregate market value of all trades during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,243,926,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_max_trade_price,Highest trade price recorded during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,213,781,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_min_trade_price,Lowest trade price recorded during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,209,762,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_total_traded_volume,Total number of shares or contracts traded during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,143,299,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+session_1430to1430_volume_weighted_avg_price,Volume-weighted average price for trades during the 14:30 to 14:30 session.,"{'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,223,801,1.2,[],pv103,Interval and MOO&MOC statistics,pv,Price Volume,pv-price-volume,Price Volume
+aggregated_slippage_metric,Comprehensive measure of slippage across multiple trades or venues.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,93,181,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+asia_trade_cost_buy,Estimated cost incurred when buying in Asian markets.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,30,37,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+asia_trade_cost_sell,Estimated cost incurred when selling in Asian markets.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,16,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+asian_market_slippage,Slippage metric specific to Asian market trades.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+average_spread_slippage,"Estimated portion of trade slippage attributable to crossing the bid-ask spread, i.e., the extra transaction cost versus mid-price execution when trading futures","{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,23,24,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+bid_ask_price_gap,Difference between the best bid and ask prices for a security.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.989,49,62,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+group_buy_slippage,Slippage incurred when executing grouped buy orders.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,25,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+group_order_slippage,Slippage experienced when executing grouped orders.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,1.0,1.0,1,1,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+group_sell_slippage,Slippage incurred when executing grouped sell orders.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,26,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+korean_market_slippage,"Korea-specific modeled trading slippage overlay that estimates expected execution cost for Korean equities, derived from microstructure and spread data and masked for eligibility","{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,59,105,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+price_difference_bid_ask,Unknown,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.989,49,64,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+pv106_wli_lastspread,Bid-ask spread averaged over the last 30 minutes,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.957,47,105,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+pv106_wli_lastspreadbp,Bid-ask spread over the last 30 minutes expressed in basis points,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.957,39,56,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+pv106_wli_spread,Difference between bid and ask price (raw bid-ask spread),"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9867,83,142,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+pv106_wli_spreadbp,Bid-ask spread expressed in basis points,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9867,63,111,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+slippage_at_spread_20,Slippage value calculated at a spread threshold of 20 units.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,31,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+slippage_commission_2025,Estimated slippage and commission costs for the year 2025.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,21,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+slippage_commission_estimate,Estimated slippage and commission costs for trades.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,25,43,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+transaction_cost_estimate,Estimated cost incurred when executing a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,15,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+transaction_cost_maximum,Maximum estimated transaction cost for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,31,42,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+transaction_cost_median,Median estimated transaction cost for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,24,34,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+transaction_cost_percentile_10,Estimated transaction cost at the 10th percentile for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,23,24,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+transaction_cost_percentile_25,Estimated transaction cost at the 25th percentile for a trade.,"{'id': 'pv106', 'name': 'Microstructure Spread Data'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,15,1.2,[],pv106,Microstructure Spread Data,pv,Price Volume,pv-price-volume,Price Volume
+pv149_status_5,"Status code representing current state of data/record, e.g., valid, missing, suspended","{'id': 'pv149', 'name': 'Holidays and Trading Hours Calendar'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-relationship', 'name': 'Relationship'}",IND,1,TOP500,MATRIX,0.9507,1.0,94,224,1.2,[],pv149,Holidays and Trading Hours Calendar,pv,Price Volume,pv-relationship,Relationship
+industry_grouping_level10_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 10 adaptive clusters based on co-movement of returns; value is cluster label (1 to 10), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,152,274,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level10_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 10 statistically determined clusters based on historical stock return correlations, representing adaptive industry classification labels. Values from 1 to 10 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,80,141,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level10_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 10 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,9,9,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level20_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 20 adaptive clusters based on co-movement of returns; value is cluster label (1 to 20), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,86,169,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level20_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 20 statistically determined clusters based on historical stock return correlations, representing fine-grained industry classification labels. Values from 1 to 20 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,70,122,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level20_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 20 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,9,10,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level2_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 2 adaptive clusters based on co-movement of returns; value is cluster label (1 or 2), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,53,78,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level2_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 2 statistically determined clusters based on historical stock return correlations, representing broad statistical industry classification labels. Values from 1 to 2 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,103,309,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level2_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 2 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,0,0,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level50_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 50 adaptive clusters based on co-movement of returns; value is cluster label (1 to 50), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,100,240,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level50_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 50 statistically determined clusters based on historical stock return correlations, representing very fine-grained industry classification labels. Values from 1 to 50 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,58,89,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level50_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 50 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,7,7,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level5_all_instruments_variant513,"Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 5 adaptive clusters based on co-movement of returns; value is cluster label (1 to 5), or -1/10000 for unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,60,113,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level5_india500_xjp513,"For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 5 statistically determined clusters based on historical stock return correlations, representing intermediate-level statistical industry classification labels. Values from 1 to 5 indicate cluster membership; -1 or 10000 means unclassified","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9261,64,99,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+industry_grouping_level5_top800_xjp513,"Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 5 groups","{'id': 'pv29', 'name': 'Derived Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.1674,7,12,1.2,[],pv29,Derived Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method1_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,35,52,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method1_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,14,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method1_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,20,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method1_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method1_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,20,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method2_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method2_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method2_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method2_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method2_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method3_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method3_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method3_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method3_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method3_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method4_group10,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method4_group2,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method4_group20,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method4_group5,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_minvol1m_pca_method4_group50,"Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method1_group10,Asia equity principal component grouping using method 1 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method1_group2,Asia equity principal component grouping using method 1 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method1_group20,Asia equity principal component grouping using method 1 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method1_group5,Asia equity principal component grouping using method 1 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method1_group50,Asia equity principal component grouping using method 1 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method2_group10,Asia equity principal component grouping using method 2 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method2_group2,Asia equity principal component grouping using method 2 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method2_group20,Asia equity principal component grouping using method 2 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method2_group5,Asia equity principal component grouping using method 2 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method2_group50,Asia equity principal component grouping using method 2 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method3_group10,Asia equity principal component grouping using method 3 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method3_group2,Asia equity principal component grouping using method 3 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method3_group20,Asia equity principal component grouping using method 3 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method3_group5,Asia equity principal component grouping using method 3 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method3_group50,Asia equity principal component grouping using method 3 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method4_group10,Asia equity principal component grouping using method 4 with 10 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method4_group2,Asia equity principal component grouping using method 4 with 2 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method4_group20,Asia equity principal component grouping using method 4 with 20 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method4_group5,Asia equity principal component grouping using method 4 with 5 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+asia_equity_pca_method4_group50,Asia equity principal component grouping using method 4 with 50 clusters.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method1_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method1_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method1_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method1_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method1_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method2_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method2_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method2_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method2_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method2_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method3_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method3_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method3_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method3_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method3_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method4_group10,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method4_group2,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method4_group20,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method4_group5,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+ex_japan_equity_pca_method4_group50,"Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method1_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method1_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method1_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method1_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method1_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method2_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method2_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method2_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method2_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method2_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method3_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method3_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method3_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method3_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method3_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method4_group10,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method4_group2,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method4_group20,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method4_group5,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+exjapan_minvol_method4_group50,"Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor1_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,20,48,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor1_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,21,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor1_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor1_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor1_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,20,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor2_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor2_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor2_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor2_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor2_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor3_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,12,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor3_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor3_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor3_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor3_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor4_group10_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor4_group20_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor4_group2_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor4_group50_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor4_group5_top800_xjp_513,"Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method1_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method1_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method1_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method1_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method1_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method2_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method2_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method2_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method2_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method2_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method3_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method3_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method3_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method3_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method3_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method4_10clusters,Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method4_20clusters,Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method4_2clusters,Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method4_50clusters,Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+factor_based_industry_method4_5clusters,Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+first_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,12,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+first_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+fourth_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+fourth_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method1_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method1_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method1_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT1 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method2_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method2_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method2_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method3_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method3_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method3_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method4_group2,Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method4_group20,Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+global_method4_group50,Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT4 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method1_group2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method1_group20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,15,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method1_group5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method1_group50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method2_group10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method2_group2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method2_group20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method2_group50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method3_group10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method3_group2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method3_group5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method3_group50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method4_group10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method4_group20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_equity_pca_method4_group5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method1_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method1_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method1_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method1_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method1_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method2_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method2_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method2_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method2_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method2_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method3_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method3_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method3_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method3_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method3_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method4_grouping10,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 10 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method4_grouping2,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 2 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method4_grouping20,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 20 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method4_grouping5,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 5 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_region_method4_grouping50,"Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 50 clusters; integer indicating cluster membership","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method1_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method1_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method1_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method1_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method1_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method2_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method2_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method2_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method2_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method2_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method3_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method3_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method3_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method3_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method3_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method4_group10,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method4_group2,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method4_group20,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method4_group5,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hk_top200_pca_method4_group50,Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hongkong_pca_grouping_method1_10clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hongkong_pca_grouping_method2_5clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hongkong_pca_grouping_method3_20clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hongkong_pca_grouping_method4_2clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+hongkong_pca_grouping_method4_50clusters,"Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method1_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,56,134,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method1_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,28,50,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method1_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,26,84,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method1_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,37,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method1_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,25,58,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method2_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,39,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method2_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,44,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method2_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,21,71,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method2_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,67,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method2_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,20,41,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method3_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,21,67,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method3_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,11,30,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method3_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,46,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method3_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,35,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method3_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,12,44,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method4_group10,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,14,25,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method4_group2,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,27,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method4_group20,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,25,60,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method4_group5,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,13,24,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+india_top500_method4_group50,statistical industry classification,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,28,63,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method1_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,23,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method1_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,8,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method1_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method1_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,6,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method1_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method2_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method2_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method2_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method2_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method2_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method3_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method3_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method3_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method3_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method3_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method4_grouping10,"Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method4_grouping2,"Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method4_grouping20,"Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method4_grouping5,"Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+jp_minvol_method4_grouping50,"Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method1_10clusters,Industry group assignment using first PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method1_20clusters,Industry group assignment using first PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method1_2clusters,Industry group assignment using first PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method1_50clusters,Industry group assignment using first PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method1_5clusters,Industry group assignment using first PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method2_10clusters,Industry group assignment using second PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method2_20clusters,Industry group assignment using second PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method2_2clusters,Industry group assignment using second PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method2_50clusters,Industry group assignment using second PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method2_5clusters,Industry group assignment using second PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method3_10clusters,Industry group assignment using third PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method3_20clusters,Industry group assignment using third PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method3_2clusters,Industry group assignment using third PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method3_50clusters,Industry group assignment using third PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method3_5clusters,Industry group assignment using third PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method4_10clusters,Industry group assignment using fourth PCA method and 10 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method4_20clusters,Industry group assignment using fourth PCA method and 20 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method4_2clusters,Industry group assignment using fourth PCA method and 2 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method4_50clusters,Industry group assignment using fourth PCA method and 50 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+minvol_pca_grouping_method4_5clusters,Industry group assignment using fourth PCA method and 5 clusters for minimum volatility universe.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method1_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method1_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method1_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method1_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method1_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT1 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method2_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method2_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method2_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method2_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method2_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT2 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method3_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method3_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method3_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method3_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method3_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT3 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method4_10clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method4_20clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method4_2clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method4_50clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+pca_industry_grouping_method4_5clusters,Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT4 variant within the ASI_KOR module,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_0_all513,"Continuous loading on the 1st robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,14,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_0_top1200_xjp_513,First principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_10_all513,"Continuous loading on the 11th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,7,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_10_top1200_xjp_513,Eleventh principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_11_all513,"Continuous loading on the 12th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_11_top1200_xjp_513,Twelfth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_12_all513,"Continuous loading on the 13th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,8,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_12_top1200_xjp_513,Thirteenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_13_all513,"Continuous loading on the 14th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_13_top1200_xjp_513,Fourteenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_14_all513,"Continuous loading on the 15th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,4,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_14_top1200_xjp_513,Fifteenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_1_all513,"Continuous loading on the 2nd robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,14,15,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_1_top1200_xjp_513,Second principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_2_all513,"Continuous loading on the 3rd robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,19,24,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_2_top1200_xjp_513,Third principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_3_all513,"Continuous loading on the 4th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,15,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_3_top1200_xjp_513,Fourth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_4_all513,"Continuous loading on the 5th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,35,39,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_4_top1200_xjp_513,Fifth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_5_all513,"Continuous loading on the 6th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,10,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_5_top1200_xjp_513,Sixth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_6_all513,"Continuous loading on the 7th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,11,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_6_top1200_xjp_513,Seventh principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_7_all513,"Continuous loading on the 8th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,19,25,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_7_top1200_xjp_513,Eighth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_8_all513,"Continuous loading on the 9th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,13,14,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_8_top1200_xjp_513,Ninth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_9_all513,"Continuous loading on the 10th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9426,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_9_top1200_xjp_513,Tenth principal component value for the top 1200 ex-Japan securities in group 513.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.3788,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method1_10clusters,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method1_20clusters,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method1_2clusters_2,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method1_50clusters,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method1_5clusters,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method2_10clusters,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method2_20clusters,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method2_2clusters_2,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method2_50clusters_2,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method2_5clusters,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_10clusters,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_10clusters_2,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_20clusters,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_20clusters_2,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_2clusters,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_50clusters,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_50clusters_3,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method3_5clusters,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_10clusters,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_10clusters_2,"Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_20clusters,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_20clusters_3,"Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_2clusters,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_2clusters_3,"Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_50clusters,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_50clusters_2,"Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_5clusters,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+principal_component_grouping_method4_5clusters_2,"Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_0,First robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,24,28,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_1,Second robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,12,16,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_10,Eleventh robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,9,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_11,Twelfth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_12,Thirteenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,12,17,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_13,Fourteenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,6,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_14,Fifteenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,11,13,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_2,Third robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,6,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_3,Fourth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_4,Fifth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,10,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_5,Sixth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,5,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_6,Seventh robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,11,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_7,Eighth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,9,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_8,Ninth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,11,15,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+robust_factor_component_9,Tenth robust factor derived from principal component analysis of returns.,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,0.9282,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+second_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,16,32,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+second_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT2 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,17,29,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_equity_pca_method1_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,10,37,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_equity_pca_method1_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,10,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_equity_pca_method1_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,7,9,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_equity_pca_method3_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,12,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_equity_pca_method4_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method1_group2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method1_group20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method1_group50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method2_group10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method2_group2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method2_group20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method2_group50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method3_group2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method3_group5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method3_group50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method4_group10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,4,6,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method4_group20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top100_method4_group5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,5,5,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method1_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method1_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method2_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method2_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method2_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method2_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method2_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method3_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method3_group2,"Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method3_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method3_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method4_group10,"Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method4_group20,"Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method4_group5,"Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top300_method4_group50,"Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method1_group10,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method1_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method1_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method1_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method1_group50,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method2_group10,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method2_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method2_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method2_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method2_group50_500,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method3_group10,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method3_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method3_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method3_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method3_group50,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method4_group10_500,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 10 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method4_group2,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 2 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method4_group20,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 20 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,7,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method4_group5,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 5 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+taiwan_top500_method4_group50,"Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 50 clusters","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,4,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+third_method_cluster10_all,Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,15,37,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+third_method_cluster5_all,Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT3 method for the ALL universe under the _513 variant,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,9,11,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor1_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor1_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor1_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor1_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor1_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT1 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor2_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor2_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor2_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor2_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor2_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT2 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor3_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor3_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor3_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor3_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor3_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT3 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor4_grouping10,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor4_grouping2,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor4_grouping20,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor4_grouping5,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1000_pca_factor4_grouping50,"Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT4 method (513-window variant)","{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor1_grouping10,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor1_grouping2,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor1_grouping20,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor1_grouping5,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor1_grouping50,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor2_grouping10,Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor2_grouping2,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor2_grouping20,Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor2_grouping5,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor2_grouping50,Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor3_grouping2,Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top1500_pca_factor3_grouping5,Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method1_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method1_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method1_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method1_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method1_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT1 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method2_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method2_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method2_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method2_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method2_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT2 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,8,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method3_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method3_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method3_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method3_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method3_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT3 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method4_10,Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method4_2,Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method4_20,Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,0,0,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method4_5,Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+top500_industry_grouping_method4_50,Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT4 method (513-window variant),"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method1_grouping10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method1_grouping5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method2_grouping5,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 5 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method3_grouping10,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 10 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,1,1,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method3_grouping20,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 20 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,3,3,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method4_grouping2,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 2 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume
+tw_region_method4_grouping50,Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 50 clusters,"{'id': 'pv30', 'name': 'Alternate Industry Classification'}","{'id': 'pv', 'name': 'Price Volume'}","{'id': 'pv-price-volume', 'name': 'Price Volume'}",IND,1,TOP500,MATRIX,0.9507,1.0,2,2,1.2,[],pv30,Alternate Industry Classification,pv,Price Volume,pv-price-volume,Price Volume

simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv

@@ -0,0 +1,7 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+rsk68_beta,"Unitless stock beta for a European asset relative to the regional liquidity-weighted market_return, typically estimated over a long rolling window (e.g., ~504 trading days)","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.9589,436,826,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models
+rsk68_residual_return,"Idiosyncratic 1-day return for a European asset unexplained by the market factor, computed as actual return minus beta times market_return, expressed in decimal","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.9587,821,2301,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models
+rsk68_weight_dadv,"Simple moving average dollar average daily trading volume for European instruments, used as a liquidity weight (dollar units)","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.9884,294,500,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models
+rsk68_weight_edadv,"Exponentially decayed dollar average daily trading volume for European instruments, used as a liquidity weight (dollar units)","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.989,282,476,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models
+rsk68_weight_volatility_short,"Short-term volatility estimate for European instruments, expressed in decimal terms","{'id': 'risk68', 'name': 'Forecast Data'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,0.9507,0.998,434,912,1.0,[],risk68,Forecast Data,risk,Risk,risk-risk-models,Risk Models
+alternative_market_cap_usd,Alternative calculation of market capitalization in US dollars.,"{'id': 'risk88', 'name': 'Other Multi-Factor Risk Models'}","{'id': 'risk', 'name': 'Risk'}","{'id': 'risk-risk-models', 'name': 'Risk Models'}",IND,1,TOP500,MATRIX,1.0,1.0,548,1351,1.0,[],risk88,Other Multi-Factor Risk Models,risk,Risk,risk-risk-models,Risk Models

simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv

@@ -0,0 +1,211 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+snt21_2neg_conf_low,Lower bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,351,797,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neg_conf_up,Upper bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,212,541,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neg_max,Maximum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,221,488,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neg_mean,Mean value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,123,249,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neg_median,Median value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,126,306,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neg_min,Minimum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,110,254,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neg_std,Standard deviation of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,132,313,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_conf_low,Lower bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,96,198,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_conf_up,Upper bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,74,130,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_max,Maximum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,85,165,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_mean,Mean value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,72,135,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_median,Median value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,75,128,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_min,Minimum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,107,221,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2neut_std,Standard deviation of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,53,68,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_conf_low,Lower bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,58,109,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_conf_up,Upper bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,68,101,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_max,Maximum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,78,129,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_mean,Mean value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,87,155,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_median,Median value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,62,92,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_min,Minimum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,99,191,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_2pos_std,Standard deviation of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,118,305,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_conf_low,Lower bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,50,124,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_conf_up,Upper bound of the confidence interval for the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,36,52,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_max,Maximum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,64,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_mean,Mean value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,40,54,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_median,Median value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,60,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_min,Minimum value of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,64,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neg_std,Standard deviation of the negative sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,61,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_conf_low,Lower bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,36,50,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_conf_up,Upper bound of the confidence interval for the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,30,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_max,Maximum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,34,36,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_mean,Mean value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,32,49,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_median,Median value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,31,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_min,Minimum value of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,31,77,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3neut_std,Standard deviation of the neutral sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,36,61,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_conf_low,Lower bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_conf_up,Upper bound of the confidence interval for the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,31,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_max,Maximum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,43,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_mean,Mean value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,24,25,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_median,Median value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,32,51,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_min,Minimum value of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,29,39,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_3pos_std,Standard deviation of the positive sentiment measure in USA modules,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,81,161,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_conf_low,"Lower bound of the approximate 95% confidence interval for the daily mean negative sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,55,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_conf_up,"Upper bound of the approximate 95% confidence interval for the daily mean negative sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,31,39,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_max,Daily maximum of negative sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,42,48,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_mean,"Daily mean negative sentiment score from TRNA equity-linked news linked to the stock, computed by the in-house transformer in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,39,51,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_median,Daily median negative sentiment score from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,46,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_min,Daily minimum of negative sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,70,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neg_std,Daily standard deviation of negative sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,32,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_conf_low,"Daily lower bound of the approximate 95% confidence interval for the mean neutral sentiment score, aggregated from that day's TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,23,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_conf_up,"Daily upper bound of the approximate 95% confidence interval for the mean neutral sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_max,Daily maximum of neutral sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_mean,"Daily mean neutral sentiment score from TRNA equity-linked news linked to the stock, computed by the in-house transformer in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,43,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_median,Daily median neutral sentiment score from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,30,34,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_min,Daily minimum of neutral sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,29,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4neut_std,Daily standard deviation of neutral sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_conf_low,"Daily lower bound of the approximate 95% confidence interval for the mean positive sentiment score, aggregated from TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,27,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_conf_up,"Daily upper bound of the approximate 95% confidence interval for the mean positive sentiment score, aggregated from that day's TRNA equity-linked news linked to the stock in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,43,61,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_max,Daily maximum of positive sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,47,101,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_mean,"Daily mean positive sentiment score from TRNA equity-linked news linked to the stock, computed by the in-house transformer in this USA D0 module","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,34,57,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_median,Daily median positive sentiment score from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_min,Daily minimum of positive sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,27,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_4pos_std,Daily standard deviation of positive sentiment scores from TRNA equity-linked news linked to the stock in this USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,41,57,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_conf_low,Lower bound of the confidence interval for the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,33,44,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_conf_up,Upper bound of the confidence interval for the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,46,84,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_max,Maximum value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,55,128,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_mean,Mean value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,57,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_median,Median value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,24,30,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_min,Minimum value of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,32,52,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neg_std,Standard deviation of the negative sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,54,89,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_conf_low,Lower bound of the confidence interval for the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,31,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_conf_up,Upper bound of the confidence interval for the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,14,28,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_max,Maximum value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,29,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_mean,Mean value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,28,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_median,Median value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_min,Minimum value of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,38,104,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5neut_std,Standard deviation of the neutral sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,20,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_conf_low,Lower bound of the confidence interval for the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,23,36,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_conf_up,Upper bound of the confidence interval for the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_max,Maximum value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,44,96,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_mean,Mean value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,36,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_median,Median value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,16,19,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_min,Minimum value of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,31,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_5pos_std,Standard deviation of the positive sentiment measure,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,33,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_conf_low,Lower bound of the approximate 95% confidence interval for the daily mean negative sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,21,35,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_conf_up,Upper bound of the approximate 95% confidence interval for the daily mean negative sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,18,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_max,Maximum value of the daily negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,24,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_mean,Daily mean of the negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,33,46,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_median,Daily median of the negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,13,29,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_min,Minimum value of the daily negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,16,23,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neg_std,Daily standard deviation of the negative sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,16,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_conf_low,Lower bound of the approximate 95% confidence interval for the daily mean neutral sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,22,38,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_conf_up,Upper bound of the approximate 95% confidence interval for the daily mean neutral sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,20,26,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_max,Maximum value of the daily neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,21,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_mean,Daily mean of the neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,19,27,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_median,Daily median of the neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,17,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_min,Minimum value of the daily neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,26,37,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_neut_std,Daily standard deviation of the neutral sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,23,29,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_conf_low,Lower bound of the approximate 95% confidence interval for the daily mean positive sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,21,22,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_conf_up,Upper bound of the approximate 95% confidence interval for the daily mean positive sentiment in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,25,28,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_max,Maximum value of the daily positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,28,32,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_mean,Daily mean of the positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,69,143,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_median,Daily median of the positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,27,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_min,"Minimum value of the daily positive sentiment scores (0–1) for a stock in the USA D0 module, aggregated from TRNA equity-linked events","{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,35,45,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt21_pos_std,Daily standard deviation of the positive sentiment scores for a stock in the USA D0 module,"{'id': 'sentiment21', 'name': 'AI Sentiment Score Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.9681,37,46,1.4,[],sentiment21,AI Sentiment Score Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_conf_low,Lower bound of the 95% confidence interval for the mean negative sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,100,159,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_conf_up,Upper bound of the 95% confidence interval for the mean negative sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,82,198,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_max,Maximum negative sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,57,110,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_mean,Average negative sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,53,77,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_median,Median negative sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,55,85,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_min,Minimum negative sentiment score across all Inferess-linked news stories for the stock on that date (same-day aggregation); unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,40,48,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neg_std,Standard deviation of negative sentiment scores across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,67,122,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_conf_low,Lower bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,36,39,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_conf_up,Upper bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,31,38,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_max,Maximum neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,47,114,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_mean,Average neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,44,60,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_median,Median neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,26,52,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_min,Minimum neutral sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,25,36,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2neut_std,Standard deviation of neutral sentiment scores across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,28,30,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_conf_low,Lower bound of the 95% confidence interval for the mean positive sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,27,30,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_conf_up,Upper bound of the 95% confidence interval for the mean positive sentiment score for the stock on that date; may fall outside 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,26,35,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_max,Maximum positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,25,31,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_mean,Average positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,31,48,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_median,Median positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,48,105,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_min,Minimum positive sentiment score across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,52,63,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_2pos_std,Standard deviation of positive sentiment scores across all stories for the stock on that date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,24,26,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_conf_low,"Lower bound of the 95% confidence interval for the mean negative sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly be below 0","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,14,17,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_conf_up,"Upper bound of the 95% confidence interval for the mean negative sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly exceed 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,14,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_max,"Maximum negative sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_mean,"Average negative sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_median,"Median negative sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,14,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_min,"Minimum negative sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neg_std,"Standard deviation of negative sentiment scores across all stories for the stock on the day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_conf_low,"Lower bound of the 95% confidence interval for the mean neutral sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly be below 0","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,24,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_conf_up,"Upper bound of the 95% confidence interval for the mean neutral sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly exceed 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_max,"Maximum neutral sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_mean,"Average neutral sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_median,"Median neutral sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,20,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_min,"Minimum neutral sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,19,56,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3neut_std,"Standard deviation of neutral sentiment scores across all stories for the stock on the day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_conf_low,"Lower bound of the 95% confidence interval for the mean positive sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly be below 0","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_conf_up,"Upper bound of the 95% confidence interval for the mean positive sentiment score across stories for the stock-day (USA, variant 3, next-day aggregation); may slightly exceed 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_max,"Maximum positive sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,13,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_mean,"Average positive sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_median,"Median positive sentiment score across all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,45,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_min,"Minimum positive sentiment score among all Inferess news stories linked to the stock on the given trading day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_3pos_std,"Standard deviation of positive sentiment scores across all stories for the stock on the day (USA, variant 3, next-day aggregation); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_conf_low,"Lower bound of the 95% confidence interval for the mean negative sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,24,30,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_conf_up,"Upper bound of the 95% confidence interval for the mean negative sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_max,"Maximum negative sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,18,22,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_mean,"Average negative sentiment score across all stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_median,"Median negative sentiment score across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_min,"Minimum negative sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neg_std,"Standard deviation of negative sentiment scores across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_conf_low,"Lower bound of the 95% confidence interval for the mean neutral sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_conf_up,"Upper bound of the 95% confidence interval for the mean neutral sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_max,"Maximum neutral sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,21,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_mean,Average neutral sentiment score across stories for the stock on the date; unit: proportion 0–1,"{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_median,"Median neutral sentiment score across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_min,"Minimum neutral sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4neut_std,"Standard deviation of neutral sentiment scores across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_conf_low,"Lower bound of the 95% confidence interval for the mean positive sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_conf_up,"Upper bound of the 95% confidence interval for the mean positive sentiment score for the stock-date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,18,21,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_max,"Maximum positive sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,13,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_mean,"Average positive sentiment score across all stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_median,"Median positive sentiment score across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_min,"Minimum positive sentiment score among stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_4pos_std,"Standard deviation of positive sentiment scores across stories for the stock on the date (AMR, variant 4, D0); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,14,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_conf_low,"Lower bound of the 95% confidence interval for the mean negative sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_conf_up,"Upper bound of the 95% confidence interval for the mean negative sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_max,"Maximum negative sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_mean,"Average negative sentiment score across all stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,17,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_median,"Median negative sentiment score across stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,14,16,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_min,"Minimum negative sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neg_std,"Standard deviation of negative sentiment scores across stories for the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_conf_low,"Lower bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_conf_up,"Upper bound of the 95% confidence interval for the mean neutral sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,5,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_max,"Maximum neutral sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_mean,"Average neutral sentiment score across all stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_median,"Median neutral sentiment score across stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_min,"Minimum neutral sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,5,5,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5neut_std,"Standard deviation of neutral sentiment scores across stories for the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_conf_low,"Lower bound of the 95% confidence interval for the mean positive sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_conf_up,"Upper bound of the 95% confidence interval for the mean positive sentiment score for the stock on that trading day (AMR, variant 5, D0; may slightly fall outside [0,1])","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_max,"Maximum positive sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,16,21,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_mean,"Average positive sentiment score across news stories linked to the stock on the day (USA, D0, variant 5); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_median,"Median positive sentiment score across stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,4,4,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_min,"Minimum positive sentiment score among stories linked to the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_5pos_std,"Standard deviation of positive sentiment scores across stories for the stock on that trading day (AMR, variant 5, D0; unit: proportion 0–1)","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,9,10,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_conf_low,"Lower bound of the 95% confidence interval for the negative sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,7,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_conf_up,"Upper bound of the 95% confidence interval for the negative sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_max,"Maximum negative sentiment score across all news stories for the stock on the day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,7,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_mean,"Average negative sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,13,13,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_median,"Median negative sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,10,12,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_min,"Minimum negative sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,5,5,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neg_std,"Standard deviation of negative sentiment scores across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_conf_low,"Lower bound of the 95% confidence interval for the neutral sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_conf_up,"Upper bound of the 95% confidence interval for the neutral sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,9,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_max,"Maximum neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,6,6,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_mean,"Average neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,12,18,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_median,"Median neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_min,"Minimum neutral sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,4,4,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_neut_std,"Standard deviation of neutral sentiment scores across all stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,8,8,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_conf_low,"Lower bound of the 95% confidence interval for the positive sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,7,11,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_conf_up,"Upper bound of the 95% confidence interval for the positive sentiment mean for the stock-day (USA, D1); may extend beyond [0,1]","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,15,15,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_max,"Maximum positive sentiment score across all stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,11,19,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_mean,"Average positive sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,31,38,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_median,"Median positive sentiment score across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,32,64,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_min,"Minimum positive sentiment score across all stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,77,161,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment
+snt23_pos_std,"Standard deviation of positive sentiment scores across stories for the stock-day (USA, D1); unit: proportion 0–1","{'id': 'sentiment23', 'name': 'Textual Sentiment Analysis Data'}","{'id': 'sentiment', 'name': 'Sentiment'}","{'id': 'sentiment-sentiment', 'name': 'Sentiment'}",IND,1,TOP500,MATRIX,0.9507,0.7028,24,29,1.4,[],sentiment23,Textual Sentiment Analysis Data,sentiment,Sentiment,sentiment-sentiment,Sentiment

simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv

@@ -0,0 +1,4 @@
+id,description,dataset,category,subcategory,region,delay,universe,type,dateCoverage,coverage,userCount,alphaCount,pyramidMultiplier,themes,dataset_id,dataset_name,category_id,category_name,subcategory_id,subcategory_name
+max_price_decrease_ratio,"The daily proportionate lower price limit ratio (as a decimal or percentage), representing how much a stock’s price can fall in a single day according to exchange rules or special regimes. Used to define price-down limit constraints for each stock on each analytic date","{'id': 'shortinterest5', 'name': 'Daily Price Limit Data'}","{'id': 'shortinterest', 'name': 'Short Interest'}","{'id': 'shortinterest-short-sale-models', 'name': 'Short Sale Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,251,576,1.0,[],shortinterest5,Daily Price Limit Data,shortinterest,Short Interest,shortinterest-short-sale-models,Short Sale Models
+max_price_increase_ratio,"The daily proportionate upper price limit ratio (as a decimal or percentage), representing how much a stock’s price can rise in a single day according to exchange rules or special regimes. Used to define price-up limit constraints for each stock on each analytic date","{'id': 'shortinterest5', 'name': 'Daily Price Limit Data'}","{'id': 'shortinterest', 'name': 'Short Interest'}","{'id': 'shortinterest-short-sale-models', 'name': 'Short Sale Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,214,463,1.0,[],shortinterest5,Daily Price Limit Data,shortinterest,Short Interest,shortinterest-short-sale-models,Short Sale Models
+price_limit_condition,"Numeric code indicating the current tradability regime for the security, such as fully tradable, limit up, limit down, or halted/suspended; requires codebook decoding for exact regime meaning","{'id': 'shortinterest5', 'name': 'Daily Price Limit Data'}","{'id': 'shortinterest', 'name': 'Short Interest'}","{'id': 'shortinterest-short-sale-models', 'name': 'Short Sale Models'}",IND,1,TOP500,MATRIX,0.9507,1.0,207,373,1.0,[],shortinterest5,Daily Price Limit Data,shortinterest,Short Interest,shortinterest-short-sale-models,Short Sale Models

simple72/main.py

@@ -206,6 +206,10 @@ async def generate_alpha(request: Request):
         user_category = data.get('user_category', [])
         categories_to_check = user_category if isinstance(user_category, list) else [user_category] if user_category else []
 
+        # 输出用户选择的类别
+        logger.info(f"[Generate] 用户选择的类别: {categories_to_check}")
+        logger.info(f"[Generate] Region: {data.get('user_region')}, Delay: {data.get('user_delay')}, Universe: {data.get('user_universe')}")
+
         # 创建会话来获取数据集信息
         session = SingleSession()
         session.auth = (data.get('brain_username'), data.get('brain_password'))
@@ -655,6 +659,32 @@ async def download_datafields(request: Request):
         dataset_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dataset')
         os.makedirs(dataset_dir, exist_ok=True)
 
+        # 检查哪些类别已经存在缓存文件
+        categories_to_download_filtered = []
+        already_exists = []
+        for cat in categories_to_download:
+            cache_filename = f"datafields_cache_{region}_{universe}_D{delay}_{cat}.csv"
+            cache_path = os.path.join(dataset_dir, cache_filename)
+            if os.path.exists(cache_path):
+                already_exists.append(cat)
+                logger.info(f"类别 '{cat}' 的缓存文件已存在，跳过下载: {cache_filename}")
+            else:
+                categories_to_download_filtered.append(cat)
+
+        if already_exists:
+            logger.info(f"以下类别缓存已存在，将跳过: {already_exists}")
+
+        if not categories_to_download_filtered:
+            logger.info("所有类别的缓存文件都已存在，无需下载")
+            return JSONResponse(content={
+                "success": True,
+                "count": 0,
+                "message": "所有类别的缓存文件都已存在",
+                "skipped_categories": already_exists
+            })
+
+        categories_to_download = categories_to_download_filtered
+
         logger.info(f"开始下载数据字段: region={region}, delay={delay}, universe={universe}, type={data_type}, categories={categories_to_download}")
 
         total_count = 0
@@ -740,7 +770,11 @@ async def download_results(alpha_id: str):
         # 生成时间戳
         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
         zip_filename = f"{alpha_id}_{timestamp}.zip"
-        zip_path = os.path.join(output_dir, zip_filename)
+        
+        # 保存到 save_zip 文件夹（不会被清空）
+        save_zip_dir = os.path.join(transformer_dir, 'save_zip')
+        os.makedirs(save_zip_dir, exist_ok=True)
+        zip_path = os.path.join(save_zip_dir, zip_filename)
         
         # 创建 zip 文件
         with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:

simple72/templates/app.js

@@ -179,63 +179,65 @@ function populateCategoryButtons(categories) {
     });
 }
 
-function toggleCategory(btn) {
+// 全选/反选功能
-    const allBtn = document.getElementById('cat-all');
+function toggleAllCategories() {
-    const isAllBtn = (btn === allBtn);
+    const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)');
     
-    if (isAllBtn) {
+    // 检查是否全部选中
-        // 点击"不筛选"按钮：选中所有具体类别
+    let allSelected = true;
-        allBtn.style.backgroundColor = '#000080';
+    otherBtns.forEach(b => {
-        allBtn.style.color = 'white';
+        if (!isCategoryButtonSelected(b)) {
+            allSelected = false;
+        }
+    });
     
-        const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)');
+    if (allSelected) {
+        // 全部选中 -> 全不选
         otherBtns.forEach(b => {
-            // 选中所有具体类别按钮
+            b.style.backgroundColor = 'white';
-            b.style.backgroundColor = '#000080';
+            b.style.color = 'black';
-            b.style.color = 'white';
         });
     } else {
-        // 点击具体类别按钮
+        // 不是全部选中 -> 全选
-        if (btn.style.backgroundColor === 'rgb(0, 0, 128)') {
+        otherBtns.forEach(b => {
-            btn.style.backgroundColor = '#c0c0c0';
+            b.style.backgroundColor = '#87CEEB'; // 浅蓝色
-            btn.style.color = 'black';
+            b.style.color = 'black';
-        } else {
-            btn.style.backgroundColor = '#000080';
-            btn.style.color = 'white';
-        }
-
-        // 检查是否有选中的具体类别
-        const anySelected = categoryButtons.querySelectorAll('button:not(#cat-all)');
-        let hasSelected = false;
-        anySelected.forEach(b => {
-            if (b.style.backgroundColor === 'rgb(0, 0, 128)') {
-                hasSelected = true;
-            }
         });
+    }
+}
 
-        // 如果没有选中任何具体类别，恢复"不筛选"按钮
+// 点击具体类别按钮
-        if (!hasSelected) {
+function toggleCategory(btn) {
-            allBtn.style.backgroundColor = '#000080';
+    if (isCategoryButtonSelected(btn)) {
-            allBtn.style.color = 'white';
+        btn.style.backgroundColor = 'white';
-            // 自动选中所有类别
+        btn.style.color = 'black';
-            anySelected.forEach(b => {
+    } else {
-                b.style.backgroundColor = '#000080';
+        btn.style.backgroundColor = '#87CEEB'; // 浅蓝色
-                b.style.color = 'white';
+        btn.style.color = 'black';
-            });
-        } else {
-            allBtn.style.backgroundColor = '#c0c0c0';
-            allBtn.style.color = 'black';
-        }
     }
 }
 
+// 检查类别按钮是否被选中的辅助函数
+function isCategoryButtonSelected(btn) {
+    const bg = btn.style.backgroundColor;
+    // 浅蓝色或 rgb(135, 206, 235) 都表示选中
+    return bg === '#87CEEB' || bg === 'rgb(135, 206, 235)' || bg === 'lightblue';
+}
+
 // 获取选中的类别列表
 function getSelectedCategories() {
-    const allBtn = document.getElementById('cat-all');
     const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)');
 
-    // 如果"不筛选"按钮被选中，返回所有具体类别的值
+    // 获取选中的具体类别（使用新的选中判断）
-    if (allBtn.style.backgroundColor === 'rgb(0, 0, 128)') {
+    const selected = [];
+    otherBtns.forEach(b => {
+        if (isCategoryButtonSelected(b)) {
+            selected.push(b.dataset.value);
+        }
+    });
+
+    // 如果没有选中任何具体类别，返回所有类别（默认全选）
+    if (selected.length === 0) {
         const allCategories = [];
         otherBtns.forEach(b => {
             allCategories.push(b.dataset.value);
@@ -243,13 +245,6 @@ function getSelectedCategories() {
         return allCategories;
     }
 
-    // 否则返回选中的具体类别
-    const selected = [];
-    otherBtns.forEach(b => {
-        if (b.style.backgroundColor === 'rgb(0, 0, 128)') {
-            selected.push(b.dataset.value);
-        }
-    });
     return selected;
 }
 
@@ -323,20 +318,10 @@ form.addEventListener('submit', async (e) => {
     if (delay) formData.user_delay = parseInt(delay);
     if (universe) formData.user_universe = universe;
     
-    const allBtn = document.getElementById('cat-all');
+    // 使用 getSelectedCategories 获取选中的类别
-    let selectedCategories = [];
+    const selectedCategories = getSelectedCategories();
-    
+    if (selectedCategories.length > 0) {
-    if (allBtn.style.backgroundColor !== 'rgb(0, 0, 128)') {
+        formData.user_category = selectedCategories;
-        const categoryBtns = categoryButtons.querySelectorAll('button:not(#cat-all)');
-        categoryBtns.forEach(btn => {
-            if (btn.style.backgroundColor === 'rgb(0, 0, 128)') {
-                selectedCategories.push(btn.dataset.value);
-            }
-        });
-        
-        if (selectedCategories.length > 0) {
-            formData.user_category = selectedCategories;
-        }
     }
     
     submitBtn.disabled = true;

simple72/templates/index.html

@@ -108,25 +108,25 @@
                     </div>
                     
                     <div class="form-group">
-                        <label for="delay">目标Delay</label>
+                        <label for="universe">目标股票池 (可选 - 留空则使用种子Alpha的默认值)</label>
-                        <select id="delay" name="delay">
+                        <select id="universe" name="universe" disabled>
-                            <option value="1" selected>1</option>
+                            <option value="">-- 先选择Delay --</option>
-                            <option value="0">0</option>
                         </select>
                     </div>
 
                     <div class="form-group">
-                        <label for="universe">目标股票池 (可选 - 留空则使用种子Alpha的默认值)</label>
+                        <label for="delay">目标Delay</label>
-                        <select id="universe" name="universe" disabled>
+                        <select id="delay" name="delay">
-                            <option value="">-- 先选择Delay --</option>
+                            <option value="1" selected>1</option>
+                            <option value="0">0</option>
                         </select>
                     </div>
                     
                     <div class="form-group">
                         <label>目标数据类别 (可多选)</label>
                         <div id="category-buttons">
-                            <button type="button" class="btn" id="cat-all" data-value="" onclick="toggleCategory(this)">
+                            <button type="button" class="btn" id="cat-all" data-value="" onclick="toggleAllCategories()">
-                                不筛选 (默认)
+                                全选/反选
                             </button>
                         </div>
                     </div>