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jack 1 week ago
parent e75ed8b4ae
commit 0f0b0371d3
25 changed files with 3728 additions and 82 deletions
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  1. 2
      api_llm_generate/api_llm_generate.py
  2. 5
      simple72/Tranformer/ace_lib.py
  3. 274
      simple72/Tranformer/output/Alpha_candidates.json
  4. 1
      simple72/Tranformer/output/Alpha_generated_expressions_error.json
  5. 47
      simple72/Tranformer/output/Alpha_generated_expressions_success.json
  6. 2
      simple72/Tranformer/test_config.json
  7. 4
      simple72/config.json
  8. 2
      simple72/config_minimax.json
  9. 144
      simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv
  10. 9
      simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv
  11. 689
      simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv
  12. 3
      simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv
  13. 12
      simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv
  14. 2
      simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv
  15. 1590
      simple72/dataset/datafields_cache_IND_TOP500_D1_model.csv
  16. 73
      simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv
  17. 31
      simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv
  18. 525
      simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv
  19. 7
      simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv
  20. 211
      simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv
  21. 4
      simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv
  22. 36
      simple72/main.py
  23. 2
      simple72/running_error.txt
  24. 107
      simple72/templates/app.js
  25. 18
      simple72/templates/index.html

2
api_llm_generate/api_llm_generate.py
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@ -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

5
simple72/Tranformer/ace_lib.py
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@ -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,

274
simple72/Tranformer/output/Alpha_candidates.json
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@ -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
}
]
}
}
}
}

1
simple72/Tranformer/output/Alpha_generated_expressions_error.json
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@ -0,0 +1 @@
[]

47
simple72/Tranformer/output/Alpha_generated_expressions_success.json
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@ -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))"
]

2
simple72/Tranformer/test_config.json
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@ -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",

4
simple72/config.json
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@ -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"
}
}

2
simple72/config_minimax.json
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@ -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,

144
simple72/dataset/datafields_cache_IND_TOP500_D1_analyst.csv
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@ -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
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simple72/dataset/datafields_cache_IND_TOP500_D1_earnings.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_fundamental.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_imbalance.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_institutions.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_macro.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_model.csv
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simple72/dataset/datafields_cache_IND_TOP500_D1_news.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_other.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_pv.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_risk.csv
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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
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simple72/dataset/datafields_cache_IND_TOP500_D1_sentiment.csv
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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
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4
simple72/dataset/datafields_cache_IND_TOP500_D1_shortinterest.csv
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@ -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
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simple72/main.py
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@ -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:

2
simple72/running_error.txt
View File

File diff suppressed because one or more lines are too long

107
simple72/templates/app.js
Unescape View File

@ -179,63 +179,65 @@ function populateCategoryButtons(categories) {
});
}
function toggleCategory(btn) {
const allBtn = document.getElementById('cat-all');
const isAllBtn = (btn === allBtn);
if (isAllBtn) {
// 点击"不筛选"按钮：选中所有具体类别
allBtn.style.backgroundColor = '#000080';
allBtn.style.color = 'white';
// 全选/反选功能
function toggleAllCategories() {
const otherBtns = categoryButtons.querySelectorAll('button:not(#cat-all)');
otherBtns.forEach(b => {
// 选中所有具体类别按钮
b.style.backgroundColor = '#000080';
b.style.color = 'white';
});
} else {
// 点击具体类别按钮
if (btn.style.backgroundColor === 'rgb(0, 0, 128)') {
btn.style.backgroundColor = '#c0c0c0';
btn.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;
// 检查是否全部选中
let allSelected = true;
otherBtns.forEach(b => {
if (!isCategoryButtonSelected(b)) {
allSelected = false;
}
});
// 如果没有选中任何具体类别，恢复"不筛选"按钮
if (!hasSelected) {
allBtn.style.backgroundColor = '#000080';
allBtn.style.color = 'white';
// 自动选中所有类别
anySelected.forEach(b => {
b.style.backgroundColor = '#000080';
b.style.color = 'white';
if (allSelected) {
// 全部选中 -> 全不选
otherBtns.forEach(b => {
b.style.backgroundColor = 'white';
b.style.color = 'black';
});
} else {
allBtn.style.backgroundColor = '#c0c0c0';
allBtn.style.color = 'black';
// 不是全部选中 -> 全选
otherBtns.forEach(b => {
b.style.backgroundColor = '#87CEEB'; // 浅蓝色
b.style.color = 'black';
});
}
}
// 点击具体类别按钮
function toggleCategory(btn) {
if (isCategoryButtonSelected(btn)) {
btn.style.backgroundColor = 'white';
btn.style.color = 'black';
} else {
btn.style.backgroundColor = '#87CEEB'; // 浅蓝色
btn.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
if (allBtn.style.backgroundColor === 'rgb(0, 0, 128)') {
// 获取选中的具体类别（使用新的选中判断）
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;
}
@ -323,21 +318,11 @@ form.addEventListener('submit', async (e) => {
if (delay) formData.user_delay = parseInt(delay);
if (universe) formData.user_universe = universe;
const allBtn = document.getElementById('cat-all');
let selectedCategories = [];
if (allBtn.style.backgroundColor !== 'rgb(0, 0, 128)') {
const categoryBtns = categoryButtons.querySelectorAll('button:not(#cat-all)');
categoryBtns.forEach(btn => {
if (btn.style.backgroundColor === 'rgb(0, 0, 128)') {
selectedCategories.push(btn.dataset.value);
}
});
// 使用 getSelectedCategories 获取选中的类别
const selectedCategories = getSelectedCategories();
if (selectedCategories.length > 0) {
formData.user_category = selectedCategories;
}
}
submitBtn.disabled = true;
submitBtn.textContent = '处理中...';

18
simple72/templates/index.html
Unescape View File

@ -108,25 +108,25 @@
</div>
<div class="form-group">
<label for="delay">目标Delay</label>
<select id="delay" name="delay">
<option value="1" selected>1</option>
<option value="0">0</option>
<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="universe" name="universe" disabled>
<option value="">-- 先选择Delay --</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>
<select id="universe" name="universe" disabled>
<option value="">-- 先选择Delay --</option>
<label for="delay">目标Delay</label>
<select id="delay" name="delay">
<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)">
不筛选 (默认)
<button type="button" class="btn" id="cat-all" data-value="" onclick="toggleAllCategories()">
全选/反选
</button>
</div>
</div>

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