++

change72/run.py

@@ -0,0 +1,102 @@
+import json
+import os
+
+# 获取当前目录下的JSON文件路径
+file_path = os.path.join(os.path.dirname(__file__), 'Alpha_candidates.json')
+
+if not os.path.exists(file_path):
+    print('not find json file')
+    exit(1)
+
+with open(file_path, 'r', encoding='utf-8') as file:
+    json_data = json.load(file)
+
+# 打开输出文件
+output_path = os.path.join(os.path.dirname(__file__), 'output.txt')
+with open(output_path, 'w', encoding='utf-8') as output_file:
+    first_alpha = True  # 用于控制空行
+    total_expressions = 0  # 统计生成的表达式总数
+    
+    for formula_template, data in json_data.items():
+        # 获取占位符和候选数据
+        placeholder_candidates = data.get('placeholder_candidates', {})
+        
+        if not placeholder_candidates:
+            # 如果没有占位符，直接输出原始公式
+            if not first_alpha:
+                output_file.write('\n')  # 空一行
+            output_file.write(formula_template + '\n')
+            first_alpha = False
+            total_expressions += 1
+            continue
+        
+        # 收集所有占位符的候选值
+        placeholder_values = []
+        placeholder_names = []
+        
+        for placeholder_name, candidate_info in placeholder_candidates.items():
+            candidates = candidate_info.get('candidates', [])
+            values = [candidate['id'] for candidate in candidates]
+            placeholder_values.append(values)
+            placeholder_names.append(placeholder_name)
+        
+        # 计算该公式会生成多少表达式（每个占位符候选数的乘积）
+        formula_count = 1
+        for values in placeholder_values:
+            formula_count *= len(values)
+        
+        # 生成所有可能的组合（笛卡尔积）
+        def generate_combinations(values_list, current_index=0, current_combination=None):
+            if current_combination is None:
+                current_combination = {}
+            
+            if current_index == len(values_list):
+                # 生成最终的表达式
+                expression = formula_template
+                for placeholder_name, value in current_combination.items():
+                    expression = expression.replace(placeholder_name, value)
+                yield expression
+            else:
+                placeholder_name = placeholder_names[current_index]
+                for value in values_list[current_index]:
+                    current_combination[placeholder_name] = value
+                    yield from generate_combinations(values_list, current_index + 1, current_combination.copy())
+        
+        # 输出所有生成的表达式
+        expressions_generated = 0
+        for expression in generate_combinations(placeholder_values):
+            # if not first_alpha:
+            #     output_file.write('\n')  # 空一行
+            output_file.write(expression + '\n')
+            first_alpha = False
+            expressions_generated += 1
+        
+        # 验证生成数量是否正确
+        if expressions_generated != formula_count:
+            print(f"警告: 公式 '{formula_template}' 预期生成 {formula_count} 个表达式，实际生成 {expressions_generated} 个")
+        
+        total_expressions += expressions_generated
+
+print(f"Alpha表达式已生成到: {output_path}")
+print(f"共处理了 {len(json_data)} 条公式模板")
+print(f"总共生成了 {total_expressions} 个alpha表达式")
+
+# 可选：显示每个公式的生成数量统计
+print("\n详细统计：")
+for formula_template, data in json_data.items():
+    placeholder_candidates = data.get('placeholder_candidates', {})
+    if not placeholder_candidates:
+        print(f"  '{formula_template}': 1个表达式（无占位符）")
+    else:
+        placeholder_counts = []
+        for placeholder_name, candidate_info in placeholder_candidates.items():
+            candidates = candidate_info.get('candidates', [])
+            placeholder_counts.append(f"{placeholder_name}({len(candidates)}个候选)")
+        
+        formula_count = 1
+        for placeholder_name, candidate_info in placeholder_candidates.items():
+            candidates = candidate_info.get('candidates', [])
+            formula_count *= len(candidates)
+        
+        placeholders_str = " × ".join(placeholder_counts)
+        print(f"  '{formula_template}': {formula_count}个表达式 ({placeholders_str})")

generated_alpha/2026/01/30/1.txt

@@ -0,0 +1,10 @@
+multiply(rank(reverse(market_capitalization_current)),rank(price_difference_bid_ask))
+multiply(rank(reverse(quarter_total_assets)),rank(pv37_low_global1h))
+multiply(rank(short_term_payables_other),rank(reverse(last_closing_price)))
+multiply(rank(fnd72_pit_or_is_q_is_foreign_exch_loss),rank(pv173_zscoresmt5yzspread))
+multiply(rank(social_score_positive_correlation),rank(reverse(pv37_cap_global30m)))
+group_rank(group_neutralize(subtract(ts_mean(divide(multiply(ts_delta(last_closing_price, 1), market_capitalization_current), ts_sum(multiply(last_closing_price, market_capitalization_current), 20)), 20), industry), industry), industry)
+group_zscore(ts_sum(if_else(subtract(ts_mean(pv37_low_global2, 20), last_closing_price) > ts_std_dev(last_closing_price, 20), ts_av_diff(market_capitalization_current, 20), ts_covariance(pv37_cap_global30m, last_closing_price, 20)), 20), industry)
+multiply(ts_rank(divide(subtract(ts_max(quarterly_cash_and_equivalents, 20), ts_min(quarterly_cash_and_equivalents, 20)), ts_mean(quarterly_total_assets, 20)), 20), ts_corr(quarterly_long_term_debt, pv37_low_global30m, 20))
+subtract(ts_regression(market_capitalization_current, pv37_cap_global30m, 20, 0, 1), ts_regression(quarterly_current_assets_2, last_closing_price, 20, 0, 1))
+add(ts_zscore(ts_sum(divide(subtract(ts_delay(market_capitalization_current, 5), market_capitalization_current), ts_mean(market_capitalization_current, 20)), 20), 20), ts_zscore(ts_sum(divide(subtract(ts_delay(quarterly_long_term_debt, 5), quarterly_long_term_debt), ts_mean(quarterly_long_term_debt, 20)), 20), 20))

main.py

@@ -18,7 +18,7 @@ PREPARE_PROMPT = os.path.join(PROJECT_PATH, 'prepare_prompt')
 KEYS_TEXT = os.path.join(PREPARE_PROMPT, 'keys_text.txt')
 
 # 是否使用AI生成
-USE_AI = 1
+USE_AI = 0
 
 # 模型温度
 TEMPERATURE = 0.2

manual_prompt/2026/01/30/manual_prompt_20260130145331.txt

@@ -0,0 +1,816 @@
+Tail Liquidity Premium Factor
+Hypothesis
+During periods of market stress, small-cap or low-liquidity stocks often experience excessive discounting due to concentrated selling by investors, yet they tend to exhibit stronger subsequent rebounds compared to large-cap stocks—manifesting a "tail liquidity premium." This phenomenon stems from institutional aversion to liquidity risk and retail-driven irrational trading under extreme sentiment, creating short-term mispricing that offers opportunities for contrarian strategies.
+Implementation
+Using daily trading data from the past 20 days, select the bottom 10% of stocks by turnover ratio across the market. Compute the volatility of their excess returns relative to industry averages. The final factor score is the product of this volatility and the magnitude of recent price drawdown, designed to identify oversold tail assets with rebound potential.
+
+*=========================================================================================*
+输出格式：
+输出必须是且仅是纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
+===================== !!! 重点(输出方式) !!! =====================
+现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
+不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
+**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+=================================================================
+重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 5 个 alpha：
+不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
+!! 数据集使用要求尽量分散, 不要重复使用
+!! 数据集使用要求尽量分散, 不要重复使用
+=================================================================
+**操作符汇总
+**算术运算符 (Arithmetic):
+abs(x) - 绝对值
+add(x, y, filter=false) - 加法 (x + y)
+densify(x) - 分组字段稠密化
+divide(x, y) - 除法 (x / y)
+inverse(x) - 倒数 (1/x)
+log(x) - 自然对数
+max(x, y, ..) - 最大值
+min(x, y, ..) - 最小值
+multiply(x, y, filter=false) - 乘法 (x * y)
+power(x, y) - 幂运算 (x^y)
+reverse(x) - 取反 (-x)
+sign(x) - 符号函数
+signed_power(x, y) - 保留符号的幂运算
+sqrt(x) - 平方根
+subtract(x, y, filter=false) - 减法 (x - y)
+to_nan(x, value=0, reverse=false) - 值与NaN转换
+**逻辑运算符 (Logical):
+and(input1, input2) - 逻辑与
+if_else(input1, input2, input3) - 条件判断
+input1 < input2 - 小于比较
+input1 <= input2 - 小于等于
+input1 == input2 - 等于比较
+input1 > input2 - 大于比较
+input1 >= input2 - 大于等于
+input1 != input2 - 不等于
+is_nan(input) - 是否为NaN
+not(x) - 逻辑非
+or(input1, input2) - 逻辑或
+**时间序列运算符 (Time Series):
+days_from_last_change(x) - 上次变化天数
+hump(x, hump=0.01) - 限制变化幅度
+jump_decay(x, d, sensitivity=0.5, force=0.1) - 跳跃衰减
+kth_element(x, d, k) - 第K个值
+last_diff_value(x, d) - 最后一个不同值
+ts_arg_max(x, d) - 最大值相对索引
+ts_arg_min(x, d) - 最小值相对索引
+ts_av_diff(x, d) - 与均值的差
+ts_backfill(x, lookback=d, k=1, ignore="NAN") - 回填
+ts_corr(x, y, d) - 时间序列相关性
+ts_count_nans(x, d) - NaN计数
+ts_covariance(y, x, d) - 协方差
+ts_decay_linear(x, d, dense=false) - 线性衰减
+ts_delay(x, d) - 延迟值
+ts_delta(x, d) - 差值 (x - 延迟值)
+ts_max(x, d) - 时间序列最大值
+ts_mean(x, d) - 时间序列均值
+ts_min(x, d) - 时间序列最小值
+ts_product(x, d) - 时间序列乘积
+ts_quantile(x, d, driver="gaussian") - 分位数
+ts_rank(x, d, constant=0) - 时间序列排名
+ts_regression(y, x, d, lag=0, rettype=0) - 回归分析
+ts_scale(x, d, constant=0) - 时间序列缩放
+ts_std_dev(x, d) - 时间序列标准差
+ts_step(1) - 天数计数器
+ts_sum(x, d) - 时间序列求和
+ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率衰减
+ts_target_tvr_delta_limit(x, y, lambda_min=0, lambda_max=1, target_tvr=0.1) - 目标换手率差值限制
+ts_zscore(x, d) - 时间序列Z分数
+**横截面运算符 (Cross Sectional):
+normalize(x, useStd=false, limit=0.0) - 标准化
+quantile(x, driver=gaussian, sigma=1.0) - 分位数转换
+rank(x, rate=2) - 排名
+scale(x, scale=1, longscale=1, shortscale=1) - 缩放
+scale_down(x, constant=0) - 按比例缩放
+vector_neut(x, y) - 向量中性化
+winsorize(x, std=4) - 缩尾处理
+zscore(x) - Z分数
+**向量运算符 (Vector):
+vec_avg(x) - 向量均值
+vec_max(x) - 向量最大值
+vec_min(x) - 向量最小值
+vec_sum(x) - 向量求和
+**变换运算符 (Transformational):
+bucket(rank(x), range="0,1,0.1" or buckets="2,5,6,7,10") - 分桶
+generate_stats(alpha) - 生成统计量
+trade_when(x, y, z) - 条件交易
+**分组运算符 (Group):
+combo_a(alpha, nlength=250, mode='algo1') - 组合Alpha
+group_backfill(x, group, d, std=4.0) - 分组回填
+group_cartesian_product(g1, g2) - 笛卡尔积分组
+group_max(x, group) - 分组最大值
+group_mean(x, weight, group) - 分组均值
+group_min(x, group) - 分组最小值
+group_neutralize(x, group) - 分组中性化
+group_rank(x, group) - 分组排名
+group_scale(x, group) - 分组缩放
+group_zscore(x, group) - 分组Z分数
+**特殊运算符 (Special):
+in - 包含判断
+self_corr(input) - 自相关性
+universe_size - 宇宙大小
+**归约运算符 (Reduce):
+reduce_avg(input, threshold=0) - 平均值归约
+reduce_choose(input, nth, ignoreNan=true) - 选择归约
+reduce_count(input, threshold) - 计数归约
+reduce_ir(input) - IR归约
+reduce_kurtosis(input) - 峰度归约
+reduce_max(input) - 最大值归约
+reduce_min(input) - 最小值归约
+reduce_norm(input) - 范数归约
+reduce_percentage(input, percentage=0.5) - 百分比归约
+reduce_powersum(input, constant=2, precise=false) - 幂和归约
+reduce_range(input) - 范围归约
+reduce_skewness(input) - 偏度归约
+reduce_stddev(input, threshold=0) - 标准差归约
+reduce_sum(input) - 求和归约
+
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
+
+========================= 操作符开始 =======================================
+注意: Operator: 后面的是操作符(是可以使用的),
+Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符
+特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false)
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
+Operator: divide(x, y)
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false)
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false)
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1
+Operator: ts_arg_min(x, d)
+Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default.
+Operator: ts_rank(x, d, constant = 0)
+Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
+Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
+Description: Returns various parameters related to regression function
+Operator: ts_scale(x, d, constant = 0)
+Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
+Operator: ts_std_dev(x, d)
+Description: Returns standard deviation of x for the past d days
+Operator: ts_step(1)
+Description: Returns days' counter
+Operator: ts_sum(x, d)
+Description: Sum values of x for the past d days.
+Operator: ts_zscore(x, d)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
+Operator: normalize(x, useStd = false, limit = 0.0)
+Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
+Operator: quantile(x, driver = gaussian, sigma = 1.0)
+Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector
+Operator: rank(x, rate=2)
+Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0
+Operator: scale(x, scale=1, longscale=1, shortscale=1)
+Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
+Operator: winsorize(x, std=4)
+Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
+Operator: zscore(x)
+Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
+Operator: vec_avg(x)
+Description: Taking mean of the vector field x
+Operator: vec_sum(x)
+Description: Sum of vector field x
+Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
+Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
+Operator: trade_when(x, y, z)
+Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition
+Operator: group_backfill(x, group, d, std = 4.0)
+Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
+Operator: group_mean(x, weight, group)
+Description: All elements in group equals to the mean
+Operator: group_neutralize(x, group)
+Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
+Operator: group_rank(x, group)
+Description: Each elements in a group is assigned the corresponding rank in this group
+Operator: group_scale(x, group)
+Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
+Operator: group_zscore(x, group)
+Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
+========================= 操作符结束 =======================================
+
+========================= 数据字段开始 =======================================
+注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用)
+
+{'id': 60630, 'data_set_name': '可以使用:anl11_esgposcorreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on ESG Max Correlation Score with 100% being the best'}
+{'id': 61975, 'data_set_name': '可以使用:quarter_total_assets', 'description': '不可使用,仅供参考:Total assets reported for the most recent quarter.'}
+{'id': 137311, 'data_set_name': '可以使用:market_capitalization_current', 'description': "不可使用,仅供参考:The current total market value of a company's outstanding shares."}
+{'id': 373209, 'data_set_name': '可以使用:price_difference_bid_ask', 'description': '不可使用,仅供参考:Difference between the highest bid and lowest ask prices for a security.'}
+{'id': 60711, 'data_set_name': '可以使用:social_score_positive_correlation', 'description': '不可使用,仅供参考:Social score weighted by metrics most positively correlated to financial returns.'}
+{'id': 140700, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_foreign_exch_loss', 'description': '不可使用,仅供参考:Foreign Exchange Losses'}
+{'id': 373585, 'data_set_name': '可以使用:pv37_low_global1h', 'description': '不可使用,仅供参考:Global Lowest Price 1H'}
+{'id': 139102, 'data_set_name': '可以使用:short_term_payables_other', 'description': '不可使用,仅供参考:[Quarterly] Total Plan Obligations'}
+{'id': 140590, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_form_of_statement', 'description': '不可使用,仅供参考:No longer supported'}
+{'id': 60897, 'data_set_name': '可以使用:anl14_low_bvps_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Book Value Per Share - Upcoming Year'}
+{'id': 373355, 'data_set_name': '可以使用:pv173_zscoresmt5yzspread', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve.'}
+{'id': 373349, 'data_set_name': '可以使用:pv173_rawratiostwist6m1yavg120dema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of average factor loading of the third principal component for the 6-month and 1-year z-spread'}
+{'id': 62225, 'data_set_name': '可以使用:ttm_price_to_free_cash_flow_per_share_3', 'description': '不可使用,仅供参考:Price divided by free cash flow per share for trailing twelve months.'}
+{'id': 60997, 'data_set_name': '可以使用:anl14_low_ntprep_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Net Profit - Upcoming Year'}
+{'id': 373348, 'data_set_name': '可以使用:pv173_rawratiostwist120dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the third principal component for the z-spread between 6 months and 40 years'}
+{'id': 62359, 'data_set_name': '可以使用:forecast_currency_roa', 'description': '不可使用,仅供参考:Currency in which the ROA forecast is denominated.'}
+{'id': 60950, 'data_set_name': '可以使用:anl14_low_eps_fp1', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming quarter'}
+{'id': 140156, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_apg', 'description': '不可使用,仅供参考:TTM Gross Profit to Assets. It is defined as the TTM (Sales - COGS)/Total Assets.'}
+{'id': 140929, 'data_set_name': '可以使用:fnd86_relative_valuation_score', 'description': '不可使用,仅供参考:Relative valuation score'}
+{'id': 60471, 'data_set_name': '可以使用:anl10_grmpast_det_anntime_6325', 'description': '不可使用,仅供参考:Announce time for gross margin estimates'}
+{'id': 140160, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_earningstorpedo', 'description': '不可使用,仅供参考:Earnings Torpedo. It is defined as Next 4 Quarter EPS Estimate + TTM Real Earnings Surprise/TTM EPS where, TTM Earnings Surprise = Actual EPS - Est EPS, summed over the last 4 quarters.'}
+{'id': 373334, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadindrelsbst', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 138877, 'data_set_name': '可以使用:fnd23_tot_assets', 'description': '不可使用,仅供参考:total assets.'}
+{'id': 60661, 'data_set_name': '可以使用:anl11_gposcorreg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G Max Correlation Score with 100% being the best'}
+{'id': 136841, 'data_set_name': '可以使用:fnd17_alldelay1_qltd2cap', 'description': '不可使用,仅供参考:LT debt/total capital - most recent quarter'}
+{'id': 60918, 'data_set_name': '可以使用:anl14_low_cfps_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Cash Flow Per Share - Upcoming 3 Years'}
+{'id': 373343, 'data_set_name': '可以使用:pv173_rawratiosshift60dzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of ShiftIn Bond Z-spread Curve.'}
+{'id': 139083, 'data_set_name': '可以使用:short_term_loans_advances_quarterly', 'description': '不可使用,仅供参考:Short-term loans and advances provided by the company for the quarter.'}
+{'id': 60922, 'data_set_name': '可以使用:anl14_low_div_fp3', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming 3 quarters'}
+{'id': 61061, 'data_set_name': '可以使用:anl14_mean_capex_fy5', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming 5 years'}
+{'id': 137308, 'data_set_name': '可以使用:last_closing_price', 'description': '不可使用,仅供参考:The most recent closing or last bid price of the security.'}
+{'id': 61992, 'data_set_name': '可以使用:quarterly_cash_and_equivalents', 'description': '不可使用,仅供参考:Cash and short-term investments for the most recent quarter.'}
+{'id': 373554, 'data_set_name': '可以使用:pv37_cap_global30m', 'description': '不可使用,仅供参考:Global Market Capitalization 30M'}
+{'id': 140359, 'data_set_name': '可以使用:fnd6_us_ciotherq', 'description': '不可使用,仅供参考:Comp Inc - Other Adj'}
+{'id': 140133, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_slope4qeps5y', 'description': '不可使用,仅供参考:Slope of 5-yr TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'}
+{'id': 60936, 'data_set_name': '可以使用:anl14_low_ebit_fy2', 'description': '不可使用,仅供参考:The lowest estimation of earnings before interest & taxes - upcoming 2 years'}
+{'id': 140491, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_capital_reserve', 'description': '不可使用,仅供参考:Includes Accumulative Comprehensive Income Reserve, Merger Reserve, as Well as All Other Reserves and Equities Not Included in Retained Earnings'}
+{'id': 60592, 'data_set_name': '可以使用:anl11_emp2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on EMP2 100% being the best'}
+{'id': 60987, 'data_set_name': '可以使用:anl14_low_ntp_fy1', 'description': '不可使用,仅供参考:The Lowest estimation of Net Profit - upcoming year'}
+{'id': 62078, 'data_set_name': '可以使用:quarterly_long_term_debt', 'description': '不可使用,仅供参考:Long-term debt reported for the most recent quarter.'}
+{'id': 140552, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'}
+{'id': 373341, 'data_set_name': '可以使用:pv173_rawratiosshift60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the first principal component for the z-spread between 6 months and 40 years'}
+{'id': 373528, 'data_set_name': '可以使用:top2000_factor1_group20_score', 'description': '不可使用,仅供参考:First factor score for top 2000 securities, grouped into 20 clusters.'}
+{'id': 373586, 'data_set_name': '可以使用:pv37_low_global2', 'description': '不可使用,仅供参考:Global Lowest Price 2'}
+{'id': 373330, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadchg60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 60962, 'data_set_name': '可以使用:anl14_low_epsrep_fp3', 'description': '不可使用,仅供参考:The lowest estimation of reported earnings per share - upcoming 3 quarters'}
+{'id': 60629, 'data_set_name': '可以使用:anl11_esg_ttcrg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on ESG Hybrid Score with 1 being the highest rank'}
+{'id': 60986, 'data_set_name': '可以使用:anl14_low_ntp_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Net Profit - upcoming 5 quarters'}
+{'id': 140546, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cap_expend_prpty_add', 'description': '不可使用,仅供参考:Capital Expenditures/Property Additions'}
+{'id': 140621, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_cogs_to_fe_and_pp_and_g', 'description': '不可使用,仅供参考:Cost of Goods Sold/Fuel Expense & Purchased Power & Gas'}
+{'id': 139088, 'data_set_name': '可以使用:short_term_net_cash_balance', 'description': '不可使用,仅供参考:Net Cash – Beginning Balance represents the beginning balance of cash and cash equivalents, as\r\ndefined by a company.'}
+{'id': 62193, 'data_set_name': '可以使用:quarterly_total_debt_to_assets', 'description': '不可使用,仅供参考:Ratio of total debt to total assets for the quarter.'}
+{'id': 137323, 'data_set_name': '可以使用:normalized_earnings_before_tax_annual', 'description': '不可使用,仅供参考:Earnings before taxes for the most recent fiscal year, excluding unusual or one-time items.'}
+{'id': 136649, 'data_set_name': '可以使用:fnd17_alldelay1_acapspps', 'description': '不可使用,仅供参考:Capital Spending per Share, most recent fiscal year'}
+{'id': 136560, 'data_set_name': '可以使用:earnings_before_tax_quarterly', 'description': '不可使用,仅供参考:Earnings before taxes for the most recent quarter.'}
+{'id': 373545, 'data_set_name': '可以使用:top2000_factor4_group50_score', 'description': '不可使用,仅供参考:Fourth factor score for top 2000 securities, grouped into 50 clusters.'}
+{'id': 136809, 'data_set_name': '可以使用:fnd17_alldelay1_price2bk', 'description': '不可使用,仅供参考:Price to Book - most recent quarter'}
+{'id': 62083, 'data_set_name': '可以使用:quarterly_long_term_debt_per_share_3', 'description': '不可使用,仅供参考:Long-term debt divided by shares outstanding for the quarter.'}
+{'id': 373342, 'data_set_name': '可以使用:pv173_rawratiosshift60dstd60d', 'description': '不可使用,仅供参考:It is defined as the 60-day standard deviation of ShiftIn Bond Z-spread Curve.'}
+{'id': 62087, 'data_set_name': '可以使用:quarterly_long_term_debt_to_capital', 'description': '不可使用,仅供参考:Long-term debt divided by total capital for the quarter.'}
+{'id': 137354, 'data_set_name': '可以使用:quarterly_earnings_before_tax_5', 'description': '不可使用,仅供参考:Earnings before taxes for the most recent quarter.'}
+{'id': 373654, 'data_set_name': '可以使用:pv72_news_score', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 60578, 'data_set_name': '可以使用:anl11_e3reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on E3 1 being the highest rank'}
+{'id': 60635, 'data_set_name': '可以使用:anl11_esgreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on ESG Score with 1 being the highest rank'}
+{'id': 137468, 'data_set_name': '可以使用:annual_reserves_capital', 'description': '不可使用,仅供参考:Restricted Cash – Current not available for use immediately and due within a year'}
+{'id': 60665, 'data_set_name': '可以使用:anl11_greg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G Score with 100% being the best'}
+{'id': 61973, 'data_set_name': '可以使用:lowest_pe_ratio_quarterly', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'}
+{'id': 60979, 'data_set_name': '可以使用:anl14_low_ndebt_fy3', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming 3 years'}
+{'id': 139133, 'data_set_name': '可以使用:total_liabilities_long_term', 'description': '不可使用,仅供参考:Total long-term liabilities outstanding.'}
+{'id': 60963, 'data_set_name': '可以使用:anl14_low_epsrep_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 4 quarters'}
+{'id': 373400, 'data_set_name': '可以使用:pca_industry_grouping_method1_5', 'description': '不可使用,仅供参考:Industry grouping using first method and 5 clusters for top 3000 equities.'}
+{'id': 373541, 'data_set_name': '可以使用:top2000_factor3_group5_score', 'description': '不可使用,仅供参考:Third factor score for top 2000 securities, grouped into 5 clusters.'}
+{'id': 139043, 'data_set_name': '可以使用:share_capital_subscribed', 'description': '不可使用,仅供参考:Share capital subscribed reported for the annual period.'}
+{'id': 373405, 'data_set_name': '可以使用:pca_industry_grouping_method2_5', 'description': '不可使用,仅供参考:Industry grouping using second method and 5 clusters for top 3000 equities.'}
+{'id': 61025, 'data_set_name': '可以使用:anl14_low_revenue_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 2 years'}
+{'id': 373655, 'data_set_name': '可以使用:pv72_news_score_float', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 140903, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_other_adj_comp_inc', 'description': '不可使用,仅供参考:Other Adjustments - Other Comprehensive Income'}
+{'id': 61355, 'data_set_name': '可以使用:anl14_numofests_capex_fp4', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - upcoming 4 quarters'}
+{'id': 62123, 'data_set_name': '可以使用:quarterly_payout_ratio_2', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'}
+{'id': 136795, 'data_set_name': '可以使用:fnd17_alldelay1_nlow', 'description': '不可使用,仅供参考:Price - 12 month low'}
+{'id': 139095, 'data_set_name': '可以使用:short_term_notes_current_2', 'description': '不可使用,仅供参考:Short-term notes currently outstanding.'}
+{'id': 60597, 'data_set_name': '可以使用:anl11_emp3reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on the third EMP score with 1 as highest'}
+{'id': 140699, 'data_set_name': '可以使用:fnd72_pit_or_is_q_is_foreign_crncy_trans_adj', 'description': '不可使用,仅供参考:Foreign Currency Translation Adjustment'}
+{'id': 137516, 'data_set_name': '可以使用:earnings_before_tax', 'description': '不可使用,仅供参考:Earnings before tax expenses are deducted.'}
+{'id': 373640, 'data_set_name': '可以使用:pv72_ibeseps2_score_float', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 61209, 'data_set_name': '可以使用:anl14_median_capex_fy3', 'description': '不可使用,仅供参考:Median of estimations of capital expenditures - upcoming 3 years'}
+{'id': 61510, 'data_set_name': '可以使用:anl14_stddev_capex_fy4', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 4 years'}
+{'id': 61008, 'data_set_name': '可以使用:anl14_low_ptp_fp3', 'description': '不可使用,仅供参考:The lowest estimation of pretax profit - upcoming 3 quarters'}
+{'id': 138994, 'data_set_name': '可以使用:other_non_current_investments', 'description': '不可使用,仅供参考:Other non-current investments held by the company.'}
+{'id': 60924, 'data_set_name': '可以使用:anl14_low_div_fp5', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming 5 quarters'}
+{'id': 61966, 'data_set_name': '可以使用:cash_flow_per_share_quarterly_prev_year', 'description': '不可使用,仅供参考:Cash flow per share for the same quarter in the previous year.'}
+{'id': 140584, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_chng_non_cash_work_cap', 'description': '不可使用,仅供参考:Changes in Non-Cash Working Capital'}
+{'id': 62081, 'data_set_name': '可以使用:quarterly_long_term_debt_per_share', 'description': '不可使用,仅供参考:Long-term debt divided by shares outstanding for the quarter.'}
+{'id': 62058, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_alt_2', 'description': '不可使用,仅供参考:Alternate calculation of free cash flow per share for the quarter.'}
+{'id': 137178, 'data_set_name': '可以使用:fnd17_qtrperiods', 'description': '不可使用,仅供参考:Number of historical periods - Quarterly'}
+{'id': 373336, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'}
+{'id': 62056, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_3', 'description': '不可使用,仅供参考:Free cash flow divided by average shares for the quarter.'}
+{'id': 60430, 'data_set_name': '可以使用:anl10_fcfpast_det_analyst_6379', 'description': '不可使用,仅供参考:Analyst ID for free cash flow estimates'}
+{'id': 60353, 'data_set_name': '可以使用:anl10_cpspast_det_estflag_6355', 'description': '不可使用,仅供参考:Estimate flag for cash per share'}
+{'id': 60588, 'data_set_name': '可以使用:anl11_emp1reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on EMP1 100% being the best'}
+{'id': 61893, 'data_set_name': '可以使用:anl15_s_cal_fy2_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a calendarized 2 fiscal year mean price estimation.'}
+{'id': 62211, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price', 'description': '不可使用,仅供参考:Working capital per share divided by current price for the quarter.'}
+{'id': 138921, 'data_set_name': '可以使用:loans_current_assets_value', 'description': '不可使用,仅供参考:[Quarterly] Customer Advances'}
+{'id': 139029, 'data_set_name': '可以使用:return_on_equity_ratio_3', 'description': "不可使用,仅供参考:Return on equity, calculated as net income divided by average shareholders' equity."}
+{'id': 140163, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_mpgghcy3', 'description': '不可使用,仅供参考:3 Year Change in Gross Profit Margin. It is defined as the 3-year-on-year change in gross profit margin.'}
+{'id': 138947, 'data_set_name': '可以使用:long_term_other_liabilities', 'description': '不可使用,仅供参考:[Quarterly] Long-Term Operating Lease Liabilities, Supplemental'}
+{'id': 60941, 'data_set_name': '可以使用:anl14_low_ebitda_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 2 quarters'}
+{'id': 60970, 'data_set_name': '可以使用:anl14_low_nav_fy1', 'description': '不可使用,仅供参考:The lowest estimation of net asset value - upcoming year'}
+{'id': 140818, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_cur_asset_less_prepay', 'description': '不可使用,仅供参考:Other assets which are not account and notes receivable, inventory, derivative assets, deferred tax assets, prepaid expenses, assets for held for sales, income taxes receivable, and assets of discontinued operations'}
+{'id': 138939, 'data_set_name': '可以使用:long_term_loans_due', 'description': '不可使用,仅供参考:Long-term loans due beyond one year.'}
+{'id': 138984, 'data_set_name': '可以使用:other_long_term_obligations', 'description': '不可使用,仅供参考:Other long-term obligations reported for the annual period.'}
+{'id': 60483, 'data_set_name': '可以使用:anl10_roapast_det_estage_6330', 'description': '不可使用,仅供参考:Age of return on assets estimates in days'}
+{'id': 138883, 'data_set_name': '可以使用:fully_paid_share_capital', 'description': '不可使用,仅供参考:Total value of fully paid share capital issued by the company.'}
+{'id': 140153, 'data_set_name': '可以使用:fnd31_ohlsonscore', 'description': "不可使用,仅供参考:Ohlson Bankruptcy Score. It is a model assessing a company's probability of bankruptcy by considering firm size, capital structure, financial performance, and liquidity. It is derived from Ohlson (1980)."}
+{'id': 373658, 'data_set_name': '可以使用:pv72_pv_score_float', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 137351, 'data_set_name': '可以使用:quarterly_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Ratio of total debt to total assets for the most recent quarter.'}
+{'id': 373645, 'data_set_name': '可以使用:pv72_ibesptg2_score', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 137518, 'data_set_name': '可以使用:earnings_from_residual_operations', 'description': '不可使用,仅供参考:[Quarterly] Restructuring Charge'}
+{'id': 62360, 'data_set_name': '可以使用:forecast_currency_roe', 'description': '不可使用,仅供参考:Currency in which the ROE forecast is denominated.'}
+{'id': 140581, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_cash_from_oper', 'description': '不可使用,仅供参考:Total amount of cash a company generates from its operation'}
+{'id': 373310, 'data_set_name': '可以使用:pv173_ranksmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 140158, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_apgghcy3', 'description': '不可使用,仅供参考:3-Year Change in Gross Profit to Assets. It is defined as the 3-Year on Year Change in Gross Profit to Assets.'}
+{'id': 60955, 'data_set_name': '可以使用:anl14_low_eps_fy1', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming year'}
+{'id': 140815, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'}
+{'id': 60475, 'data_set_name': '可以使用:anl10_grmpast_det_excflag_6317', 'description': '不可使用,仅供参考:Exclusion flag for gross margin estimates'}
+{'id': 140442, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'}
+{'id': 139124, 'data_set_name': '可以使用:stockholders_other_paid_in_items', 'description': '不可使用,仅供参考:Other paid-in items attributable to stockholders.'}
+{'id': 140768, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'}
+{'id': 136445, 'data_set_name': '可以使用:other_cash_payments_2', 'description': '不可使用,仅供参考:Value of ad hoc cash payments such as relocation or fringe benefits (alternate).'}
+{'id': 139040, 'data_set_name': '可以使用:share_capital_in_preference', 'description': '不可使用,仅供参考:Share capital in preference reported for the interim period.'}
+{'id': 60519, 'data_set_name': '可以使用:anl11_cit1reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on CIT1 1 being the highest rank'}
+{'id': 60605, 'data_set_name': '可以使用:anl11_emp_ttcrg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on EMP Hybrid Score with 100% being the best'}
+{'id': 60974, 'data_set_name': '可以使用:anl14_low_ndebt_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 2 Quarters'}
+{'id': 373359, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadchgstd20dsbst', 'description': '不可使用,仅供参考:It is defined as the 20-day standard deviation of changeIn 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 61040, 'data_set_name': '可以使用:anl14_low_roe_fy4', 'description': '不可使用,仅供参考:The lowest estimation of Returns on Equity - upcoming 4 years'}
+{'id': 138893, 'data_set_name': '可以使用:income_before_tax', 'description': '不可使用,仅供参考:[Quarterly] Total Plan Interest Cost'}
+{'id': 137485, 'data_set_name': '可以使用:common_shares_outstanding_quarter_2', 'description': '不可使用,仅供参考:Number of common shares outstanding at quarter end.'}
+{'id': 136797, 'data_set_name': '可以使用:fnd17_alldelay1_nprice', 'description': '不可使用,仅供参考:Price - closing or last bid'}
+{'id': 60612, 'data_set_name': '可以使用:anl11_empreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on EMP Score with 1 being the highest rank'}
+{'id': 136453, 'data_set_name': '可以使用:total_equity_at_risk_2', 'description': '不可使用,仅供参考:Total stock, option, and incentive plan awards at risk for the director (alternate).'}
+{'id': 138977, 'data_set_name': '可以使用:other_assets_purchase_balance', 'description': '不可使用,仅供参考:represents changes in accounts payable during the period. An increase inaccounts payable has a positive impact on cash flow from operating activities, and vice versa.'}
+{'id': 140168, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pcwt', 'description': '不可使用,仅供参考:Time Weighted Cash Yield. It is defined as the time-weighted average of cash flows per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'}
+{'id': 60351, 'data_set_name': '可以使用:anl10_cpspast_det_anntime_6354', 'description': '不可使用,仅供参考:Announce time for cash per share estimates'}
+{'id': 137312, 'data_set_name': '可以使用:market_capitalization_latest', 'description': "不可使用,仅供参考:The latest available total market value of a company's outstanding shares."}
+{'id': 136947, 'data_set_name': '可以使用:fnd17_alldelay1_ttmpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, trailing 12 months'}
+{'id': 60570, 'data_set_name': '可以使用:anl11_e1reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on E1 1 being the highest rank'}
+{'id': 139085, 'data_set_name': '可以使用:short_term_loans_debt_2', 'description': '不可使用,仅供参考:Short-term loans and debt obligations due within one year.'}
+{'id': 62017, 'data_set_name': '可以使用:quarterly_current_assets_3', 'description': '不可使用,仅供参考:Sum of all current assets for the most recent quarter.'}
+{'id': 139096, 'data_set_name': '可以使用:short_term_notes_discounted', 'description': '不可使用,仅供参考:Short-term notes discounted and outstanding at period end.'}
+{'id': 373531, 'data_set_name': '可以使用:top2000_factor1_group5_score', 'description': '不可使用,仅供参考:First factor score for top 2000 securities, grouped into 5 clusters.'}
+{'id': 137698, 'data_set_name': '可以使用:fnd23_curr_assets', 'description': '不可使用,仅供参考:current assets'}
+{'id': 140850, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_cash_from_oper', 'description': '不可使用,仅供参考:Total amount of cash a company generates from its operation'}
+{'id': 139071, 'data_set_name': '可以使用:short_term_debt_issued', 'description': '不可使用,仅供参考:Depreciation/Depletion represents the sum of Depreciation and Depletion.'}
+{'id': 373648, 'data_set_name': '可以使用:pv72_ibesptg_score', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 137532, 'data_set_name': '可以使用:extraordinary_items_other', 'description': '不可使用,仅供参考:Other extraordinary items reported in the financials.'}
+{'id': 60432, 'data_set_name': '可以使用:anl10_fcfpast_det_estage_6380', 'description': '不可使用,仅供参考:Age of free cash flow estimates in days'}
+{'id': 62038, 'data_set_name': '可以使用:quarterly_earnings_before_tax_3', 'description': '不可使用,仅供参考:Pretax income for the most recent quarter.'}
+{'id': 60989, 'data_set_name': '可以使用:anl14_low_ntp_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Profit - Upcoming 3 Years'}
+{'id': 137517, 'data_set_name': '可以使用:earnings_from_associates', 'description': '不可使用,仅供参考:[Quarterly] Research & Development'}
+{'id': 140859, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'}
+{'id': 137415, 'data_set_name': '可以使用:twelve_month_low_price', 'description': '不可使用,仅供参考:The lowest price at which the security traded in the last twelve months.'}
+{'id': 139162, 'data_set_name': '可以使用:voting_common_shares_outstanding', 'description': '不可使用,仅供参考:Number of voting common shares currently outstanding.'}
+{'id': 60362, 'data_set_name': '可以使用:anl10_dpspast_det_anntime_6225', 'description': '不可使用,仅供参考:Announce time for dividends per share estimates'}
+{'id': 137131, 'data_set_name': '可以使用:fnd17_qcapspps', 'description': '不可使用,仅供参考:Capital Spending per Share. most recent quarter'}
+{'id': 60961, 'data_set_name': '可以使用:anl14_low_epsrep_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - upcoming 2 quarters'}
+{'id': 62212, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price_2', 'description': '不可使用,仅供参考:Ratio of working capital per share to current price for the quarter.'}
+{'id': 62068, 'data_set_name': '可以使用:quarterly_interest_coverage_ratio', 'description': '不可使用,仅供参考:Earnings before interest and taxes divided by interest expense for the quarter.'}
+{'id': 373363, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'}
+{'id': 62076, 'data_set_name': '可以使用:quarterly_inventory_turnover_ratio', 'description': '不可使用,仅供参考:Inventory turnover ratio for the most recent quarter.'}
+{'id': 60894, 'data_set_name': '可以使用:anl14_low_bvps_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Book Value Per Share - upcoming 3 quarters'}
+{'id': 60956, 'data_set_name': '可以使用:anl14_low_eps_fy2', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 2 years'}
+{'id': 139052, 'data_set_name': '可以使用:shareholders_other_equity_2', 'description': '不可使用,仅供参考:Other equity attributable to shareholders.'}
+{'id': 140867, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_non_cash_adj_less', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'}
+{'id': 138999, 'data_set_name': '可以使用:other_unusual_income_expense', 'description': '不可使用,仅供参考:Other unusual income or expense items not classified elsewhere.'}
+{'id': 140189, 'data_set_name': '可以使用:fnd44_factor_benford', 'description': '不可使用,仅供参考:Benford score based on KS statistics of first and second digits.'}
+{'id': 60913, 'data_set_name': '可以使用:anl14_low_cfps_fp2', 'description': '不可使用,仅供参考:The lowest estimation of Cash Flow Per Share - upcoming 2 quarters'}
+{'id': 62481, 'data_set_name': '可以使用:anl69_best_px_bps_ratio', 'description': '不可使用,仅供参考:P/Bk'}
+{'id': 140813, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'}
+{'id': 373365, 'data_set_name': '可以使用:pv173_zscoresreltermspreadtransfema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread.'}
+{'id': 373587, 'data_set_name': '可以使用:pv37_low_global2h', 'description': '不可使用,仅供参考:Global Lowest Price 2H'}
+{'id': 139092, 'data_set_name': '可以使用:short_term_non_current_cash', 'description': '不可使用,仅供参考:Short-term cash classified as non-current.'}
+{'id': 136843, 'data_set_name': '可以使用:fnd17_alldelay1_qpayratio', 'description': '不可使用,仅供参考:Payout ratio - most recent quarter'}
+{'id': 140511, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_other_lt_liabilities', 'description': '不可使用,仅供参考:Other LT Liabilities'}
+{'id': 60717, 'data_set_name': '可以使用:social_totalcorr_industry_percentile', 'description': '不可使用,仅供参考:Percentile ranking within industry peer group for social score weighted by total correlation to financial returns.'}
+{'id': 60898, 'data_set_name': '可以使用:anl14_low_bvps_fy2', 'description': '不可使用,仅供参考:The Lowest estimation of Book Value Per Share - upcoming 2 years'}
+{'id': 139142, 'data_set_name': '可以使用:unrealized_other_gain_balancesheet', 'description': '不可使用,仅供参考:[Quarterly] Right-of-Use Assets - Operating Lease, Gross - Supplemental'}
+{'id': 140131, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_psdwf', 'description': '不可使用,仅供参考:Leading 12 Month Sales Yield. It is defined as the FY1 estimate of Sales value per share divided by its trading price.'}
+{'id': 140132, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts' earnings forecasts for fiscal year 2, scaled by previous month-end trading price."}
+{'id': 60930, 'data_set_name': '可以使用:anl14_low_ebit_fp1', 'description': '不可使用,仅供参考:The lowest estimation of earnings before interest & taxes - upcoming quarter'}
+{'id': 62114, 'data_set_name': '可以使用:quarterly_non_annualized_cash_flow_per_share', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized.'}
+{'id': 139110, 'data_set_name': '可以使用:short_term_tax_incentives', 'description': '不可使用,仅供参考:Short-term tax incentives recognized during the period.'}
+{'id': 140580, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_cash_from_inv_act', 'description': '不可使用,仅供参考:Cash From Investing Activities'}
+{'id': 62590, 'data_set_name': '可以使用:anl69_ebit_most_recent_period_end_dt', 'description': '不可使用,仅供参考:Most Recent Period End Date'}
+{'id': 62080, 'data_set_name': '可以使用:quarterly_long_term_debt_3', 'description': '不可使用,仅供参考:Sum of all long-term debt for the most recent quarter.'}
+{'id': 137100, 'data_set_name': '可以使用:fnd17_nprice', 'description': '不可使用,仅供参考:Price - closing or last bid'}
+{'id': 373337, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadzscore60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 60944, 'data_set_name': '可以使用:anl14_low_ebitda_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 5 quarters'}
+{'id': 61970, 'data_set_name': '可以使用:free_cash_flow_per_share_quarterly_prev_year', 'description': '不可使用,仅供参考:Free cash flow per share for the same quarter in the previous year, not annualized.'}
+{'id': 138898, 'data_set_name': '可以使用:insurance_premiums_subscribed', 'description': '不可使用,仅供参考:[Quarterly] Prior Service Cost - Domestic'}
+{'id': 137151, 'data_set_name': '可以使用:fnd17_qpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, most recent quarter'}
+{'id': 373333, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadindrel', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve.'}
+{'id': 139039, 'data_set_name': '可以使用:share_capital_extraordinary', 'description': '不可使用,仅供参考:Extraordinary share capital reported for the annual period.'}
+{'id': 139082, 'data_set_name': '可以使用:short_term_loans_advances', 'description': '不可使用,仅供参考:Short-term loans and advances provided by the company.'}
+{'id': 60573, 'data_set_name': '可以使用:anl11_e2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on E2 100% being the best'}
+{'id': 373399, 'data_set_name': '可以使用:pca_industry_grouping_method1_20', 'description': '不可使用,仅供参考:Industry grouping using first method and 20 clusters for top 3000 equities.'}
+{'id': 61037, 'data_set_name': '可以使用:anl14_low_roe_fy1', 'description': '不可使用,仅供参考:The Lowest Estimation of Returns on Equity - Upcoming Year'}
+{'id': 140884, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_cogs_to_fe_and_pp_and_g', 'description': '不可使用,仅供参考:Cost of Goods Sold/Fuel Expense & Purchased Power & Gas'}
+{'id': 140134, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev', 'description': '不可使用,仅供参考:Time Weighted EBITDA/EV. It is defined as the time-weighted EBITDA/Enterprise Value for FY1 and FY2.'}
+{'id': 137036, 'data_set_name': '可以使用:fnd17_chpctpricemtd', 'description': '不可使用,仅供参考:Month to Date price change in percentage'}
+{'id': 137538, 'data_set_name': '可以使用:financial_risk_reserve', 'description': '不可使用,仅供参考:Financial risk reserve at the reporting date.'}
+{'id': 60713, 'data_set_name': '可以使用:social_score_sector_position', 'description': '不可使用,仅供参考:Company’s position within sector peer group for social score.'}
+{'id': 137479, 'data_set_name': '可以使用:capital_invested_accumulated_cost', 'description': '不可使用,仅供参考:Capital invested at accumulated cost for the interim period.'}
+{'id': 62355, 'data_set_name': '可以使用:forecast_currency_code_2', 'description': '不可使用,仅供参考:Currency code in which the net profit forecast is denominated.'}
+{'id': 138915, 'data_set_name': '可以使用:investment_income_cash_flow', 'description': '不可使用,仅供参考:Cash used for discontinued operations classified as Other Investing Cash Flow'}
+{'id': 373189, 'data_set_name': '可以使用:industry', 'description': '不可使用,仅供参考:Industry grouping'}
+{'id': 139064, 'data_set_name': '可以使用:short_term_contract_expenses', 'description': '不可使用,仅供参考:Expenses related to short-term contracts.'}
+{'id': 140608, 'data_set_name': '可以使用:fnd72_pit_or_is_a_earn_for_common', 'description': '不可使用,仅供参考:Net Income Available To Common Shareholders'}
+{'id': 140173, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_tw_ebitdaev', 'description': '不可使用,仅供参考:Time Weighted EBITDA/EV. It is defined as the time-weighted EBITDA/Enterprise Value for FY1 and FY2.'}
+{'id': 60958, 'data_set_name': '可以使用:anl14_low_eps_fy4', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 4 years'}
+{'id': 140504, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'}
+{'id': 61205, 'data_set_name': '可以使用:anl14_median_capex_fp4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 quarters'}
+{'id': 60369, 'data_set_name': '可以使用:anl10_ebspast_det_anntime_6209', 'description': '不可使用,仅供参考:Announce time for earnings before others estimates'}
+{'id': 60960, 'data_set_name': '可以使用:anl14_low_epsrep_fp1', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Earnings Per Share - Upcoming Quarter'}
+{'id': 136696, 'data_set_name': '可以使用:fnd17_alldelay1_aprice2bk', 'description': '不可使用,仅供参考:Price to Book - most recent fiscal year'}
+{'id': 60973, 'data_set_name': '可以使用:anl14_low_ndebt_fp1', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - upcoming quarter'}
+{'id': 373553, 'data_set_name': '可以使用:pv37_cap_global2h', 'description': '不可使用,仅供参考:Global Market Capitalization 2H.'}
+{'id': 136804, 'data_set_name': '可以使用:fnd17_alldelay1_pelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items, Low'}
+{'id': 61511, 'data_set_name': '可以使用:anl14_stddev_capex_fy5', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 5 years'}
+{'id': 137115, 'data_set_name': '可以使用:fnd17_priceavg200day', 'description': '不可使用,仅供参考:Average price of the last 200 days'}
+{'id': 61933, 'data_set_name': '可以使用:anl15_s_ltg_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a long-term growth price estimation.'}
+{'id': 140451, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_percent_of_foreign_ownership', 'description': '不可使用,仅供参考:Percentage of shares outstanding owned by foreign investors as of the period end date'}
+{'id': 62614, 'data_set_name': '可以使用:anl69_eps_most_recent_period_end_dt', 'description': '不可使用,仅供参考:Most Recent Period End Date'}
+{'id': 60942, 'data_set_name': '可以使用:anl14_low_ebitda_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 quarters'}
+{'id': 60893, 'data_set_name': '可以使用:anl14_low_bvps_fp2', 'description': '不可使用,仅供参考:The lowest estimation of book value per share - upcoming 2 quarters'}
+{'id': 373389, 'data_set_name': '可以使用:industry_grouping_level5_top1500', 'description': '不可使用,仅供参考:Industry classification at level 5 for the top 1500 instruments.'}
+{'id': 62224, 'data_set_name': '可以使用:ttm_price_to_free_cash_flow_per_share_2', 'description': '不可使用,仅供参考:Ratio of price to free cash flow per share over the trailing twelve months.'}
+{'id': 373377, 'data_set_name': '可以使用:industry_grouping_level10_top1500', 'description': '不可使用,仅供参考:Industry classification at level 10 for the top 1500 instruments.'}
+{'id': 139091, 'data_set_name': '可以使用:short_term_net_cash_inflow', 'description': '不可使用,仅供参考:non-cash items.'}
+{'id': 373428, 'data_set_name': '可以使用:resampled_pca_factor_11', 'description': '不可使用,仅供参考:Eleventh principal component factor derived from robust resampling of returns for top 2000 securities.'}
+{'id': 139074, 'data_set_name': '可以使用:short_term_group_payables', 'description': '不可使用,仅供参考:Short-term payables to group companies.'}
+{'id': 140175, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_y5speq4rqsr', 'description': '不可使用,仅供参考:R Sqr of 5-yr TTM EPS Trend Line. It is defined as the slope coefficient between monthly dates and the corresponding trailing 12-month earnings per share before extra items in the prior 20 quarters.'}
+{'id': 62024, 'data_set_name': '可以使用:quarterly_current_ratio_alt', 'description': '不可使用,仅供参考:Alternate calculation of current ratio for the most recent quarter.'}
+{'id': 140508, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_other_cur_asset', 'description': '不可使用,仅供参考:Other Current Assets'}
+{'id': 137113, 'data_set_name': '可以使用:fnd17_price2bk2', 'description': '不可使用,仅供参考:Price to Book - most recent quarter, 1 year ago'}
+{'id': 137179, 'data_set_name': '可以使用:fnd17_qwcappspr', 'description': '不可使用,仅供参考:Working Capital per share/Price - most recent quarter'}
+{'id': 139057, 'data_set_name': '可以使用:short_term_bank_debt_amount', 'description': '不可使用,仅供参考:Amount of short-term bank debt outstanding.'}
+{'id': 62055, 'data_set_name': '可以使用:quarterly_free_cash_flow_per_share_2', 'description': '不可使用,仅供参考:Free cash flow per share for the most recent quarter.'}
+{'id': 61210, 'data_set_name': '可以使用:anl14_median_capex_fy4', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 4 years'}
+{'id': 60933, 'data_set_name': '可以使用:anl14_low_ebit_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest & Taxes - Upcoming 4 Quarters'}
+{'id': 62152, 'data_set_name': '可以使用:quarterly_research_and_development_expense_3', 'description': '不可使用,仅供参考:Total research and development expense for the most recent quarter.'}
+{'id': 137494, 'data_set_name': '可以使用:current_maturities_short_term', 'description': '不可使用,仅供参考:Current maturities of short-term debt obligations.'}
+{'id': 139077, 'data_set_name': '可以使用:short_term_investment_cash_flow', 'description': '不可使用,仅供参考:Other Investing Cash Flow Items, Total represents all items reported within the investing activities in the cash flow statement, other than capital expenditures.'}
+{'id': 62425, 'data_set_name': '可以使用:security_trading_currency_3', 'description': '不可使用,仅供参考:Currency in which the security is traded.'}
+{'id': 136446, 'data_set_name': '可以使用:security_market_price_ltip', 'description': '不可使用,仅供参考:Price of the stock at the relevant report date for LTIP compensation.'}
+{'id': 62093, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_ratio_prior_year', 'description': '不可使用,仅供参考:Long-term debt divided by equity for the same quarter last year.'}
+{'id': 60639, 'data_set_name': '可以使用:anl11_g1reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G1 100% being the best'}
+{'id': 139048, 'data_set_name': '可以使用:share_premium_reserve', 'description': '不可使用,仅供参考:Preferred Stock – Non-Redeemable, Total.'}
+{'id': 373371, 'data_set_name': '可以使用:pv173_zscorestilt5y1ydiff60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of the difference of factor loading of the second principal component for the 5-year z-spread and 1-year z-spread'}
+{'id': 60988, 'data_set_name': '可以使用:anl14_low_ntp_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Profit - Upcoming 2 Years'}
+{'id': 60975, 'data_set_name': '可以使用:anl14_low_ndebt_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 3 Quarters'}
+{'id': 139084, 'data_set_name': '可以使用:short_term_loans_debt', 'description': '不可使用,仅供参考:Short-term loans and debt reported for the interim period.'}
+{'id': 138952, 'data_set_name': '可以使用:long_term_trade_debt_2', 'description': '不可使用,仅供参考:Long-term trade debt outstanding at period end.'}
+{'id': 373344, 'data_set_name': '可以使用:pv173_rawratiostilt5y1ydiff60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of the difference of factor loading of the second principal component for the 5-year z-spread and 1-year z-spread'}
+{'id': 62016, 'data_set_name': '可以使用:quarterly_current_assets_2', 'description': '不可使用,仅供参考:Current assets for the most recent quarter.'}
+{'id': 373535, 'data_set_name': '可以使用:top2000_factor2_group50_score', 'description': '不可使用,仅供参考:Second factor score for top 2000 securities, grouped into 50 clusters.'}
+{'id': 61022, 'data_set_name': '可以使用:anl14_low_revenue_fp4', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 4 quarters'}
+{'id': 373390, 'data_set_name': '可以使用:industry_grouping_level5_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 5 for the top 2000 instruments.'}
+{'id': 140800, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_capital_stock', 'description': '不可使用,仅供参考:Capital Stock: When a company issues shares with par value for cash, a part of the proceeds are recorded as capital stock'}
+{'id': 62126, 'data_set_name': '可以使用:quarterly_pe_ratio_low', 'description': '不可使用,仅供参考:Lowest price-to-earnings ratio for the most recent quarter.'}
+{'id': 140124, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_mpgghcy3', 'description': '不可使用,仅供参考:3-Year Change in Gross Profit Margin. It is defined as the 3-Year on Year Change in Gross Profit Margin.'}
+{'id': 60394, 'data_set_name': '可以使用:anl10_entpast_det_estvalue_6307', 'description': '不可使用,仅供参考:Estimate value for earnings net taxes'}
+{'id': 60980, 'data_set_name': '可以使用:anl14_low_ndebt_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 4 Years'}
+{'id': 60574, 'data_set_name': '可以使用:anl11_e2reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on E2 1 being the highest rank'}
+{'id': 140820, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_lt_liabilities', 'description': '不可使用,仅供参考:Other LT Liabilities'}
+{'id': 140559, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_incr_cap_stock', 'description': '不可使用,仅供参考:Increase in Capital Stocks'}
+{'id': 373407, 'data_set_name': '可以使用:pca_industry_grouping_method3_10', 'description': '不可使用,仅供参考:Industry grouping using third method and 10 clusters for top 3000 equities.'}
+{'id': 140634, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_fair_value_plan_assets', 'description': '不可使用,仅供参考:Fair market value of the pension plan assets at the end of the period'}
+{'id': 373199, 'data_set_name': '可以使用:subindustry', 'description': '不可使用,仅供参考:Subindustry grouping'}
+{'id': 373368, 'data_set_name': '可以使用:pv173_zscoresshift60dlast', 'description': '不可使用,仅供参考:It is defined as the average factor loading of the first principal component for the z-spread between 6 months and 40 years'}
+{'id': 60911, 'data_set_name': '可以使用:anl14_low_capex_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 5 years'}
+{'id': 139062, 'data_set_name': '可以使用:short_term_cash_market_securities', 'description': '不可使用,仅供参考:Short-term cash and marketable securities held.'}
+{'id': 139081, 'data_set_name': '可以使用:short_term_investments_7', 'description': '不可使用,仅供参考:Short-term investments reported in interim financials.'}
+{'id': 139054, 'data_set_name': '可以使用:short_term_bank_acceptances_issued', 'description': '不可使用,仅供参考:Short-term bank acceptances issued during the period.'}
+{'id': 62002, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_nonannualized_prior', 'description': '不可使用,仅供参考:Cash flow per share for the quarter, not annualized, for the same quarter last year.'}
+{'id': 373387, 'data_set_name': '可以使用:industry_grouping_level50_top2000', 'description': '不可使用,仅供参考:Industry classification group at level 50 for the top 2000 instruments.'}
+{'id': 62088, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity', 'description': '不可使用,仅供参考:Ratio of long-term debt to shareholder equity for the quarter.'}
+{'id': 139151, 'data_set_name': '可以使用:valuation_investment_assets_total', 'description': '不可使用,仅供参考:Total valuation allowance for investment assets.'}
+{'id': 62122, 'data_set_name': '可以使用:quarterly_payout_ratio', 'description': '不可使用,仅供参考:Percentage of earnings paid as dividends for the quarter.'}
+{'id': 136386, 'data_set_name': '可以使用:equity_linked_compensation_ratio_2', 'description': '不可使用,仅供参考:Proportion of equity-linked compensation in total director remuneration (alternate).'}
+{'id': 61851, 'data_set_name': '可以使用:anl15_s_12_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with a 12-month forward mean price estimation.'}
+{'id': 61979, 'data_set_name': '可以使用:quarterly_asset_turnover', 'description': '不可使用,仅供参考:Annualized ratio of sales to average assets for the quarter.'}
+{'id': 60977, 'data_set_name': '可以使用:anl14_low_ndebt_fy1', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming year'}
+{'id': 140582, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_cash_paid_for_tax', 'description': '不可使用,仅供参考:Cash Paid for Taxes'}
+{'id': 61943, 'data_set_name': '可以使用:anl39_cursharesoutstanding', 'description': '不可使用,仅供参考:Current Shares Outstanding'}
+{'id': 139109, 'data_set_name': '可以使用:short_term_securities_investments_2', 'description': '不可使用,仅供参考:Short-term investments in securities at period end.'}
+{'id': 60541, 'data_set_name': '可以使用:anl11_citreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on CIT Score with 1 being the highest rank'}
+{'id': 139107, 'data_set_name': '可以使用:short_term_securities_gain_adjustment', 'description': '不可使用,仅供参考:Adjustment for gains on short-term securities.'}
+{'id': 138882, 'data_set_name': '可以使用:free_firm_cash_flow', 'description': '不可使用,仅供参考:other financing cash flow'}
+{'id': 137352, 'data_set_name': '可以使用:quarterly_debt_to_equity_ratio_2', 'description': '不可使用,仅供参考:Ratio of total debt to total equity for the most recent quarter.'}
+{'id': 62090, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_prior', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the same quarter last year.'}
+{'id': 60367, 'data_set_name': '可以使用:anl10_dpspast_det_indicator_6220', 'description': '不可使用,仅供参考:Indicator for fiscal periods in ASCII values (54=fq1, 55=fq2, 49=fy1, 50=fy2) for dividends per share estimates'}
+{'id': 138985, 'data_set_name': '可以使用:other_long_term_obligations_2', 'description': '不可使用,仅供参考:Other long-term obligations not classified elsewhere.'}
+{'id': 60355, 'data_set_name': '可以使用:anl10_cpspast_det_excflag_6356', 'description': '不可使用,仅供参考:Exclusion flag for cash per share estimates'}
+{'id': 136536, 'data_set_name': '可以使用:annual_normalized_earnings_before_tax', 'description': '不可使用,仅供参考:Earnings before taxes for the most recent fiscal year, adjusted to exclude unusual or one-time items.'}
+{'id': 373346, 'data_set_name': '可以使用:pv173_rawratiostilt60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of average factor loading of the second principal component for the z-spread between 6 months and 40 years'}
+{'id': 61996, 'data_set_name': '可以使用:quarterly_cash_flow_per_share_3', 'description': '不可使用,仅供参考:Cash flow divided by average shares for the most recent quarter.'}
+{'id': 61027, 'data_set_name': '可以使用:anl14_low_revenue_fy4', 'description': '不可使用,仅供参考:The Lowest Estimation of Revenue - upcoming 4 years'}
+{'id': 140578, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_cap_expend_prpty_add', 'description': '不可使用,仅供参考:Capital Expenditures/Property Additions'}
+{'id': 140545, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_act_cash_paid_for_int_debt', 'description': '不可使用,仅供参考:Cash Paid for Interest'}
+{'id': 138922, 'data_set_name': '可以使用:long_term_bond_debt', 'description': '不可使用,仅供参考:Long-term bond debt reported in interim financials.'}
+{'id': 61643, 'data_set_name': '可以使用:rtk_ptg_low', 'description': '不可使用,仅供参考:The Lowest of Price Target'}
+{'id': 140906, 'data_set_name': '可以使用:fnd72_s_pit_or_is_q_is_sh_for_diluted_eps', 'description': '不可使用,仅供参考:Weighted average number of shares used to calculate Diluted EPS'}
+{'id': 373588, 'data_set_name': '可以使用:pv37_low_global30m', 'description': '不可使用,仅供参考:Global Lowest Price 30M'}
+{'id': 373519, 'data_set_name': '可以使用:top1500_pca_factor1_grouping50', 'description': '不可使用,仅供参考:First principal component grouping for top 1500 securities with 50 clusters.'}
+{'id': 136384, 'data_set_name': '可以使用:director_attrition_ratio_3yr', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding three years.'}
+{'id': 136383, 'data_set_name': '可以使用:director_attrition_ratio_2', 'description': '不可使用,仅供参考:Proportion of directors who have left a role in the preceding period (alternate).'}
+{'id': 60999, 'data_set_name': '可以使用:anl14_low_ntprep_fy3', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 3 years'}
+{'id': 139070, 'data_set_name': '可以使用:short_term_debt_ending_balance', 'description': '不可使用,仅供参考:Ending balance of short-term debt for the period.'}
+{'id': 137041, 'data_set_name': '可以使用:fnd17_debtcap_a', 'description': '不可使用,仅供参考:Debt divided by capitalization, LFY'}
+{'id': 138941, 'data_set_name': '可以使用:long_term_loans_liabilities_2', 'description': '不可使用,仅供参考:Long-term loans and related liabilities outstanding.'}
+{'id': 140541, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_lt_capital_lease_obligations', 'description': '不可使用,仅供参考:Noncurrent amount payable by a lessee under capital leases'}
+{'id': 60756, 'data_set_name': '可以使用:anl14_high_capex_fp5', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - upcoming 5 quarters'}
+{'id': 60981, 'data_set_name': '可以使用:anl14_low_ndebt_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Debt - Upcoming 5 Years'}
+{'id': 60990, 'data_set_name': '可以使用:anl14_low_ntp_fy4', 'description': '不可使用,仅供参考:The lowest estimation of net profit - upcoming 4 years'}
+{'id': 140746, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_other_asset_def_chng_other', 'description': '不可使用,仅供参考:Other Assets/Def Chg'}
+{'id': 62195, 'data_set_name': '可以使用:quarterly_total_debt_to_assets_ratio', 'description': '不可使用,仅供参考:Total debt divided by total assets for the most recent quarter.'}
+{'id': 61507, 'data_set_name': '可以使用:anl14_stddev_capex_fy1', 'description': '不可使用,仅供参考:Standard deviation of estimations of capital expenditures - upcoming year'}
+{'id': 138942, 'data_set_name': '可以使用:long_term_loans_total_debt', 'description': '不可使用,仅供参考:Total long-term loans and debt reported for the interim period.'}
+{'id': 62213, 'data_set_name': '可以使用:quarterly_working_capital_per_share_to_price_db', 'description': '不可使用,仅供参考:Working capital per share divided by price for the quarter (alternate source).'}
+{'id': 140548, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cash_from_inv_act', 'description': '不可使用,仅供参考:Cash From Investing Activities'}
+{'id': 138975, 'data_set_name': '可以使用:other_assets_current', 'description': '不可使用,仅供参考:Other current assets not classified elsewhere.'}
+{'id': 140866, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_other_inv_act', 'description': '不可使用,仅供参考:Other Investing Activities'}
+{'id': 61741, 'data_set_name': '可以使用:anl15_gr_ltg_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a long-term growth price estimation.'}
+{'id': 140188, 'data_set_name': '可以使用:fnd44_cashflow_accruals', 'description': '不可使用,仅供参考:Cash Flow Accruals'}
+{'id': 138988, 'data_set_name': '可以使用:other_long_term_obligations_7', 'description': '不可使用,仅供参考:includes diverse cash flows that are reported outside of the operating, investing and financing sections of the cash flow statement.'}
+{'id': 60976, 'data_set_name': '可以使用:anl14_low_ndebt_fp4', 'description': '不可使用,仅供参考:The lowest estimation of net debt - upcoming 4 quarters'}
+{'id': 139061, 'data_set_name': '可以使用:short_term_cash_investment_proceeds', 'description': '不可使用,仅供参考:cash interest paid represents interest paid in cash during the period Cash Interest.'}
+{'id': 137414, 'data_set_name': '可以使用:twelve_month_high_price_2', 'description': '不可使用,仅供参考:The highest price at which the security traded in the last twelve months.'}
+{'id': 139016, 'data_set_name': '可以使用:quarterly_total_share_capital', 'description': '不可使用,仅供参考:Total share capital at the end of the quarter.'}
+{'id': 137023, 'data_set_name': '可以使用:fnd17_atotd2cap', 'description': '不可使用,仅供参考:Total debt/total capital - most recent fiscal year'}
+{'id': 60906, 'data_set_name': '可以使用:anl14_low_capex_fp5', 'description': '不可使用,仅供参考:The lowest estimation of Capital Expenditures - upcoming 5 quarters'}
+{'id': 140506, 'data_set_name': '可以使用:fnd72_pit_or_bs_q_bs_options_outstanding', 'description': '不可使用,仅供参考:Options Outstanding End of Period'}
+{'id': 373373, 'data_set_name': '可以使用:pv173_zscorestilt60dema60d', 'description': '不可使用,仅供参考:It is defined as the 60-day exponential average of average factor loading of the second principal component for the z-spread between 6 months and 40 years'}
+{'id': 136691, 'data_set_name': '可以使用:fnd17_alldelay1_apayratio2', 'description': '不可使用,仅供参考:Payout ratio - most recent fiscal year -1'}
+{'id': 62702, 'data_set_name': '可以使用:anl69_roe_most_recent_period_end_dt', 'description': '不可使用,仅供参考:Most Recent Period End Date'}
+{'id': 136726, 'data_set_name': '可以使用:fnd17_alldelay1_atotd2cap', 'description': '不可使用,仅供参考:Total debt/total capital - most recent fiscal year'}
+{'id': 140744, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'}
+{'id': 139020, 'data_set_name': '可以使用:quick_assets_total', 'description': '不可使用,仅供参考:Total value of quick assets, typically cash and equivalents.'}
+{'id': 140632, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_expected_return_plan_assets', 'description': '不可使用,仅供参考:The estimated expected long-term rate of return on pension plan assets expressed as a percent'}
+{'id': 60991, 'data_set_name': '可以使用:anl14_low_ntp_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Net Profit - upcoming 5 years'}
+{'id': 140438, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_mkt_sec_other_st_invest', 'description': '不可使用,仅供参考:Marketable Securities & Other ST Investments'}
+{'id': 373350, 'data_set_name': '可以使用:pv173_zscoresatlas_unit_name', 'description': '不可使用,仅供参考:Atlas unit name'}
+{'id': 60628, 'data_set_name': '可以使用:anl11_esg_ttcrg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on ESG Hybrid Score with 100% being the best'}
+{'id': 62147, 'data_set_name': '可以使用:quarterly_receivables_turnover_2', 'description': '不可使用,仅供参考:Receivables turnover ratio for the most recent quarter.'}
+{'id': 140166, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pbwt', 'description': '不可使用,仅供参考:Time Weighted Book Yield. It is defined as the time-weighted average of book value per share estimates in fiscal year 1 and fiscal year 2 divided by close price.'}
+{'id': 139120, 'data_set_name': '可以使用:statement_of_cash_flows_2', 'description': '不可使用,仅供参考:Statement of cash flows for the period.'}
+{'id': 373525, 'data_set_name': '可以使用:top1500_pca_factor3_grouping2', 'description': '不可使用,仅供参考:Third principal component grouping for top 1500 securities with 2 clusters.'}
+{'id': 140618, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_avg_num_sh_for_eps', 'description': '不可使用,仅供参考:Average # of Shares for EPS'}
+{'id': 60925, 'data_set_name': '可以使用:anl14_low_div_fy1', 'description': '不可使用,仅供参考:The lowest estimation of dividend - upcoming year'}
+{'id': 373434, 'data_set_name': '可以使用:resampled_pca_factor_4', 'description': '不可使用,仅供参考:Fourth principal component factor derived from robust resampling of returns for top 2000 securities.'}
+{'id': 137543, 'data_set_name': '可以使用:financing_cashflow_net', 'description': '不可使用,仅供参考:Net cash flow from financing activities.'}
+{'id': 138980, 'data_set_name': '可以使用:other_current_liabilities_3', 'description': '不可使用,仅供参考:Other liabilities classified as current.'}
+{'id': 137426, 'data_set_name': '可以使用:accounts_current_assets_equity', 'description': '不可使用,仅供参考:Current assets related to equity accounts.'}
+{'id': 373357, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadchg60dsbst', 'description': '不可使用,仅供参考:It is defined as the 60-day change of 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 138891, 'data_set_name': '可以使用:goodwill_and_intangibles', 'description': '不可使用,仅供参考:Total value of goodwill and intangible assets on the balance sheet.'}
+{'id': 140930, 'data_set_name': '可以使用:fnd86_risk_score', 'description': '不可使用,仅供参考:Risk score'}
+{'id': 60905, 'data_set_name': '可以使用:anl14_low_capex_fp4', 'description': '不可使用,仅供参考:The lowest estimation of capital expenditures - upcoming 4 quarters'}
+{'id': 373549, 'data_set_name': '可以使用:pv37_cap_15', 'description': '不可使用,仅供参考:Market cap'}
+{'id': 140663, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_trading_acct_prof', 'description': '不可使用,仅供参考:Trading Acct'}
+{'id': 138944, 'data_set_name': '可以使用:long_term_obligations_commitments', 'description': '不可使用,仅供参考:[Quarterly] Current Portion of Long-Term Operating Leases, Supplemental'}
+{'id': 140444, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_cur_asset', 'description': '不可使用,仅供参考:Other Current Assets'}
+{'id': 138894, 'data_set_name': '可以使用:income_before_tax_value', 'description': '不可使用,仅供参考:Earnings before provision for income taxes for the period.'}
+{'id': 60947, 'data_set_name': '可以使用:anl14_low_ebitda_fy3', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming 3 years'}
+{'id': 373415, 'data_set_name': '可以使用:pca_industry_grouping_method4_5', 'description': '不可使用,仅供参考:Industry grouping using fourth method and 5 clusters for top 3000 equities.'}
+{'id': 139067, 'data_set_name': '可以使用:short_term_currency_instruments', 'description': '不可使用,仅供参考:Short-term currency instruments held by the company.'}
+{'id': 61023, 'data_set_name': '可以使用:anl14_low_revenue_fp5', 'description': '不可使用,仅供参考:The lowest estimation of revenue - upcoming 5 quarters'}
+{'id': 60370, 'data_set_name': '可以使用:anl10_ebspast_det_estage_6210', 'description': '不可使用,仅供参考:Age of earnings before others estimates in days'}
+{'id': 138974, 'data_set_name': '可以使用:other_assets_cash_receipts', 'description': '不可使用,仅供参考:represents changes in accounts receivable during the period. An increase in accounts receivable has a negative impact on cash flow from operating activities, and vice versa.'}
+{'id': 140480, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_st_capital_lease_obligations', 'description': '不可使用,仅供参考:Amount payable within 1 year by a lessee under capital leases'}
+{'id': 140648, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_other_adj_comp_inc', 'description': '不可使用,仅供参考:Other Adjustments - Other Comprehensive Income'}
+{'id': 136532, 'data_set_name': '可以使用:annual_earnings_before_tax', 'description': '不可使用,仅供参考:Earnings before taxes for the most recent fiscal year.'}
+{'id': 139098, 'data_set_name': '可以使用:short_term_notes_issued_net', 'description': '不可使用,仅供参考:Net amount of short-term notes issued during the period.'}
+{'id': 61020, 'data_set_name': '可以使用:anl14_low_revenue_fp2', 'description': '不可使用,仅供参考:The lowest estimation of Revenue - upcoming 2 quarters'}
+{'id': 60350, 'data_set_name': '可以使用:anl10_cpspast_det_analyst_6350', 'description': '不可使用,仅供参考:Analyst ID for cash per share estimates'}
+{'id': 139093, 'data_set_name': '可以使用:short_term_non_current_investments', 'description': '不可使用,仅供参考:Short-term investments that are classified as non-current.'}
+{'id': 373388, 'data_set_name': '可以使用:industry_grouping_level50_top3000_2', 'description': '不可使用,仅供参考:Industry classification group at level 50 for the top 3000 instruments.'}
+{'id': 60939, 'data_set_name': '可以使用:anl14_low_ebit_fy5', 'description': '不可使用,仅供参考:The Lowest Estimation of Earnings Before Interest & Taxes - Upcoming 5 Years'}
+{'id': 138916, 'data_set_name': '可以使用:investment_securities_financial_assets', 'description': '不可使用,仅供参考:Sale of assets to be leased out under operating lease contracts.'}
+{'id': 373335, 'data_set_name': '可以使用:pv173_rawratiosmt5yzspreadsbst', 'description': '不可使用,仅供参考:It is defined as the region relative 5-year mid z-spreadIn the bond z-spread curve'}
+{'id': 140117, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_apg', 'description': '不可使用,仅供参考:TTM Gross Profit to Assets. It is defined as the TTM (Sales - COGS)/Total Assets.'}
+{'id': 137550, 'data_set_name': '可以使用:fixed_long_term_debt_repayment', 'description': '不可使用,仅供参考:Repayment amount of fixed long-term debt during the period.'}
+{'id': 140167, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_pcdwf', 'description': '不可使用,仅供参考:Leading 12 Month Cash Yield. It is defined as the average quarterly forward-looking cash flows per share divided by close price.'}
+{'id': 60998, 'data_set_name': '可以使用:anl14_low_ntprep_fy2', 'description': '不可使用,仅供参考:The lowest estimation of reported net profit - upcoming 2 years'}
+{'id': 61690, 'data_set_name': '可以使用:anl15_gr_cal_fy1_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a calendarized 1 fiscal year mean price estimation.'}
+{'id': 139100, 'data_set_name': '可以使用:short_term_other_current_assets_2', 'description': '不可使用,仅供参考:Short-term other current assets reported in interim financials.'}
+{'id': 373646, 'data_set_name': '可以使用:pv72_ibesptg2_score_float', 'description': '不可使用,仅供参考:pv_score'}
+{'id': 62085, 'data_set_name': '可以使用:quarterly_long_term_debt_to_assets_2', 'description': '不可使用,仅供参考:Long-term debt divided by total assets for the quarter.'}
+{'id': 140846, 'data_set_name': '可以使用:fnd72_s_pit_or_cf_q_cf_act_cash_paid_for_int_debt', 'description': '不可使用,仅供参考:Cash Paid for Interest'}
+{'id': 373360, 'data_set_name': '可以使用:pv173_zscoresmt5yzspreadindrel', 'description': '不可使用,仅供参考:It is defined as theIndustry relative 5-year mid z-spreadIn the bond z-spread curve.'}
+{'id': 61006, 'data_set_name': '可以使用:anl14_low_ptp_fp1', 'description': '不可使用,仅供参考:The Lowest Estimation of Pretax Profit - upcoming quarter'}
+{'id': 62134, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'}
+{'id': 140171, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_rev6fy2', 'description': "不可使用,仅供参考:Averaged Last 6-M EPS Revisions for FY2. It is defined as the average of prior 6-month monthly changes in a stock's consensus analysts earnings forecasts for fiscal year 2, scaled by previous month-end trading price."}
+{'id': 60755, 'data_set_name': '可以使用:anl14_high_capex_fp4', 'description': '不可使用,仅供参考:The Highest Estimation of Capital Expenditures - Upcoming 4 Quarters'}
+{'id': 140137, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_y5speq4vc', 'description': '不可使用,仅供参考:Stability of 5-yr TTM Earnings per Share. It is defined as the standard deviation of the last 20-Month trailing 12-month earnings per share (EPS) divided by the mean of these EPSs.'}
+{'id': 138987, 'data_set_name': '可以使用:other_long_term_obligations_6', 'description': '不可使用,仅供参考:Other obligations due beyond one year not classified elsewhere.'}
+{'id': 139126, 'data_set_name': '可以使用:total_assets_4', 'description': '不可使用,仅供参考:Sum of all assets owned by the company.'}
+{'id': 60904, 'data_set_name': '可以使用:anl14_low_capex_fp3', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 3 quarters'}
+{'id': 61974, 'data_set_name': '可以使用:price_to_cash_flow_per_share_quarterly', 'description': '不可使用,仅供参考:Ratio of share price to cash flow per share for the most recent quarter.'}
+{'id': 140630, 'data_set_name': '可以使用:fnd72_pit_or_is_a_is_eqy_earn_from_invest_assoc', 'description': '不可使用,仅供参考:Earnings from investments in associated companies, joint ventures, unconsolidated subsidiaries that are valued according to the equity method'}
+{'id': 60436, 'data_set_name': '可以使用:anl10_fcfpast_det_indicator_6377', 'description': '不可使用,仅供参考:Indicator for fiscal periods in ASCII values (54=fq1, 55=fq2, 49=fy1, 50=fy2) for free cash flow estimates'}
+{'id': 138935, 'data_set_name': '可以使用:long_term_debt_value', 'description': '不可使用,仅供参考:Total amount of long-term debt obligations for the period.'}
+{'id': 62187, 'data_set_name': '可以使用:quarterly_total_assets_change_prior_year', 'description': '不可使用,仅供参考:Percent change in total assets from the same quarter last year.'}
+{'id': 140602, 'data_set_name': '可以使用:fnd72_pit_or_cf_q_cf_other_non_cash_adjust', 'description': '不可使用,仅供参考:Other Non-Cash Adjustments'}
+{'id': 138972, 'data_set_name': '可以使用:other_amortization_items', 'description': '不可使用,仅供参考:Other amortization items reported for the annual period.'}
+{'id': 140771, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_2_bs_capital_stock', 'description': '不可使用,仅供参考:Capital Stock: When a company issues shares with par value for cash, a part of the proceeds are recorded as capital stock'}
+{'id': 60656, 'data_set_name': '可以使用:anl11_g_ttcrg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G Hybrid Score with 100% being the best'}
+{'id': 61032, 'data_set_name': '可以使用:anl14_low_roa_fy4', 'description': '不可使用,仅供参考:The lowest estimation of Returns on Assets - upcoming 4 years'}
+{'id': 62358, 'data_set_name': '可以使用:forecast_currency_netprofit', 'description': '不可使用,仅供参考:Currency in which the net profit forecast is denominated.'}
+{'id': 60643, 'data_set_name': '可以使用:anl11_g2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on G2 100% being the best'}
+{'id': 61360, 'data_set_name': '可以使用:anl14_numofests_capex_fy4', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - Upcoming 4 Years'}
+{'id': 62136, 'data_set_name': '可以使用:quarterly_price_to_sales_ratio_3', 'description': '不可使用,仅供参考:Price divided by sales per share for the quarter.'}
+{'id': 138943, 'data_set_name': '可以使用:long_term_minority_interest', 'description': '不可使用,仅供参考:Long-term minority interest in consolidated subsidiaries.'}
+{'id': 60945, 'data_set_name': '可以使用:anl14_low_ebitda_fy1', 'description': '不可使用,仅供参考:The lowest estimation of Earnings Before Interest, Taxes, Depreciation & Amortization - upcoming year'}
+{'id': 373555, 'data_set_name': '可以使用:pv37_cap_global3h', 'description': '不可使用,仅供参考:Global Market Capitalization 3H'}
+{'id': 373516, 'data_set_name': '可以使用:top1500_pca_factor1_grouping2', 'description': '不可使用,仅供参考:First principal component grouping for top 1500 securities with 2 clusters.'}
+{'id': 140819, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_other_cur_liab', 'description': '不可使用,仅供参考:Includes customer deposits, short-term deferred income tax liabilities, refunds due customers, and other current liabilities'}
+{'id': 61004, 'data_set_name': '可以使用:anl14_low_opp_fy2', 'description': '不可使用,仅供参考:The Lowest Estimation of Operating Profit - upcoming 2 years'}
+{'id': 60752, 'data_set_name': '可以使用:anl14_high_capex_fp1', 'description': '不可使用,仅供参考:The highest estimation of capital expenditures - upcoming quarter'}
+{'id': 61009, 'data_set_name': '可以使用:anl14_low_ptp_fy1', 'description': '不可使用,仅供参考:The lowest estimation of pretax profit - upcoming year'}
+{'id': 62089, 'data_set_name': '可以使用:quarterly_long_term_debt_to_equity_2', 'description': '不可使用,仅供参考:Long-term debt divided by shareholder equity for the quarter.'}
+{'id': 61503, 'data_set_name': '可以使用:anl14_stddev_capex_fp2', 'description': '不可使用,仅供参考:Standard Deviation of Estimations of Capital Expenditures - upcoming 2 quarters'}
+{'id': 140753, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_1_bs_sh_cap_and_apic', 'description': '不可使用,仅供参考:Share Capital & APIC'}
+{'id': 140547, 'data_set_name': '可以使用:fnd72_pit_or_cf_a_cf_cash_from_fnc_act', 'description': '不可使用,仅供参考:Cash from Financing Activities'}
+{'id': 136811, 'data_set_name': '可以使用:fnd17_alldelay1_priceavg150day', 'description': '不可使用,仅供参考:Average price of the last 150 days'}
+{'id': 139137, 'data_set_name': '可以使用:trade_payables_short_term', 'description': '不可使用,仅供参考:Short-term trade payables owed by the company.'}
+{'id': 62075, 'data_set_name': '可以使用:quarterly_inventory_turnover_2', 'description': '不可使用,仅供参考:Annualized cost of goods sold divided by average inventory for the quarter.'}
+{'id': 60953, 'data_set_name': '可以使用:anl14_low_eps_fp4', 'description': '不可使用,仅供参考:The lowest estimation of earnings per share - upcoming 4 quarters'}
+{'id': 138934, 'data_set_name': '可以使用:long_term_debt_total_2', 'description': '不可使用,仅供参考:Total long-term debt outstanding.'}
+{'id': 60472, 'data_set_name': '可以使用:anl10_grmpast_det_estage_6320', 'description': '不可使用,仅供参考:Age of gross margin estimates in days'}
+{'id': 138978, 'data_set_name': '可以使用:other_borrowings_total', 'description': '不可使用,仅供参考:Total other borrowings reported for the annual period.'}
+{'id': 62392, 'data_set_name': '可以使用:anl45_net_market_exposure', 'description': '不可使用,仅供参考:Difference in fractional total value between long and short ideas'}
+{'id': 138982, 'data_set_name': '可以使用:other_interest_liabilities', 'description': '不可使用,仅供参考:Other interest-bearing liabilities reported for the annual period.'}
+{'id': 61059, 'data_set_name': '可以使用:anl14_mean_capex_fy3', 'description': '不可使用,仅供参考:Mean of Estimations of Capital Expenditures - upcoming 3 years'}
+{'id': 373556, 'data_set_name': '可以使用:pv37_cap_global4h', 'description': '不可使用,仅供参考:Global Market Capitalization 4H'}
+{'id': 62156, 'data_set_name': '可以使用:quarterly_return_on_average_assets', 'description': '不可使用,仅供参考:Annualized net income divided by average assets for the quarter.'}
+{'id': 60485, 'data_set_name': '可以使用:anl10_roapast_det_estvalue_6335', 'description': '不可使用,仅供参考:Estimate value for return on assets'}
+{'id': 62410, 'data_set_name': '可以使用:anl45_risk_free_rate', 'description': '不可使用,仅供参考:The unrealised return on an open idea'}
+{'id': 140119, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_apgghcy3', 'description': '不可使用,仅供参考:3-Year Change in Gross Profit to Assets. It is defined as the 3-Year on Year Change in Gross Profit to Assets.'}
+{'id': 136845, 'data_set_name': '可以使用:fnd17_alldelay1_qpelow', 'description': '不可使用,仅供参考:P/E excluding extraordinary items low, most recent quarter'}
+{'id': 138937, 'data_set_name': '可以使用:long_term_liabilities_total_3', 'description': '不可使用,仅供参考:Total long-term liabilities outstanding at period end.'}
+{'id': 60757, 'data_set_name': '可以使用:anl14_high_capex_fy1', 'description': '不可使用,仅供参考:The highest estimation of Capital Expenditures - upcoming year'}
+{'id': 140796, 'data_set_name': '可以使用:fnd72_s_pit_or_bs_q_bs_add_paid_in_cap', 'description': '不可使用,仅供参考:Additional Paid in Capital'}
+{'id': 140169, 'data_set_name': '可以使用:fnd31_qsg5additionalfactor3_monthly_ps_wt', 'description': '不可使用,仅供参考:Time Weighted Sales Yield. It is defined as the time-weighted sales per share for FY1 and FY2 divided by its price.'}
+{'id': 139117, 'data_set_name': '可以使用:special_premium_trust_monetary', 'description': '不可使用,仅供参考:Special premium trust monetary value for the interim period.'}
+{'id': 373402, 'data_set_name': '可以使用:pca_industry_grouping_method2_10', 'description': '不可使用,仅供参考:Industry grouping using second method and 10 clusters for top 3000 equities.'}
+{'id': 137473, 'data_set_name': '可以使用:assets_total_2', 'description': '不可使用,仅供参考:Total assets held by the company.'}
+{'id': 140125, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_numrevy2', 'description': '不可使用,仅供参考:Net # of Revisions for Fiscal Year 2. It is defined as the weighted average of the number of FY2 analyst earnings forecasts raised less the number lowered within a month, divided by the total number of analyst forecasts.'}
+{'id': 60662, 'data_set_name': '可以使用:anl11_gposcorreg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on G Max Correlation Score with 1 being the highest rank'}
+{'id': 61356, 'data_set_name': '可以使用:anl14_numofests_capex_fp5', 'description': '不可使用,仅供参考:Num of Estimations of Capital Expenditures - Upcoming 5 Quarters'}
+{'id': 138996, 'data_set_name': '可以使用:other_operating_assets', 'description': '不可使用,仅供参考:Other operating assets reported for the annual period.'}
+{'id': 61701, 'data_set_name': '可以使用:anl15_gr_cal_fy2_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS group grouping with a calendarized 2 fiscal year mean price estimation.'}
+{'id': 138997, 'data_set_name': '可以使用:other_operating_cash_flows', 'description': '不可使用,仅供参考:Other operating cash flows reported in interim financials.'}
+{'id': 140447, 'data_set_name': '可以使用:fnd72_pit_or_bs_a_bs_other_lt_liabilities', 'description': '不可使用,仅供参考:Other LT Liabilities'}
+{'id': 61208, 'data_set_name': '可以使用:anl14_median_capex_fy2', 'description': '不可使用,仅供参考:Median of Estimations of Capital Expenditures - upcoming 2 years'}
+{'id': 136813, 'data_set_name': '可以使用:fnd17_alldelay1_priceavg50day', 'description': '不可使用,仅供参考:Average price of the last 50 days'}
+{'id': 60390, 'data_set_name': '可以使用:anl10_entpast_det_analyst_6309', 'description': '不可使用,仅供参考:Analyst ID for earnings net taxes estimates'}
+{'id': 140680, 'data_set_name': '可以使用:fnd72_pit_or_is_q_earn_for_common', 'description': '不可使用,仅供参考:Net Income Available To Common Shareholders'}
+{'id': 139108, 'data_set_name': '可以使用:short_term_securities_investments', 'description': '不可使用,仅供参考:Short-term securities investments reported for the interim period.'}
+{'id': 138931, 'data_set_name': '可以使用:long_term_debt', 'description': '不可使用,仅供参考:Total value of long-term debt obligations outstanding.'}
+{'id': 139122, 'data_set_name': '可以使用:stockholders_other_comprehensive_assets', 'description': '不可使用,仅供参考:Other comprehensive assets attributable to stockholders.'}
+{'id': 60476, 'data_set_name': '可以使用:anl10_grmpast_det_indicator_6322', 'description': '不可使用,仅供参考:Indicator for fiscal periods in ASCII values (54=fq1, 55=fq2, 49=fy1, 50=fy2) for gross margin estimates'}
+{'id': 137474, 'data_set_name': '可以使用:assets_total_3', 'description': '不可使用,仅供参考:Total assets held by the company at period end.'}
+{'id': 61867, 'data_set_name': '可以使用:anl15_s_18_m_mktcap', 'description': '不可使用,仅供参考:The total market capitalization for the companies aggregated within GICS sector grouping with an 18-month forward mean price estimation.'}
+{'id': 140190, 'data_set_name': '可以使用:fnd44_fscore', 'description': '不可使用,仅供参考:A model intent on predicting accounting misstatements. The results of the model show the accounting metrics most indicative of misconduct. These accounting metrics can be classified into six broad categories: accruals, financial performance, market-related, nonfinancial, off balance sheet, and market incentives. Note that there are three F-score models. The F-Score is estimated over a longer period than the M-Score. But it shows challenging outof-sample performance in the US marke'}
+{'id': 61029, 'data_set_name': '可以使用:anl14_low_roa_fy1', 'description': '不可使用,仅供参考:The Lowest estimation of Returns on Assets - upcoming year'}
+{'id': 60903, 'data_set_name': '可以使用:anl14_low_capex_fp2', 'description': '不可使用,仅供参考:The Lowest Estimation of Capital Expenditures - upcoming 2 quarters'}
+{'id': 62094, 'data_set_name': '可以使用:quarterly_long_term_debt_to_total_capital', 'description': '不可使用,仅供参考:Ratio of long-term debt to total capital for the quarter.'}
+{'id': 137264, 'data_set_name': '可以使用:fnd17_ttmcapspps', 'description': '不可使用,仅供参考:Capital Spending per share, trailing 12 month'}
+{'id': 136984, 'data_set_name': '可以使用:fnd17_annperiods', 'description': '不可使用,仅供参考:Number of historical periods - Annual'}
+{'id': 60901, 'data_set_name': '可以使用:anl14_low_bvps_fy5', 'description': '不可使用,仅供参考:The lowest estimation of book value per share - upcoming 5 years'}
+{'id': 62238, 'data_set_name': '可以使用:anl44_2_capex_lastactvalue', 'description': '不可使用,仅供参考:capex lastactvalue'}
+{'id': 137486, 'data_set_name': '可以使用:cumulative_financial_risk_reserve_since_q1', 'description': '不可使用,仅供参考:Cumulative financial risk reserve value since the first quarter.'}
+{'id': 373536, 'data_set_name': '可以使用:top2000_factor2_group5_score', 'description': '不可使用,仅供参考:Second factor score for top 2000 securities, grouped into 5 clusters.'}
+{'id': 60996, 'data_set_name': '可以使用:anl14_low_ntprep_fp5', 'description': '不可使用,仅供参考:The Lowest Estimation of Reported Net Profit - upcoming 5 quarters'}
+{'id': 138993, 'data_set_name': '可以使用:other_net_income_total', 'description': '不可使用,仅供参考:Total other net income for the period.'}
+{'id': 140135, 'data_set_name': '可以使用:fnd31_devnorthamericaadditionalfactor4_twepsstdrev', 'description': '不可使用,仅供参考:Time Weighted Earnings Revision Dispersion. It is defined as the 6-month average of the time-weighted sum of the FY1 and FY2 earnings estimate revisions adjusted by the standard deviations of earnings estimates.'}
+{'id': 60433, 'data_set_name': '可以使用:anl10_fcfpast_det_estflag_6383', 'description': '不可使用,仅供参考:Estimate flag for free cash flow'}
+{'id': 60374, 'data_set_name': '可以使用:anl10_ebspast_det_indicator_6212', 'description': '不可使用,仅供参考:Indicator for fiscal periods in ASCII values (54=fq1, 55=fq2, 49=fy1, 50=fy2) for earnings before others estimates'}
+{'id': 140116, 'data_set_name': '可以使用:fnd31_creditrisk', 'description': '不可使用,仅供参考:Credit Risk is measured by CDS levels based on end-of-day par spreads.'}
+{'id': 60522, 'data_set_name': '可以使用:anl11_cit2reg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on CIT2 100% being the best'}
+{'id': 60644, 'data_set_name': '可以使用:anl11_g2reg_industryrnk', 'description': '不可使用,仅供参考:Industry Rank on G2 1 being the highest rank'}
+{'id': 373433, 'data_set_name': '可以使用:resampled_pca_factor_3', 'description': '不可使用,仅供参考:Third principal component factor derived from robust resampling of returns for top 2000 securities.'}
+{'id': 139066, 'data_set_name': '可以使用:short_term_creditors', 'description': '不可使用,仅供参考:Amount of short-term creditors at the reporting date.'}
+{'id': 139011, 'data_set_name': '可以使用:quarterly_short_term_creditors_value', 'description': '不可使用,仅供参考:Short-term creditors value for the specific quarter.'}
+{'id': 373338, 'data_set_name': '可以使用:pv173_rawratiosreltermspreadtransfema120d', 'description': '不可使用,仅供参考:It is defined as the 120-day exponential average of transformed relative term spread.'}
+{'id': 373403, 'data_set_name': '可以使用:pca_industry_grouping_method2_2', 'description': '不可使用,仅供参考:Industry grouping using second method and 2 clusters for top 3000 equities.'}
+{'id': 60619, 'data_set_name': '可以使用:anl11_ereg_industryperc', 'description': '不可使用,仅供参考:Industry Percentile on E Score with 100% being the best'}
+{'id': 373383, 'data_set_name': '可以使用:industry_grouping_level2_top1500', 'description': '不可使用,仅供参考:Industry classification at level 2 for the top 1500 instruments.'}
+{'id': 373309, 'data_set_name': '可以使用:pv173_ranksmt5yzspreadzscore60d', 'description': '不可使用,仅供参考:It is defined as the 60-day z-score of 5-year mid z-spreadIn the bond z-spread curve.'}
+{'id': 62240, 'data_set_name': '可以使用:anl44_2_capex_value', 'description': '不可使用,仅供参考:capex value'}
+========================= 数据字段结束 =======================================
+
+以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合
+
+你再检查一下, 如果你使用了
+Operator abs does not support event inputs
+Operator ts_mean does not support event inputs
+Operator ts_scale does not support event inputs
+Operator add does not support event inputs
+Operator sign does not support event inputs
+Operator greater does not support event inputs
+Operator ts_av_diff does not support event inputs
+Operator ts_quantile does not support event inputs
+Operator ts_arg_min does not support event inputs
+Operator divide does not support event inputs
+Operator ts_corr does not support event inputs
+Operator ts_decay_linear does not support event inputs
+Operator ts_sum does not support event inputs
+Operator ts_delay does not support event inputs
+Operator ts_arg_max does not support event inputs
+Operator ts_std_dev does not support event inputs
+Operator ts_regression does not support event inputs
+Operator ts_backfill does not support event inputs
+Operator signed_power does not support event inputs
+Operator ts_product does not support event inputs
+Operator ts_zscore does not support event inputs
+Operator group_rank does not support event inputs
+Operator subtract does not support event inputs
+Operator ts_delta does not support event inputs
+Operator ts_rank does not support event inputs
+Operator ts_count_nans does not support event inputs
+Operator ts_covariance does not support event inputs
+Operator multiply does not support event inputs
+Operator if_else does not support event inputs
+Operator group_neutralize does not support event inputs
+Operator group_zscore does not support event inputs
+Operator winsorize does not support event inputs
+注意, 以上操作符不能使用事件类型的数据集, 以上操作符禁止使用事件类型的数据集!!

planning_post_alpha.txt

@@ -1,501 +1,10 @@
-ts_sum(returns, 20)
+group_rank(group_neutralize(subtract(ts_mean(divide(multiply(ts_delta(last_closing_price, 1), market_capitalization_current), ts_sum(multiply(last_closing_price, market_capitalization_current), 20)), 20), industry), industry), industry)
-rank(subtract(ts_count_nans(anl14_high_ebit_fy2, 20), ts_count_nans(anl14_low_eps_fy5, 20)))
+multiply(rank(fnd72_pit_or_is_q_is_foreign_exch_loss),rank(pv173_zscoresmt5yzspread))
-zscore(ts_corr(returns, resampled_pca_factor_1, 20))
+multiply(rank(short_term_payables_other),rank(reverse(last_closing_price)))
-subtract(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), group_mean(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), 1, industry_grouping_level2_top2000))
+multiply(rank(reverse(quarter_total_assets)),rank(pv37_low_global1h))
-normalize(quarterly_working_capital_per_share_to_price_2)
+add(ts_zscore(ts_sum(divide(subtract(ts_delay(market_capitalization_current, 5), market_capitalization_current), ts_mean(market_capitalization_current, 20)), 20), 20), ts_zscore(ts_sum(divide(subtract(ts_delay(quarterly_long_term_debt, 5), quarterly_long_term_debt), ts_mean(quarterly_long_term_debt, 20)), 20), 20))
-ts_decay_linear(returns, 10)
+multiply(rank(reverse(market_capitalization_current)),rank(price_difference_bid_ask))
-subtract(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), group_mean(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), 1, industry_grouping_level2_top2000))
+multiply(ts_rank(divide(subtract(ts_max(quarterly_cash_and_equivalents, 20), ts_min(quarterly_cash_and_equivalents, 20)), ts_mean(quarterly_total_assets, 20)), 20), ts_corr(quarterly_long_term_debt, pv37_low_global30m, 20))
-zscore(divide(quarterly_free_cash_flow_per_share_non_annualized_2, multiply(net_income_annual, ts_rank(fnd44_working_capital_accruals, 12))))
+group_zscore(ts_sum(if_else(subtract(ts_mean(pv37_low_global2, 20), last_closing_price) > ts_std_dev(last_closing_price, 20), ts_av_diff(market_capitalization_current, 20), ts_covariance(pv37_cap_global30m, last_closing_price, 20)), 20), industry)
-zscore(fnd31_devnorthamericaadditionalfactor4_pcwt)
+multiply(rank(social_score_positive_correlation),rank(reverse(pv37_cap_global30m)))
-zscore(multiply(divide(quarterly_cash_flow_per_share_non_annualized_prior, quarterly_earnings_before_tax), inverse(ts_std_dev(quarterly_operating_margin_percent, 36))))
+subtract(ts_regression(market_capitalization_current, pv37_cap_global30m, 20, 0, 1), ts_regression(quarterly_current_assets_2, last_closing_price, 20, 0, 1))
-ts_corr(returns, pv173_rawratiosbondreturn20deqwt, 20)
-multiply(ts_delta(rank(daily_volume_to_shares_outstanding), 3), ts_delta(rank(ts_std_dev(returns, 20)), 3))
-ts_av_diff(returns, 20)
-ts_zscore(multiply(group_rank(anl14_high_ntp_fy1, industry), sign(ts_delta(daily_volume_percent_shares_out, 10))), 20)
-zscore(add(ts_sum(short_term_net_cash_balance, 20), reverse(ts_sum(other_off_balance_liabilities, 20))))
-ts_regression(ts_std_dev(returns, 20), ts_mean(returns, 20), 20)
-zscore(fnd44_working_capital_accruals)
-ts_zscore(multiply(ts_delay(returns, 5), ts_rank(daily_volume_percent_shares_out, 20)), 20)
-normalize(subtract(divide(quarterly_cash_flow_per_share, net_income_quarterly_4), ts_rank(fnd44_perc_accruals, 24)))
-zscore(fnd44_perc_accruals)
-subtract(ts_rank(quarterly_book_value_per_share_5, 30), ts_rank(quarterly_tangible_book_value_per_share_3, 30))
-rank(divide(multiply(quarterly_cash_flow_per_share_2, quarterly_asset_turnover), add(net_income_annual, fnd44_probability_restatement)))
-last_diff_value(ts_std_dev(returns, 20), 20)
-group_neutralize(ts_zscore(daily_volume_to_shares_outstanding, 60), industry_grouping_level2_top2000)
-group_zscore(subtract(ts_rank(anl14_high_ntp_fy1, 20), ts_rank(anl14_low_eps_fy5, 20)), industry)
-group_neutralize(ts_delta(pv37_high_global2h, 5), industry_grouping_level2_top2000)
-group_neutralize(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), industry_grouping_level2_top2000)
-zscore(anl14_high_revenue_fy2)
-ts_delta(ts_std_dev(returns, 60), 10)
-multiply(ts_std_dev(returns, 20), sign(ts_delta(returns, 1)))
-subtract(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), group_mean(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), 1, industry_grouping_level2_top2000))
-ts_decay_linear(ts_std_dev(returns, 20), 20)
-subtract(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000))
-normalize(quarterly_asset_turnover)
-group_scale(add(ts_quantile(daily_volume_percent_shares_out, 20), ts_quantile(anl14_low_ndebt_fy1, 20)), industry)
-zscore(divide(quarterly_pe_ratio_high, 1))
-group_neutralize(ts_av_diff(pv37_volume_global, 20), industry_grouping_level2_top2000)
-normalize(anl14_low_eps_fy2)
-zscore(multiply(ts_regression(returns, pv173_zscoresbondreturn20deqwt, 20), sign(ts_delta(anl14_high_revenue_fy2, 5))))
-subtract(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), group_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1, industry_grouping_level2_top2000))
-subtract(ts_corr(pv37_volume_global, returns, 20), group_mean(ts_corr(pv37_volume_global, returns, 20), 1, industry_grouping_level2_top2000))
-subtract(ts_backfill(pv37_volume_global, 20, 1, "NAN"), group_mean(ts_backfill(pv37_volume_global, 20, 1, "NAN"), 1, industry_grouping_level2_top2000))
-rank(divide(ts_std_dev(anl14_high_ntp_fy1, 20), ts_std_dev(resampled_pca_factor_1, 20)))
-subtract(rank(ts_std_dev(ttm_net_income_value, 252)), rank(ts_std_dev(quarterly_asset_turnover, 252)))
-group_neutralize(ts_rank(pv37_low_global, 20), industry_grouping_level2_top2000)
-rank(subtract(quarterly_working_capital_per_share_to_price_2, fnd44_working_capital_accruals))
-quantile(ts_std_dev(returns, 20))
-subtract(ts_zscore(ts_std_dev(returns, 20), 60), group_mean(ts_zscore(ts_std_dev(returns, 20), 60), 1, industry_grouping_level2_top2000))
-subtract(ts_zscore(daily_volume_to_shares_outstanding, 60), group_mean(ts_zscore(daily_volume_to_shares_outstanding, 60), 1, industry_grouping_level2_top2000))
-group_neutralize(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), industry_grouping_level2_top2000)
-normalize(subtract(divide(quarterly_free_cash_flow, quarterly_total_revenue_3), ts_rank(fnd44_probability_restatement, 12)))
-multiply(ts_std_dev(returns, 20), ts_mean(returns, 20), false)
-rank(divide(anl69_best_px_cps_ratio, 1))
-rank(ts_corr(ts_mean(fnd31_qsg5additionalfactor3_monthly_y5speq4rqsr, 20), ts_mean(fnd31_qsg5additionalfactor3_monthly_yoychgroepct, 20), 50))
-normalize(divide(quarterly_cash_flow_per_share, net_income_quarterly_4))
-ts_decay_linear(rank(ts_delta(fnd72_s_pit_or_is_q_is_net_non_oper_loss, 10)), 5)
-rank(anl14_high_roe_fy2)
-group_mean(multiply(ts_std_dev(returns, 20), daily_volume_percent_shares_out), 1, industry_grouping_level2_top1500)
-zscore(multiply(divide(quarterly_free_cash_flow_per_share_alt_2, quarterly_earnings_before_tax_5), sign(subtract(ts_mean(quarterly_receivables_turnover_2, 12), ts_mean(quarterly_receivables_turnover_2, 24)))))
-ts_arg_min(ts_std_dev(returns, 20), 20)
-subtract(ts_corr(returns, daily_volume_to_shares_outstanding, 20), group_mean(ts_corr(returns, daily_volume_to_shares_outstanding, 20), 1, industry_grouping_level2_top2000))
-multiply(rank(ts_delta(fnd31_qsg5additionalfactor3_monthly_fwdebitdaev, 5)), ts_rank(fnd31_qsg5additionalfactor3_monthly_pbwt, 10))
-subtract(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000))
-subtract(ts_rank(long_term_trade_debt, 20), ts_rank(long_term_other_current_liabilities, 20))
-group_neutralize(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), industry_grouping_level2_top2000)
-normalize(multiply(divide(quarterly_cash_flow_per_share_alt, normalized_net_income_available_to_common_annual), sign(ts_delta(quarterly_asset_turnover, 6))))
-rank(divide(quarterly_earnings_before_tax_5, annual_total_revenue))
-ts_decay_linear(rank(ts_std_dev(returns, 20)), 20)
-group_neutralize(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), industry_grouping_level2_top2000)
-group_neutralize(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), industry_grouping_level2_top2000)
-group_neutralize(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), industry_grouping_level2_top2000)
-group_mean(ts_std_dev(returns, 20), sharesout, industry)
-multiply(group_rank(ts_std_dev(returns, 20), industry_grouping_level2_top1500), rank(ts_std_dev(returns, 20)))
-rank(subtract(divide(quarterly_cash_per_share_3, normalized_net_income), fnd44_perc_accruals))
-rank(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01)))
-rank(multiply(inverse(ts_std_dev(quarterly_net_income_3, 24)), divide(quarterly_cash, net_income_annual)))
-zscore(multiply(divide(quarterly_cash_flow_per_share_nonannualized, net_income_quarterly_4), inverse(ts_std_dev(normalized_net_income, 24))))
-subtract(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000))
-rank(fnd44_perc_accruals)
-ts_decay_linear(subtract(ts_mean(anl14_high_ntp_fy1, 20), group_mean(anl14_high_ntp_fy1, industry, industry)), 20)
-group_neutralize(group_rank(pv37_volume_global, industry_grouping_level2_top2000), industry_grouping_level2_top2000)
-subtract(rank(ts_std_dev(returns, 5)), rank(ts_std_dev(returns, 60)))
-subtract(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), group_mean(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), 1, industry_grouping_level2_top2000))
-ts_corr(ts_std_dev(returns, 20), resampled_pca_factor_1, 20)
-divide(rank(ts_std_dev(returns, 20)), group_mean(rank(ts_std_dev(returns, 20)), 1, industry_grouping_level2_top1500))
-subtract(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), group_mean(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), 1, industry_grouping_level2_top2000))
-group_neutralize(ts_delta(pv37_low_global1h, 5), industry_grouping_level2_top2000)
-subtract(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), group_mean(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), 1, industry_grouping_level2_top2000))
-winsorize(divide(quarterly_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), std=3)
-ts_arg_max(ts_std_dev(returns, 20), 20)
-ts_zscore(divide(group_mean(anl14_high_ntp_fy1, industry, industry), ts_mean(anl14_high_ntp_fy1, 20)), 20)
-group_scale(multiply(ts_av_diff(anl14_high_ebit_fy2, 20), sign(ts_delta(pv173_zscoresbondreturn20deqwt, 5))), industry)
-ts_std_dev(returns, 60)
-rank(multiply(divide(quarterly_cash_flow_per_share_2, quarterly_earnings_before_tax_5), inverse(ts_std_dev(quarterly_operating_margin_percent, 12))))
-divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500))
-log(add(abs(fnd44_perc_accruals), 0.01))
-normalize(divide(ts_mean(anl14_high_ntp_fy1, 20), group_mean(anl14_high_ntp_fy1, industry, industry)))
-ts_corr(returns, ts_backfill(anl14_high_revenue_fy2, 250), 20)
-rank(multiply(ts_arg_max(anl14_high_revenue_fy1, 20), inverse(ts_arg_min(anl14_low_ebit_fy4, 20))))
-group_neutralize(ts_zscore(ts_std_dev(returns, 20), 60), industry_grouping_level2_top2000)
-group_backfill(ts_std_dev(returns, 20), industry, 20)
-normalize(divide(quarterly_free_cash_flow_per_share, net_income_quarterly_4))
-rank(fnd31_devnorthamericaadditionalfactor4_ps_wt)
-rank(divide(quarterly_long_term_debt_to_assets, quarterly_total_assets))
-normalize(ts_std_dev(returns, 20))
-ts_target_tvr_decay(ts_std_dev(returns, 20), 0, 1, 0.1)
-subtract(ts_rank(fnd31_devnorthamericaadditionalfactor4_y5speq4vc, 20), ts_rank(fnd31_devnorthamericaadditionalfactor4_divgp, 20))
-group_neutralize(ts_arg_min(pv37_volume_global, 10), industry_grouping_level2_top2000)
-inverse(add(fnd44_working_capital_accruals, 0.01))
-subtract(ts_rank(share_based_awards, 30), ts_rank(share_based_deferred_tax, 30))
-group_neutralize(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), industry_grouping_level2_top2000)
-divide(rank(ts_std_dev(returns, 20)), add(ts_std_dev(returns, 60), 0.001))
-group_neutralize(subtract(ts_mean(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), ts_mean(returns, 10)), industry_grouping_level2_top2000)
-add(add(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_asset_turnover)), reverse(rank(fnd44_perc_accruals)))
-subtract(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), group_mean(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 1, industry_grouping_level2_top2000))
-ts_arg_max(ts_sum(fnd31_qsg5additionalfactor3_monthly_rev6fy2, 30), 60)
-rank(fnd44_probability_restatement)
-group_neutralize(add(ts_product(anl14_high_eps_fp1, 10), reverse(ts_product(anl14_low_roe_fy1, 10))), industry)
-ts_arg_min(ts_sum(long_term_secured_debt, 30), 60)
-if_else(ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 120), reverse(rank(ts_std_dev(returns, 20))), rank(ts_std_dev(returns, 20)))
-zscore(pv173_rawratiosbondreturn20deqwt)
-ts_zscore(returns, 20)
-multiply(ts_delta(daily_volume_percent_shares_out, 3), ts_std_dev(returns, 20))
-subtract(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), group_mean(ts_quantile(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, "gaussian"), 1, industry_grouping_level2_top2000))
-divide(rank(daily_volume_to_shares_outstanding), group_mean(rank(daily_volume_to_shares_outstanding), 1, industry_grouping_level2_top1500))
-subtract(ts_delta(pv37_high_global2h, 5), group_mean(ts_delta(pv37_high_global2h, 5), 1, industry_grouping_level2_top2000))
-normalize(divide(quarterly_free_cash_flow_per_share_alt_2, multiply(net_income_after_tax_2, ts_std_dev(quarterly_net_income_3, 12))))
-ts_scale(ts_std_dev(returns, 20), 20)
-normalize(multiply(inverse(ts_std_dev(quarterly_net_income_3, 18)), divide(quarterly_free_cash_flow, quarterly_total_revenue_3)))
-ts_av_diff(ts_std_dev(returns, 20), 20)
-normalize(multiply(divide(quarterly_free_cash_flow_per_share, quarterly_pe_ratio_maximum), inverse(ts_std_dev(quarterly_asset_turnover, 24))))
-ts_sum(ts_delta(ts_std_dev(returns, 20), 1), 20)
-rank(subtract(divide(quarterly_cash_per_share_3, net_income_available_to_common_incl_extraordinary_annual), fnd44_probability_restatement))
-zscore(divide(quarterly_free_cash_flow_per_share_alt_2, net_income_quarterly_4))
-multiply(sign(ts_sum(shareholders_other_equity_2, 20)), ts_rank(short_term_bank_debt_amount, 30))
-ts_arg_max(returns, 20)
-multiply(ts_std_dev(returns, 20), ts_rank(pv37_volume_14, 20))
-subtract(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), group_mean(ts_backfill(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 1, "NAN"), 1, industry_grouping_level2_top2000))
-subtract(rank(ts_mean(fnd72_pit_or_cf_q_cf_cash_from_oper, 20)), rank(ts_mean(fnd72_pit_or_cf_q_cf_other_fnc_act, 20)))
-rank(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(fnd44_perc_accruals)))
-rank(divide(quarterly_total_shareholder_equity, add(quarterly_total_debt_4, 0.01)))
-rank(divide(net_income_quarterly_4, annual_total_revenue))
-subtract(pv37_high_global, pv37_low_global)
-group_scale(multiply(ts_arg_min(anl14_high_revenue_fy1, 20), sign(ts_delta(anl14_low_ebit_fy4, 5))), industry)
-group_mean(ts_std_dev(returns, 20), ts_mean(returns, 20), industry)
-not(ts_std_dev(returns, 20) > 0)
-group_neutralize(rank(ts_std_dev(returns, 20)), industry_grouping_level2_top1500)
-rank(ts_covariance(ts_mean(fnd72_pit_or_bs_q_bs_future_min_oper_lease_oblig, 30), ts_mean(fnd72_pit_or_is_q_is_eqy_earn_from_invest_assoc, 30), 60))
-zscore(subtract(divide(quarterly_free_cash_flow_per_share_alt_2, net_income_after_tax_2), multiply(fnd44_probability_restatement, 0.1)))
-and(ts_std_dev(returns, 20) > 0, ts_mean(returns, 20) > 0)
-ts_arg_max(ts_sum(fnd72_s_pit_or_bs_q_bs_accrued_income_taxes, 30), 60)
-subtract(signed_power(ts_delta(log(pv37_volume_global), 5), 2), group_mean(signed_power(ts_delta(log(pv37_volume_global), 5), 2), 1, industry_grouping_level2_top2000))
-zscore(divide(quarterly_total_shareholder_equity, quarterly_total_assets))
-rank(divide(quarterly_operating_margin_percent, 1))
-group_neutralize(multiply(ts_rank(pv37_volume_global, 20), ts_rank(returns, 20)), industry_grouping_level2_top2000)
-vector_neut(rank(fnd44_perc_accruals), rank(book_value_per_share_quarterly))
-rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, net_income_quarterly_4))
-divide(ts_std_dev(returns, 20), ts_mean(returns, 20))
-group_neutralize(ts_corr(returns, daily_volume_to_shares_outstanding, 20), industry_grouping_level2_top2000)
-ts_backfill(anl14_high_revenue_fy2, 250)
-ts_corr(ts_std_dev(returns, 20), ts_delay(ts_std_dev(returns, 20), 1), 20)
-ts_arg_max(ts_sum(quarterly_quick_ratio_2, 30), 60)
-ts_corr(ts_std_dev(returns, 20), ts_mean(returns, 20), 20)
-rank(fnd44_score)
-group_neutralize(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), industry_grouping_level2_top2000)
-hump(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)), hump=0.05)
-sign(ts_std_dev(returns, 20))
-subtract(ts_av_diff(pv37_volume_global, 20), group_mean(ts_av_diff(pv37_volume_global, 20), 1, industry_grouping_level2_top2000))
-group_neutralize(ts_scale(pv37_volume_global, 20), industry_grouping_level2_top2000)
-subtract(rank(ts_std_dev(returns, 20)), rank(ts_mean(daily_volume_to_shares_outstanding, 20)))
-rank(divide(anl14_low_ebitda_fy2, annual_total_revenue))
-normalize(add(ts_product(anl14_high_eps_fp1, 10), ts_product(anl14_low_roe_fy1, 10)))
-jump_decay(rank(subtract(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), fnd44_perc_accruals)), 21, sensitivity=0.5, force=0.1)
-zscore(pv37_low_14)
-subtract(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000))
-zscore(quarterly_receivables_turnover_2)
-normalize(multiply(ts_quantile(anl14_low_eps_fy5, 20), inverse(ts_mean(twelve_month_high_price, 20))))
-subtract(ts_max(ts_std_dev(returns, 20), 20), ts_std_dev(returns, 20))
-multiply(ts_delta(rank(ts_std_dev(returns, 20)), 5), ts_delta(rank(daily_volume_to_shares_outstanding), 5))
-rank(ts_corr(quarterly_cash_flow_per_share_2, quarterly_net_income_3, 252))
-ts_decay_linear(rank(ts_delta(annual_net_income_incl_extraordinary, 10)), 5)
-subtract(ts_rank(fnd31_qsg5additionalfactor3_monthly_mpgghcy3, 30), ts_rank(fnd31_qsg5additionalfactor3_monthly_numrevy2, 30))
-ts_av_diff(rank(ts_std_dev(returns, 20)), 60)
-days_from_last_change(ts_std_dev(returns, 20))
-subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals))
-multiply(ts_std_dev(returns, 20), rank(fnd44_score))
-zscore(divide(operating_income_2, annual_total_revenue))
-normalize(divide(anl14_high_ebitda_fy2, annual_total_revenue))
-ts_arg_min(ts_sum(quarterly_receivables_turnover_2, 30), 60)
-subtract(rank(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01))), rank(fnd44_working_capital_accruals))
-normalize(divide(quarterly_research_and_development_expense_2, annual_total_revenue))
-rank(divide(anl14_high_ebit_fy2, annual_total_revenue))
-zscore(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, add(fnd72_pit_or_is_q_net_income, abs(fnd44_perc_accruals))))
-abs(ts_av_diff(ts_std_dev(returns, 20), 20))
-normalize(add(ts_rank(anl14_high_revenue_fp1, 20), reverse(ts_rank(anl14_low_ntprep_fp4, 20))))
-if_else(ts_std_dev(returns, 20) > ts_mean(returns, 20), 1, 0)
-multiply(rank(ts_delta(fnd17_arev, 5)), ts_rank(fnd17_arevps, 10))
-subtract(ts_arg_min(pv37_volume_global, 10), group_mean(ts_arg_min(pv37_volume_global, 10), 1, industry_grouping_level2_top2000))
-group_neutralize(ts_regression(returns, pv37_volume_global, 20, 0, 1), industry_grouping_level2_top2000)
-zscore(add(divide(quarterly_cash_flow_per_share_non_annualized, book_value_per_share_quarterly), inverse(ts_rank(fnd44_perc_accruals, 24))))
-sign(subtract(ts_sum(fnd72_pit_or_cf_a_cf_net_inc, 250), ts_sum(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 250)))
-subtract(ts_delta(pv37_low_global1h, 5), group_mean(ts_delta(pv37_low_global1h, 5), 1, industry_grouping_level2_top2000))
-ts_rank(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals)), 252)
-group_neutralize(abs(ts_delta(log(pv37_volume_global), 5)), industry_grouping_level2_top2000)
-multiply(sign(ts_sum(fnd31_devnorthamericaadditionalfactor4_apg, 20)), ts_rank(fnd31_devnorthamericaadditionalfactor4_mpg, 30))
-subtract(ts_rank(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, 20), ts_rank(fnd72_pit_or_cf_a_cf_other_fnc_act, 20))
-subtract(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), group_mean(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), 1, industry_grouping_level2_top2000))
-rank(add(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_interest_expense), inverse(fnd44_probability_restatement)))
-rank(subtract(ts_quantile(anl14_high_ebit_fp4, 20), ts_quantile(pv173_rawratiosmt5yzspreadchg60d, 20)))
-multiply(sign(ts_sum(annual_total_revenue, 20)), ts_rank(annual_debt_to_total_capital, 30))
-zscore(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(fnd44_perc_accruals)))
-ts_std_dev(returns, 20)
-rank(add(divide(fnd72_pit_or_cf_a_cf_net_inc, fnd72_pit_or_is_a_is_net_interest_expense), inverse(ts_std_dev(quarterly_net_income_available_to_common_2, 24))))
-group_neutralize(ts_av_diff(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000)
-group_neutralize(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), industry_grouping_level2_top2000)
-normalize(divide(quarterly_net_income_3, quarterly_total_assets))
-rank(multiply(divide(quarterly_free_cash_flow_per_share_non_annualized, quarterly_book_value_per_share_5), sign(ts_delta(quarterly_asset_turnover, 12))))
-normalize(add(divide(quarterly_cash_flow_per_share_alt, book_value_per_share_quarterly), inverse(ts_rank(fnd44_perc_accruals, 18))))
-multiply(group_rank(ts_std_dev(returns, 20), industry_grouping_level2_top1500), rank(daily_volume_percent_shares_out))
-zscore(divide(quarterly_cash_flow_per_share_2, add(normalized_net_income, abs(fnd44_working_capital_accruals))))
-zscore(divide(fnd17_arevps, 1))
-add(ts_std_dev(returns, 20), ts_std_dev(returns, 10))
-ts_quantile(ts_std_dev(returns, 20), 20)
-ts_count_nans(ts_std_dev(returns, 20), 20)
-reverse(rank(ts_std_dev(returns, 20)))
-rank(quarterly_current_ratio_alt)
-group_neutralize(ts_rank(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000)
-ts_rank(ts_std_dev(returns, 20), 20)
-bucket(rank(subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)), range="0,1,0.2")
-subtract(ts_scale(pv37_volume_global, 20), group_mean(ts_scale(pv37_volume_global, 20), 1, industry_grouping_level2_top2000))
-group_zscore(pv37_volume_14, industry)
-ts_backfill(divide(group_zscore(anl14_high_nav_fy2, industry), ts_mean(anl14_low_ndebt_fy5, 20)), 20)
-rank(add(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)))))
-ts_scale(subtract(group_mean(anl14_high_revenue_fp1, industry, industry), ts_mean(fnd31_qsg5additionalfactor3_monthly_pcdwf, 20)), 20)
-rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)))
-rank(ts_covariance(ts_mean(fnd31_devnorthamericaadditionalfactor4_rev6fy2, 30), ts_mean(fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev, 30), 60))
-ts_decay_linear(rank(subtract(ts_sum(fnd72_pit_or_bs_q_bs_other_cur_asset, 60), ts_sum(fnd72_pit_or_bs_q_bs_other_cur_liab, 60))), 5)
-subtract(ts_covariance(returns, pv37_volume_global, 20), group_mean(ts_covariance(returns, pv37_volume_global, 20), 1, industry_grouping_level2_top2000))
-rank(subtract(divide(quarterly_free_cash_flow_per_share, quarterly_pe_ratio_high), fnd44_probability_restatement))
-group_neutralize(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000)
-ts_delta(ts_backfill(anl14_high_revenue_fy2, 250), 5)
-rank(ts_covariance(ts_mean(net_income_available_to_common_incl_extraordinary_annual, 30), ts_mean(normalized_net_income_annual, 30), 60))
-rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)))
-divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry))
-divide(ts_std_dev(returns, 20), ts_max(ts_std_dev(returns, 60), 60))
-zscore(add(ts_rank(anl14_high_revenue_fy2, 20), reverse(ts_rank(anl14_low_ebit_fy5, 20))))
-ts_arg_max(ts_sum(fnd17_apayratio, 30), 60)
-group_neutralize(ts_count_nans(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000)
-group_neutralize(fnd86_fundamental_score, industry)
-subtract(ts_count_nans(pv37_volume_global, 20), group_mean(ts_count_nans(pv37_volume_global, 20), 1, industry_grouping_level2_top2000))
-group_neutralize(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), industry_grouping_level2_top2000)
-rank(multiply(divide(quarterly_free_cash_flow_per_share, book_value_per_share_quarterly), sign(subtract(ts_mean(quarterly_asset_turnover, 12), ts_mean(quarterly_asset_turnover, 24)))))
-subtract(ts_regression(returns, pv37_volume_global, 20, 0, 1), group_mean(ts_regression(returns, pv37_volume_global, 20, 0, 1), 1, industry_grouping_level2_top2000))
-ts_arg_min(rank(ts_std_dev(returns, 20)), 60)
-subtract(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), group_mean(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), 1, industry_grouping_level2_top2000))
-normalize(multiply(inverse(ts_std_dev(quarterly_operating_margin_percent, 6)), divide(quarterly_free_cash_flow_per_share, quarterly_total_revenue_3)))
-rank(divide(add(quarterly_cash_flow_per_share_alt, fnd72_pit_or_cf_q_cf_cash_from_oper), add(net_income_current_period, abs(fnd44_perc_accruals))))
-group_neutralize(ts_std_dev(pv37_volume_global, 20), industry_grouping_level2_top2000)
-normalize(subtract(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), ts_rank(fnd44_perc_accruals, 6)))
-group_neutralize(ts_rank(pv37_high_global2, 20), industry_grouping_level2_top2000)
-multiply(zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry))), ts_mean(resampled_pca_factor_1, 20))
-ts_decay_linear(rank(ts_delta(share_capital_ordinary_quarterly, 10)), 5)
-add(rank(ts_std_dev(returns, 20)), multiply(rank(ts_mean(daily_volume_percent_shares_out, 20)), 0.5))
-multiply(sign(ts_sum(fnd44_probability_restatement, 20)), ts_rank(fnd44_working_capital_accruals, 30))
-ts_std_dev(ts_mean(returns, 5), 20)
-subtract(ts_rank(ts_delta(pv37_volume_global, 3), 20), group_mean(ts_rank(ts_delta(pv37_volume_global, 3), 20), 1, industry_grouping_level2_top2000))
-quantile(subtract(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(fnd44_perc_accruals)))
-normalize(srp_earnings_score)
-abs(ts_std_dev(returns, 20))
-add(multiply(rank(ts_std_dev(returns, 20)), 2), multiply(rank(daily_volume_percent_shares_out), 1.5))
-normalize(add(divide(fnd72_pit_or_cf_q_cf_net_inc, fnd72_pit_or_is_q_net_income), inverse(ts_std_dev(quarterly_net_income_3, 36))))
-ts_target_tvr_delta_limit(ts_std_dev(returns, 20), ts_mean(returns, 20), 0, 1, 0.1)
-rank(add(ts_scale(anl14_high_revenue_fy2, 20), reverse(ts_scale(anl14_low_ebit_fy5, 20))))
-rank(multiply(ts_std_dev(anl14_high_revenue_fy2, 20), inverse(ts_std_dev(anl14_low_ebit_fy5, 20))))
-normalize(add(divide(fnd72_pit_or_cf_a_cf_cash_from_fnc_act, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), inverse(ts_rank(fnd44_working_capital_accruals, 6))))
-subtract(group_rank(pv37_volume_global, industry_grouping_level2_top2000), group_mean(group_rank(pv37_volume_global, industry_grouping_level2_top2000), 1, industry_grouping_level2_top2000))
-ts_corr(ts_std_dev(returns, 20), fnd31_ohlsonscore, 20)
-rank(ts_corr(ts_mean(investment_contracts_total, 20), ts_mean(value_investment_assets_total, 20), 50))
-rank(ts_corr(ts_mean(fnd72_s_pit_or_cf_q_cf_cash_from_oper, 20), ts_mean(fnd72_s_pit_or_cf_q_cf_other_non_cash_adjust, 20), 50))
-normalize(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, multiply(fnd72_s_pit_or_is_q_net_income, ts_std_dev(normalized_net_income_annual, 12))))
-reverse(ts_delta(rank(ts_std_dev(returns, 20)), 10))
-if_else(rank(daily_volume_percent_shares_out) < 0.5, rank(ts_std_dev(returns, 20)), reverse(rank(ts_std_dev(returns, 20))))
-subtract(group_max(ts_std_dev(returns, 20), industry_grouping_level2_top1500), group_min(ts_std_dev(returns, 20), industry_grouping_level2_top1500))
-ts_arg_min(ts_sum(fnd72_s_pit_or_bs_q_bs_future_min_oper_lease_oblig, 30), 60)
-multiply(ts_std_dev(returns, 20), ts_std_dev(returns, 60))
-or(ts_std_dev(returns, 20) > 0, ts_mean(returns, 20) > 0)
-group_neutralize(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), industry_grouping_level2_top2000)
-group_neutralize(ts_decay_linear(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000)
-zscore(divide(group_rank(anl14_high_nav_fy2, industry), ts_std_dev(anl14_low_ndebt_fy5, 20)))
-normalize(divide(quarterly_price_to_sales_ratio, 1))
-normalize(ts_regression(anl14_high_ntp_fy1, daily_volume_percent_shares_out, 20))
-divide(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)), add(fnd44_perc_accruals, 0.01))
-kth_element(ts_std_dev(returns, 20), 20, 1)
-zscore(subtract(1, divide(fnd44_perc_accruals, ts_mean(quarterly_net_income_available_to_common_2, 12))))
-add(rank(fnd44_perc_accruals), rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))))
-ts_arg_min(returns, 20)
-group_neutralize(signed_power(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 3), 2), industry_grouping_level2_top2000)
-rank(ts_corr(anl14_high_ebit_fp4, pv173_rawratiosmt5yzspreadchg60d, 20))
-group_neutralize(signed_power(ts_delta(log(pv37_volume_global), 5), 2), industry_grouping_level2_top2000)
-rank(fnd44_working_capital_accruals)
-zscore(divide(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), sharesout, industry)))
-ts_corr(ts_mean(returns, 5), ts_std_dev(returns, 20), 20)
-group_neutralize(ts_delta(returns, 20), industry_grouping_level2_top2000)
-subtract(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), group_mean(ts_decay_linear(ts_zscore(pv37_volume_global, 20), 10), 1, industry_grouping_level2_top2000))
-ts_regression(anl14_high_ebit_fp4, pv173_zscoresatlas_unit_name, 20, 0, 1)
-group_neutralize(ts_quantile(pv37_volume_global, 20, "gaussian"), industry_grouping_level2_top2000)
-ts_max(rank(fnd44_working_capital_accruals), 252)
-subtract(ts_rank(pv37_low_global, 20), group_mean(ts_rank(pv37_low_global, 20), 1, industry_grouping_level2_top2000))
-normalize(multiply(inverse(ts_std_dev(quarterly_operating_margin_percent, 18)), divide(quarterly_cash, net_income_current_period)))
-multiply(ts_std_dev(returns, 20), zscore(fnd31_ohlsonscore))
-ts_quantile(returns, 20)
-subtract(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), group_mean(ts_regression(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20, 0, 0), 1, industry_grouping_level2_top2000))
-rank(multiply(divide(quarterly_cash_per_share_value, book_value_per_share_quarterly), sign(ts_delta(quarterly_receivables_turnover_2, 6))))
-ts_delay(rank(fnd44_working_capital_accruals), 21)
-rank(divide(add(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_cf_a_cf_cash_from_fnc_act), add(fnd72_pit_or_is_q_net_income, abs(fnd44_perc_accruals))))
-rank(subtract(divide(quarterly_cash_per_share_3, quarterly_book_value_per_share_2), multiply(fnd44_probability_restatement, 0.05)))
-normalize(multiply(signed_power(ts_delta(returns, 5), 2), sign(ts_delta(pv173_zscoresbondreturn20deqwt, 5))))
-winsorize(fnd44_perc_accruals, std=3)
-group_neutralize(anl14_low_ebit_fy5, industry)
-subtract(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000))
-subtract(ts_rank(quarterly_long_term_debt_to_equity_ratio, 20), ts_rank(quarterly_total_debt_to_equity_2, 20))
-trade_when(ts_std_dev(returns, 20), ts_mean(returns, 20), ts_delta(ts_std_dev(returns, 20), 1))
-normalize(subtract(1, divide(fnd44_perc_accruals, ts_mean(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 36))))
-log(ts_std_dev(returns, 20))
-group_neutralize(add(multiply(rank(ts_std_dev(returns, 20)), 3), multiply(rank(ts_mean(daily_volume_to_shares_outstanding, 20)), 2)), industry_grouping_level2_top1500)
-normalize(fnd31_devnorthamericaadditionalfactor4_pbdwf)
-group_neutralize(ts_arg_max(pv37_volume_global, 10), industry_grouping_level2_top2000)
-multiply(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500))
-rank(ts_mean(daily_volume_percent_shares_out, 20))
-normalize(rank(subtract(divide(quarterly_cash_flow_per_share_2, add(fnd13_statementq_sopi, 0.01)), fnd44_working_capital_accruals)))
-group_zscore(ts_std_dev(returns, 20), industry)
-group_neutralize(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), industry_grouping_level2_top2000)
-zscore(anl14_high_eps_fy2)
-normalize(fnd31_devnorthamericaadditionalfactor4_tw_ebitdaev)
-rank(divide(fnd72_pit_or_cf_q_cf_cash_from_oper, fnd72_pit_or_is_q_net_income))
-group_rank(ts_std_dev(returns, 20), industry)
-group_neutralize(rank(fnd44_score), industry)
-reverse(ts_std_dev(returns, 20))
-subtract(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), group_mean(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), 1, industry_grouping_level2_top2000))
-group_neutralize(ts_count_nans(pv37_volume_global, 20), industry_grouping_level2_top2000)
-zscore(group_zscore(add(ts_std_dev(anl14_high_ntp_fy1, 20), ts_std_dev(resampled_pca_factor_1, 20)), industry))
-bucket(rank(ts_std_dev(returns, 20)), "0,1,0.1")
-ts_decay_linear(multiply(signed_power(ts_delta(returns, 5), 2), daily_volume_percent_shares_out), 20)
-ts_arg_min(ts_sum(fnd17_qcurratio2, 30), 60)
-zscore(divide(quarterly_free_cash_flow, add(fnd72_pit_or_is_q_net_income, abs(fnd44_working_capital_accruals))))
-subtract(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), group_mean(ts_sum(if_else(ts_delta(pv37_volume_global, 1) > 0, 1, -1), 10), 1, industry_grouping_level2_top2000))
-group_neutralize(multiply(ts_std_dev(returns, 20), ts_std_dev(returns, 60)), industry_grouping_level2_top1500)
-ts_corr(returns, resampled_pca_factor_1, 20)
-rank(ts_std_dev(quarterly_pe_ratio_high, 126))
-normalize(divide(quarterly_cash_flow_per_share_non_annualized, multiply(net_income_available_to_common_incl_extraordinary_annual, ts_std_dev(quarterly_operating_margin_percent, 6))))
-subtract(ts_quantile(pv37_volume_global, 20, "gaussian"), group_mean(ts_quantile(pv37_volume_global, 20, "gaussian"), 1, industry_grouping_level2_top2000))
-ts_delta(group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top1500), 10)
-rank(quarterly_sga_to_sales_ratio)
-zscore(divide(ts_sum(anl14_high_ebit_fy2, 20), ts_sum(anl14_low_eps_fy5, 20)))
-normalize(add(inverse(ts_std_dev(normalized_net_income_available_to_common_annual, 18)), divide(quarterly_free_cash_flow_per_share_non_annualized, net_income_annual)))
-normalize(divide(fnd17_pehigh, 1))
-quantile(ts_std_dev(returns, 20), gaussian, 1.0)
-rank(add(add(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_asset_turnover)), subtract(rank(reverse(fnd44_perc_accruals)), rank(fnd44_probability_restatement))))
-subtract(ts_std_dev(returns, 20), group_mean(ts_std_dev(returns, 20), 1, industry_grouping_level2_top2000))
-ts_sum(ts_std_dev(returns, 20), 20)
-group_neutralize(ts_backfill(pv37_volume_global, 20, 1, "NAN"), industry_grouping_level2_top2000)
-rank(ts_std_dev(quarterly_asset_turnover, 252))
-group_neutralize(ts_covariance(returns, pv37_volume_global, 20), industry_grouping_level2_top2000)
-subtract(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), group_mean(add(ts_zscore(pv37_volume_global, 20), ts_zscore(returns, 20)), 1, industry_grouping_level2_top2000))
-min(ts_std_dev(returns, 20), ts_mean(returns, 20))
-ts_std_dev(ts_std_dev(returns, 20), 20)
-zscore(divide(quarterly_total_assets, quarterly_total_liabilities))
-subtract(ts_rank(fnd72_s_pit_or_bs_q_bs_sh_cap_and_apic, 30), ts_rank(fnd72_s_pit_or_bs_q_bs_total_avail_line_of_credit, 30))
-ts_count_nans(returns, 20)
-multiply(rank(ts_delta(fnd72_s_pit_or_is_q_is_invest_income, 5)), ts_rank(fnd72_s_pit_or_is_q_net_income, 10))
-zscore(subtract(1, divide(fnd44_working_capital_accruals, ts_mean(fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs, 24))))
-zscore(divide(anl14_low_ebit_fy2, annual_total_revenue))
-ts_decay_linear(rank(ts_delta(fnd44_perc_accruals, 10)), 5)
-zscore(divide(quarterly_tangible_book_value_per_share_3, 1))
-zscore(multiply(ts_arg_max(anl14_high_revenue_fy1, 20), ts_arg_min(anl14_low_ebit_fy4, 20)))
-signed_power(subtract(rank(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))), rank(fnd44_perc_accruals)), 2)
-multiply(ts_std_dev(returns, 20), ts_mean(pv37_volume_14, 20))
-multiply(ts_std_dev(ts_std_dev(returns, 20), 20), ts_std_dev(returns, 20))
-zscore(ts_std_dev(returns, 20))
-zscore(add(divide(fnd72_pit_or_cf_a_cf_net_inc, fnd72_pit_or_is_a_is_net_inc_avail_com_shrhldrs), inverse(ts_rank(fnd44_working_capital_accruals, 36))))
-ts_arg_min(rank(fnd44_working_capital_accruals), 252)
-zscore(quarterly_interest_coverage_ratio)
-rank(subtract(1, divide(fnd44_perc_accruals, ts_mean(fnd72_pit_or_is_q_net_income, 12))))
-subtract(ts_product(ts_zscore(pv37_volume_global, 5), 5), group_mean(ts_product(ts_zscore(pv37_volume_global, 5), 5), 1, industry_grouping_level2_top2000))
-subtract(rank(ts_std_dev(returns, 20)), group_zscore(ts_std_dev(returns, 20), industry_grouping_level2_top1500))
-group_neutralize(ts_covariance(returns, group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), industry_grouping_level2_top2000)
-rank(multiply(ts_covariance(returns, pv173_rawratiosbondreturn20deqwt, 20), sign(ts_delta(anl14_high_revenue_fy2, 5))))
-zscore(ts_corr(anl14_high_ebit_fp4, daily_volume_percent_shares_out, 20))
-subtract(divide(quarterly_free_cash_flow_per_share_2, add(quarterly_net_income_3, 0.01)), fnd44_perc_accruals)
-ts_step(1)
-zscore(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01)))
-ts_backfill(ts_std_dev(returns, 20), 20)
-power(ts_std_dev(returns, 20), 2)
-ts_min(rank(fnd44_perc_accruals), 252)
-multiply(rank(ts_delta(quarterly_pe_ratio_high, 5)), ts_rank(quarterly_price_to_sales_ratio_3, 10))
-vec_avg(ts_std_dev(returns, 20))
-rank(subtract(ts_sum(anl14_high_ebit_fp4, 20), ts_sum(pv173_rawratiosmt5yzspreadchg60d, 20)))
-ts_arg_max(ts_sum(short_term_debt_issued, 30), 60)
-rank(divide(quarterly_net_income_available_to_common_2, quarterly_total_revenue_3))
-normalize(divide(anl69_best_net_gaap, 1))
-is_nan(ts_std_dev(returns, 20))
-subtract(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), group_mean(subtract(ts_max(pv37_volume_global, 5), ts_min(pv37_volume_global, 5)), 1, industry_grouping_level2_top2000))
-subtract(ts_rank(operating_income_2, 20), ts_rank(sales_revenue_total, 20))
-zscore(divide(quarterly_cash_flow_per_share_alt_2, add(net_income_available_to_common_incl_extraordinary_annual, abs(fnd44_perc_accruals))))
-ts_product(ts_std_dev(returns, 20), 20)
-group_neutralize(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), industry_grouping_level2_top2000)
-group_neutralize(ts_corr(pv37_volume_global, returns, 20), industry_grouping_level2_top2000)
-group_neutralize(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), industry_grouping_level2_top2000)
-signed_power(ts_std_dev(returns, 20), 2)
-subtract(ts_arg_max(pv37_volume_global, 10), group_mean(ts_arg_max(pv37_volume_global, 10), 1, industry_grouping_level2_top2000))
-max(ts_std_dev(returns, 20), ts_mean(returns, 20))
-vec_sum(ts_std_dev(returns, 20))
-normalize(divide(quarterly_dividend_per_share, quarterly_earnings_before_tax))
-ts_delta(pv37_volume_14, 5)
-subtract(ts_mean(pv37_volume_global, 20), group_mean(ts_mean(pv37_volume_global, 20), 1, industry_grouping_level2_top2000))
-group_neutralize(ts_std_dev(returns, 20), industry)
-group_neutralize(ts_product(ts_zscore(pv37_volume_global, 5), 5), industry_grouping_level2_top2000)
-ts_delta(rank(fnd44_perc_accruals), 21)
-group_neutralize(ts_arg_max(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), industry_grouping_level2_top2000)
-rank(ts_corr(ts_delta(fnd72_pit_or_bs_q_bs_accrued_income_taxes, 60), ts_delta(fnd72_pit_or_is_q_is_tot_cash_com_dvd, 60), 250))
-zscore(add(divide(fnd72_s_pit_or_cf_q_cf_cash_from_oper, fnd72_s_pit_or_is_q_net_income), inverse(ts_rank(fnd44_working_capital_accruals, 18))))
-ts_corr(ts_std_dev(returns, 20), daily_volume_percent_shares_out, 20)
-subtract(ts_std_dev(returns, 10), ts_std_dev(returns, 60))
-group_neutralize(ts_rank(ts_delta(pv37_volume_global, 3), 20), industry_grouping_level2_top2000)
-ts_mean(returns, 20)
-subtract(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), group_mean(ts_arg_min(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 10), 1, industry_grouping_level2_top2000))
-divide(ts_std_dev(returns, 20), ts_mean(daily_volume_percent_shares_out, 20))
-normalize(ts_covariance(anl14_high_ntp_fy1, daily_volume_percent_shares_out, 20))
-rank(ts_std_dev(returns, 20), 2)
-normalize(subtract(ts_mean(fnd72_pit_or_cf_q_cf_other_non_cash_adj_less, 20), ts_mean(fnd72_s_pit_or_cf_q_cf_incr_cap_stock, 20)))
-subtract(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), group_mean(subtract(ts_mean(pv37_volume_global, 5), ts_mean(pv37_volume_global, 20)), 1, industry_grouping_level2_top2000))
-subtract(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), group_mean(divide(ts_mean(pv37_volume_global, 5), ts_std_dev(pv37_volume_global, 5)), 1, industry_grouping_level2_top2000))
-group_neutralize(sign(ts_delta(pv37_volume_global, 1)), industry_grouping_level2_top2000)
-ts_covariance(ts_std_dev(returns, 20), ts_mean(returns, 20), 20)
-zscore(divide(quarterly_price_to_cash_flow_per_share_2, 1))
-group_neutralize(ts_mean(pv37_volume_global, 20), industry_grouping_level2_top2000)
-group_neutralize(divide(ts_sum(pv37_volume_global, 5), ts_sum(pv37_volume_global, 20)), industry_grouping_level2_top2000)
-multiply(rank(divide(quarterly_free_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), rank(quarterly_current_ratio_alt))
-subtract(abs(ts_delta(log(pv37_volume_global), 5)), group_mean(abs(ts_delta(log(pv37_volume_global), 5)), 1, industry_grouping_level2_top2000))
-rank(ts_covariance(ts_mean(quarterly_cash_flow_per_share_2, 30), ts_mean(quarterly_free_cash_flow_per_share_2, 30), 60))
-rank(quarterly_return_on_assets_percent_2)
-ts_mean(rank(divide(quarterly_cash_flow_per_share_2, add(net_income_quarterly_4, 0.01))), 252)
-hump(ts_std_dev(returns, 20), 0.01)
-rank(divide(quarterly_book_value_per_share_5, 1))
-ts_rank(pv37_volume_global, 20)
-ts_decay_linear(group_zscore(divide(anl14_high_ebit_fp4, anl14_low_ntprep_fp4), industry), 20)
-subtract(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), group_mean(ts_sum(divide(ts_delta(pv37_volume_global, 1), ts_delay(pv37_volume_global, 1)), 10), 1, industry_grouping_level2_top2000))
-rank(ts_corr(ts_mean(quarterly_sga_to_sales_ratio, 20), ts_mean(quarterly_working_capital_per_share_to_price_2, 20), 50))
-group_neutralize(multiply(ts_arg_min(anl14_high_revenue_fy1, 20), ts_delta(pv173_zscoresbondreturn20deqwt, 5)), industry)
-group_scale(ts_std_dev(returns, 20), industry)
-group_neutralize(ts_product(ts_zscore(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), 5), industry_grouping_level2_top2000)
-group_neutralize(multiply(ts_std_dev(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 20), ts_std_dev(returns, 20)), industry_grouping_level2_top2000)
-zscore(subtract(1, divide(fnd44_perc_accruals, ts_mean(quarterly_net_income_available_to_common_2, 24))))
-subtract(ts_std_dev(pv37_volume_global, 20), group_mean(ts_std_dev(pv37_volume_global, 20), 1, industry_grouping_level2_top2000))
-rank(ts_std_dev(quarterly_asset_turnover, 126))
-ts_delta(ts_std_dev(returns, 20), 1)
-ts_corr(returns, ts_backfill(anl14_high_eps_fp1, 250), 20)
-ts_decay_linear(ts_std_dev(returns, 20), 10)
-zscore(fnd31_devnorthamericaadditionalfactor4_apg)
-group_neutralize(ts_scale(multiply(anl14_high_revenue_fp1, fnd31_qsg5additionalfactor3_monthly_pcdwf), 20), industry)
-group_neutralize(ts_av_diff(multiply(anl14_high_eps_fp1, fnd31_devnorthamericaadditionalfactor4_pbdwf), 20), industry)
-subtract(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), group_mean(ts_corr(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), returns, 20), 1, industry_grouping_level2_top2000))
-ts_zscore(group_rank(multiply(ts_delay(returns, 5), daily_volume_percent_shares_out), industry), 20)
-normalize(multiply(ts_count_nans(anl14_high_ebit_fy2, 20), sign(ts_delta(anl14_low_eps_fy5, 5))))
-normalize(quarterly_quick_ratio)
-group_scale(subtract(ts_quantile(daily_volume_percent_shares_out, 20), ts_quantile(anl14_low_ndebt_fy1, 20)), industry)
-ts_zscore(ts_std_dev(returns, 20), 20)
-rank(ts_std_dev(returns, 20))
-group_neutralize(divide(ts_sum(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 5), ts_sum(returns, 5)), industry_grouping_level2_top2000)
-zscore(ts_backfill(anl14_high_revenue_fy2, 250))
-group_neutralize(multiply(ts_rank(anl14_low_ndebt_fy1, 20), ts_rank(fnd31_devnorthamericaadditionalfactor4_pbdwf, 20)), industry)
-inverse(ts_std_dev(returns, 20))
-ts_product(rank(ts_std_dev(returns, 20)), 10)
-zscore(multiply(divide(quarterly_cash_per_share_value, net_income_quarterly_4), inverse(ts_std_dev(quarterly_receivables_turnover_2, 24))))
-group_neutralize(if_else(ts_delta(group_zscore(pv37_volume_global, industry_grouping_level2_top2000), 1) > 0, ts_zscore(returns, 20), ts_zscore(returns, 20) * -1), industry_grouping_level2_top2000)
-rank(quarterly_long_term_debt_to_equity_ratio)
-ts_covariance(returns, resampled_pca_factor_1, 20)
-rank(ts_std_dev(ttm_net_income_value, 252))
-multiply(rank(ts_delta(accounts_receivable_current_assets, 5)), ts_rank(accounts_total_current_assets, 10))
-multiply(ts_std_dev(returns, 20), ts_corr(returns, ts_delay(returns, 1), 20))
-multiply(sign(ts_delta(fnd72_pit_or_is_q_net_income, 10)), ts_rank(fnd72_pit_or_is_q_is_invest_income, 30))
-multiply(rank(ts_std_dev(returns, 20)), rank(daily_volume_to_shares_outstanding))
-ts_arg_max(rank(divide(quarterly_cash_flow_per_share_2, add(quarterly_earnings_before_tax_5, 0.01))), 252)
-subtract(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), group_mean(divide(ts_delay(pv37_volume_global, 5), ts_delay(pv37_volume_global, 10)), 1, industry_grouping_level2_top2000))
-zscore(anl14_low_roe_fy2)
-ts_backfill(add(ts_product(anl14_high_eps_fp1, 10), ts_product(anl14_low_roe_fy1, 10)), 20)
-subtract(sign(ts_delta(pv37_volume_global, 1)), group_mean(sign(ts_delta(pv37_volume_global, 1)), 1, industry_grouping_level2_top2000))
-add(rank(ts_std_dev(returns, 10)), add(rank(ts_std_dev(returns, 20)), rank(ts_std_dev(returns, 40))))
-jump_decay(ts_std_dev(returns, 20), 20, 0.5, 0.1)
-subtract(ts_std_dev(returns, 20), ts_std_dev(returns, 10))
-power(ts_sum(returns, 20), 2)
-group_scale(add(ts_av_diff(industry_grouping_level2_top1500, 20), ts_av_diff(anl14_low_ntprep_fp4, 20)), industry)
-rank(ts_covariance(ts_mean(long_term_liabilities_total_3, 30), ts_mean(long_term_minority_interest, 30), 60))
-ts_backfill(group_neutralize(divide(anl14_high_nav_fy2, anl14_low_ndebt_fy5), industry), 20)
-ts_delta(ts_std_dev(returns, 20), 5)
-sqrt(ts_std_dev(returns, 20))
-rank(add(divide(quarterly_free_cash_flow, quarterly_total_assets_change_percent), inverse(fnd44_working_capital_accruals)))
-ts_arg_min(ts_sum(fnd31_qsg5additionalfactor3_monthly_tw_ebitdaev, 30), 60)
-subtract(ts_rank(pv37_high_global2, 20), group_mean(ts_rank(pv37_high_global2, 20), 1, industry_grouping_level2_top2000))
-zscore(multiply(ts_quantile(fnd31_qsg5additionalfactor3_monthly_pcdwf, 20, "uniform"), inverse(ts_mean(anl14_low_ndebt_fy1, 20))))
-add(zscore(anl14_high_revenue_fy2), zscore(anl14_high_ebit_fy2))

prepare_prompt/alpha_prompt.txt

@@ -1,9 +1,8 @@
-研究因子名称
+Tail Liquidity Premium Factor
-盈利质量溢价因子
+Hypothesis
-假设
+During periods of market stress, small-cap or low-liquidity stocks often experience excessive discounting due to concentrated selling by investors, yet they tend to exhibit stronger subsequent rebounds compared to large-cap stocks—manifesting a "tail liquidity premium." This phenomenon stems from institutional aversion to liquidity risk and retail-driven irrational trading under extreme sentiment, creating short-term mispricing that offers opportunities for contrarian strategies.
-高质量的盈利通常表现为持续性、可预测性和较低的应计成分，这类公司往往具备更强的经营现金流支撑和更低的财务操纵风险。根据行为金融学中的“代表性启发”偏差，市场投资者容易过度关注账面利润而忽视盈利构成的质量，导致高质量盈利公司被系统性低估，从而在中长期产生超额收益。
+Implementation
-实施方案
+Using daily trading data from the past 20 days, select the bottom 10% of stocks by turnover ratio across the market. Compute the volatility of their excess returns relative to industry averages. The final factor score is the product of this volatility and the magnitude of recent price drawdown, designed to identify oversold tail assets with rebound potential.
-基于财报数据，构建一个综合盈利质量指标：首先计算应计利润占总利润的比例（应计比率），再结合经营性现金流与净利润的比值、盈利波动率以及资产周转稳定性等维度进行标准化加权，最终形成横截面上的排序因子。该因子值越高，代表公司盈利质量越好，预期未来收益越高。 
 
 
 *=========================================================================================*

prepare_prompt/keys_text.txt

@@ -1,4 +1,5 @@
-["accrual", "cashflow", "earnings", "quality", "profitability", "stability", "volatility", "turnover", "ratio", "persistence"]
+Tail Liquidity Premium Factor
-Earnings Quality Premium Factor
+Hypothesis
-High-quality earnings are typically characterized by persistence, predictability, and low accrual components, reflecting stronger operating cash flow support and lower risk of financial manipulation. Behavioral finance suggests that investors often suffer from representativeness bias—overemphasizing headline earnings while neglecting the underlying quality—leading to systematic underpricing of firms with high earnings quality and thus generating long-term abnormal returns.
+During periods of market stress, small-cap or low-liquidity stocks often experience excessive discounting due to concentrated selling by investors, yet they tend to exhibit stronger subsequent rebounds compared to large-cap stocks—manifesting a "tail liquidity premium." This phenomenon stems from institutional aversion to liquidity risk and retail-driven irrational trading under extreme sentiment, creating short-term mispricing that offers opportunities for contrarian strategies.
-Construct a composite earnings quality metric using financial statement data: first compute the ratio of accruals to total earnings, then integrate metrics such as operating cash flow to net income, earnings volatility, and asset turnover stability. Standardize and weight these components to form a cross-sectional ranking factor. Higher factor values indicate better earnings quality and are expected to predict higher future returns.
+Implementation
+Using daily trading data from the past 20 days, select the bottom 10% of stocks by turnover ratio across the market. Compute the volatility of their excess returns relative to industry averages. The final factor score is the product of this volatility and the magnitude of recent price drawdown, designed to identify oversold tail assets with rebound potential.