任务指令
你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
核心规则
设计维度框架
维度1：时间序列动量（TM）
核心概念：捕捉行业价格的趋势、动量和形态变化
设计思路：
动量的变化率、加速度或平滑度构建
动量衰减或增强模式识别
价格与成交量关系的时序分析
维度2：横截面领导力（CL）
核心概念：识别行业内部的分化、龙头效应和相对强度
bucket（用于龙头股筛选）
设计思路：
行业内部龙头股与平均表现的差异
行业成分股的离散度分析
相对排名的变化和稳定性
维度3：市场状态适应性（MS）
核心概念：根据市场环境动态调整因子逻辑
设计思路：
波动率调整的动量指标
不同市场状态（高/低波动）使用不同的回顾期
条件逻辑下的参数动态调整
维度4：行业间联动（IS）
多序列相关性分析
设计思路：
领先-滞后行业的相关性分析
行业间动量传导效应
板块轮动的早期信号识别
维度5：交易行为情绪（TS）
核心概念：基于交易行为和情绪指标的反转信号
设计思路：
超买超卖状态识别
交易拥挤度指标
情绪极端值后的均值回归
复合因子设计原则
强制要求：
每个表达式必须融合至少两个设计维度
必须使用提供的操作符列表中的函数
因子应具有经济逻辑解释性
推荐组合模式：
TM + CL：时序动量 + 横截面领导力
示例：行业动量加速度 × 龙头股相对强度
TM + MS：时序动量 + 状态适应性
示例：波动率调整后的动量指标
CL + IS：横截面 + 行业间联动
示例：龙头股表现与相关行业的领先滞后关系
MS + TS：状态适应 + 交易情绪
示例：不同市场状态下的反转信号
IS + TS：行业联动 + 交易情绪
示例：行业间相关性变化与交易拥挤度
参数化建议：
使用不同的时间窗口组合（短/中/长周期）
尝试不同的权重分配方式
考虑非线性变换（log, power, sqrt）
使用条件逻辑增强鲁棒性
表达式构建指南
基本结构：
text
复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]

操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
操作符使用策略：
算术运算：abs(x), add(x, y, filter = false), densify(x), divide(x, y), inverse(x), max(x, y, ..), min(x, y ..), multiply(x ,y, ... , filter=false), power(x, y), reverse(x), sign(x), signed_power(x, y), sqrt(x), subtract(x, y, filter=false)
条件逻辑：and(input1, input2), if_else(input1, input2, input 3), input1 < input2, input1 <= input2, input1 == input2, input1 > input2, input1 >= input2, input1!= input2, is_nan(input), not(x), or(input1, input2)
时间序列操作：days_from_last_change(x), hump(x, hump = 0.01), kth_element(x, d, k), last_diff_value(x, d), ts_arg_max(x, d), ts_arg_min(x, d), ts_av_diff(x, d), ts_backfill(x,lookback = d, k=1, ignore="NAN"), ts_corr(x, y, d), ts_count_nans(x ,d), ts_covariance(y, x, d), ts_decay_linear(x, d, dense = false), ts_delay(x, d), ts_delta(x, d), ts_mean(x, d), ts_product(x, d), "ts_quantile(x,d, driver=""gaussian"" )", ts_rank(x, d, constant = 0), ts_regression(y, x, d, lag = 0, rettype = 0), ts_scale(x, d, constant = 0), ts_std_dev(x, d), ts_step(1), ts_sum(x, d), ts_zscore(x, d)
横截面操作: normalize(x, useStd = false, limit = 0.0), quantile(x, driver = gaussian, sigma = 1.0), rank(x, rate=2), scale(x, scale=1, longscale=1, shortscale=1), winsorize(x, std=4), zscore(x)
向量操作符：vec_avg(x), vec_sum(x)
转换操作符: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10"), trade_when(x, y, z)
聚合操作符: group_backfill(x, group, d, std = 4.0), group_mean(x, weight, group), group_neutralize(x, group), group_rank(x, group), group_scale(x, group), group_zscore(x, group), subtract(x, y, filter=false), multiply(x ,y, ... , filter=false), divide(x, y), add(x, y, filter = false)
*=====*
注意事项:
避免过度复杂的嵌套
使用经济直觉验证逻辑合理性
考虑实际交易可行性
包含风险控制元素（如波动率调整）
*=====*

参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现“行业”逻辑。
输出格式：
输出必须是且仅是 100行纯文本。
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。
示例思维（仅供理解，不输出）
一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
**输出格式**(一行一个表达式, 只要表达式本身, 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

请提供具体的WQ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, 进行生成,组合因子

========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
Description: 此字段后面的是操作符对应的描述或使用说明, 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: DataField: 后面的是数据字段, DataFieldDescription: 此字段后面的是数据字段对应的描述或使用说明, DataFieldDescription字段后面的内容是使用说明, 不是数据字段

DataField: forward_price_120
DataFieldDescription: Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: put_breakeven_60
DataFieldDescription: Price at which a stock's put options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_150
DataFieldDescription: Forward price at 150 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: option_breakeven_60
DataFieldDescription: Price at which a stock's options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_90
DataFieldDescription: Price at which a stock's put options with expiration 90 days in the future break even based on its recent bid/ask mean.
DataField: call_breakeven_60
DataFieldDescription: Price at which a stock's call options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_1080
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
DataField: put_breakeven_30
DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_1080
DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: forward_price_180
DataFieldDescription: Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.
DataField: pcr_vol_30
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.
DataField: put_breakeven_180
DataFieldDescription: Price at which a stock's put options with expiration 180 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_90
DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_60
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
DataField: option_breakeven_30
DataFieldDescription: Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_1080
DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_270
DataFieldDescription: Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.
DataField: call_breakeven_20
DataFieldDescription: Price at which a stock's call options with expiration 20 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_720
DataFieldDescription: Price at which a stock's put options with expiration 720 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_30
DataFieldDescription: Forward price at 30 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: forward_price_360
DataFieldDescription: Forward price at 360 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: option_breakeven_360
DataFieldDescription: Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_120
DataFieldDescription: Price at which a stock's put options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_20
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
DataField: call_breakeven_1080
DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_120
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
DataField: forward_price_720
DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: put_breakeven_360
DataFieldDescription: Price at which a stock's put options with expiration 360 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_120
DataFieldDescription: Price at which a stock's options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_120
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 120 days in the future.
DataField: cashflow
DataFieldDescription: Cashflow (Annual)
DataField: fnd6_newqv1300_cstkq
DataFieldDescription: Common/Ordinary Stock (Capital)
DataField: fnd6_newqeventv110_setpq
DataFieldDescription: Settlement (Litigation/Insurance) Pretax
DataField: cash
DataFieldDescription: Cash
DataField: fnd6_niadj
DataFieldDescription: Net Income Adjusted for Common/Ordinary Stock (Capital) Equivalents
DataField: fnd6_eventv110_nrtxtepsq
DataFieldDescription: Nonrecurring Income Taxes Basic EPS Effect
DataField: cash_st
DataFieldDescription: Cash and Short-Term Investments
DataField: fnd6_pstkc
DataFieldDescription: Preferred Stock - Convertible
DataField: fnd6_nopio
DataFieldDescription: Nonoperating Income (Expense) - Other
DataField: fnd6_newa2v1300_tstkn
DataFieldDescription: Treasury Stock - Number of Common Shares
DataField: equity
DataFieldDescription: Common/Ordinary Equity - Total
DataField: fnd6_newa2v1300_rect
DataFieldDescription: Receivables - Total
DataField: fnd6_newa1v1300_ao
DataFieldDescription: Assets - Other
DataField: fnd6_cptnewqv1300_saleq
DataFieldDescription: Sales/Turnover (Net)
DataField: fnd6_eventv110_pncd12
DataFieldDescription: Core Pension Adjustment Diluted EPS Effect 12MM
DataField: fnd6_newa2v1300_re
DataFieldDescription: Retained Earnings
DataField: fnd6_newa2v1300_spi
DataFieldDescription: Special Items
DataField: fnd6_cptnewqv1300_ltq
DataFieldDescription: Liabilities - Total
DataField: fnd6_drlt
DataFieldDescription: Deferred Revenue - Long-term
DataField: fnd6_mfma1_csho
DataFieldDescription: Common Shares Outstanding
DataField: fnd6_newqv1300_cogsq
DataFieldDescription: Cost of Goods Sold
DataField: fnd6_xintopt
DataFieldDescription: Implied Option Expense
DataField: fnd6_newqv1300_acomincq
DataFieldDescription: Accumulated Other Comprehensive Income (Loss)
DataField: fnd6_newqeventv110_txditcq
DataFieldDescription: Deferred Taxes and Investment Tax Credit
DataField: fnd6_newqeventv110_cshprq
DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - Basic
DataField: fnd6_newa1v1300_invt
DataFieldDescription: Inventories - Total
DataField: sales_growth
DataFieldDescription: Growth in Sales (Quarterly)
DataField: fnd6_newqeventv110_pncwiapq
DataFieldDescription: Core Pension w/o Interest Adjustment After-tax Preliminary
DataField: fnd6_newqeventv110_spceepspq
DataFieldDescription: S&P Core Earnings EPS Basic - Preliminary
DataField: fnd6_newa2v1300_pi
DataFieldDescription: Pretax Income
DataField: scl12_alltype_buzzvec
DataFieldDescription: sentiment volume
DataField: scl12_alltype_sentvec
DataFieldDescription: sentiment
DataField: scl12_alltype_typevec
DataFieldDescription: instrument type index
DataField: scl12_buzz
DataFieldDescription: relative sentiment volume
DataField: scl12_buzz_fast_d1
DataFieldDescription: relative sentiment volume
DataField: scl12_buzzvec
DataFieldDescription: sentiment volume
DataField: scl12_sentiment
DataFieldDescription: sentiment
DataField: scl12_sentiment_fast_d1
DataFieldDescription: sentiment
DataField: scl12_sentvec
DataFieldDescription: sentiment
DataField: scl12_typevec
DataFieldDescription: instrument type index
DataField: snt_buzz
DataFieldDescription: negative relative sentiment volume, fill nan with 0
DataField: snt_buzz_bfl
DataFieldDescription: negative relative sentiment volume, fill nan with 1
DataField: snt_buzz_bfl_fast_d1
DataFieldDescription: negative relative sentiment volume, fill nan with 1
DataField: snt_buzz_fast_d1
DataFieldDescription: negative relative sentiment volume, fill nan with 0
DataField: snt_buzz_ret
DataFieldDescription: negative return of relative sentiment volume
DataField: snt_buzz_ret_fast_d1
DataFieldDescription: negative return of relative sentiment volume
DataField: snt_value
DataFieldDescription: negative sentiment, fill nan with 0
DataField: snt_value_fast_d1
DataFieldDescription: negative sentiment, fill nan with 0
DataField: analyst_revision_rank_derivative
DataFieldDescription: Change in ranking for analyst revisions and momentum compared to previous period.
DataField: cashflow_efficiency_rank_derivative
DataFieldDescription: Change in ranking for cash flow generation and profitability compared to previous period.
DataField: composite_factor_score_derivative
DataFieldDescription: Change in overall composite factor score from the prior period.
DataField: earnings_certainty_rank_derivative
DataFieldDescription: Change in ranking for earnings sustainability and certainty compared to previous period.
DataField: fscore_bfl_growth
DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
DataField: fscore_bfl_momentum
DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
DataField: fscore_bfl_profitability
DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
DataField: fscore_bfl_quality
DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
DataField: fscore_bfl_surface
DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
DataField: fscore_bfl_surface_accel
DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
DataField: fscore_bfl_total
DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
DataField: fscore_bfl_value
DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
DataField: fscore_growth
DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
DataField: fscore_momentum
DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
DataField: fscore_profitability
DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
DataField: fscore_quality
DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
DataField: fscore_surface
DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
DataField: fscore_surface_accel
DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
DataField: fscore_total
DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
DataField: fscore_value
DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
DataField: growth_potential_rank_derivative
DataFieldDescription: Change in ranking for medium-term growth potential compared to previous period.
DataField: multi_factor_acceleration_score_derivative
DataFieldDescription: Change in the acceleration of multi-factor score compared to previous period.
DataField: multi_factor_static_score_derivative
DataFieldDescription: Change in static multi-factor score compared to previous period.
DataField: relative_valuation_rank_derivative
DataFieldDescription: Change in ranking for valuation metrics compared to previous period.
DataField: snt_social_value
DataFieldDescription: Z score of sentiment
DataField: snt_social_volume
DataFieldDescription: Normalized tweet volume
DataField: beta_last_30_days_spy
DataFieldDescription: Beta to SPY in 30 Days
DataField: beta_last_360_days_spy
DataFieldDescription: Beta to SPY in 360 Days
DataField: beta_last_60_days_spy
DataFieldDescription: Beta to SPY in 60 Days
DataField: beta_last_90_days_spy
DataFieldDescription: Beta to SPY in 90 Days
DataField: correlation_last_30_days_spy
DataFieldDescription: Correlation to SPY in 30 Days
DataField: correlation_last_360_days_spy
DataFieldDescription: Correlation to SPY in 360 Days
DataField: correlation_last_60_days_spy
DataFieldDescription: Correlation to SPY in 60 Days
DataField: correlation_last_90_days_spy
DataFieldDescription: Correlation to SPY in 90 Days
DataField: systematic_risk_last_30_days
DataFieldDescription: Systematic Risk Last 30 Days
DataField: systematic_risk_last_360_days
DataFieldDescription: Systematic Risk Last 360 Days
DataField: systematic_risk_last_60_days
DataFieldDescription: Systematic Risk Last 60 Days
DataField: systematic_risk_last_90_days
DataFieldDescription: Systematic Risk Last 90 Days
DataField: unsystematic_risk_last_30_days
DataFieldDescription: Unsystematic Risk Last 30 Days - Relative to SPY
DataField: unsystematic_risk_last_360_days
DataFieldDescription: Unsystematic Risk Last 360 Days - Relative to SPY
DataField: unsystematic_risk_last_60_days
DataFieldDescription: Unsystematic Risk Last 60 Days - Relative to SPY
DataField: unsystematic_risk_last_90_days
DataFieldDescription: Unsystematic Risk Last 90 Days - Relative to SPY
DataField: anl4_cuo1actualqfv110_actual
DataFieldDescription: Announced financial data
DataField: anl4_af_cfps_value
DataFieldDescription: Cash Flow Per Share - Actual Value
DataField: anl4_eaz1laf_person
DataFieldDescription: Broker Id
DataField: funds_from_operations_max_guidance
DataFieldDescription: The maximum guidance value for Funds from operation - annual
DataField: est_ebitda
DataFieldDescription: Earnings before interest, taxes, depreciation, and amortization - mean of estimations
DataField: anl4_ptp_high
DataFieldDescription: Pretax income - the highest estimation
DataField: anl4_dez1basicqfv4_preest
DataFieldDescription: The previous estimation of finanicial item
DataField: anl4_basicconafv110_pu
DataFieldDescription: The number of upper estimations
DataField: anl4_adxqfv110_down
DataFieldDescription: Number of lower estimations
DataField: anl4_bac1actualqfv110_item
DataFieldDescription: Financial item
DataField: anl4_fcf_low
DataFieldDescription: Free Cash Flow - The lowest estimation
DataField: min_free_cash_flow_per_share_guidance
DataFieldDescription: Free cash flow per share - minimum guidance value for the annual period
DataField: anl4_af_div_value
DataFieldDescription: Dividend - Actual value
DataField: min_net_income_guidance
DataFieldDescription: Net profit - minimum guidance value
DataField: anl4_cuo1detailqfv110_item
DataFieldDescription: Financial item
DataField: anl4_basicdetailqfv110_prevval
DataFieldDescription: The previous estimation of financial item
DataField: min_total_goodwill_guidance
DataFieldDescription: Total Goodwill - The lowest guidance value
DataField: sales_estimate_median_quarterly
DataFieldDescription: Sales - median of estimations
DataField: anl4_ads1detailqfv110_prevval
DataFieldDescription: The previous estimation of financial item
DataField: min_gross_income_guidance
DataFieldDescription: The minimum guidance value for Gross Income.
DataField: anl4_cuo1guidaf_item
DataFieldDescription: Financial item
DataField: anl4_bvps_value
DataFieldDescription: Book value per share - announced financial value
DataField: anl4_qfv4_cfps_high
DataFieldDescription: Cash Flow Per Share - The highest estimation for the quarter
DataField: maximum_guidance_value
DataFieldDescription: Maximum guidance value for basic annual financials
DataField: max_reported_eps_guidance_2
DataFieldDescription: Reported Earnings Per Share - Maximum guidance value for the annual period
DataField: anl4_dez1safv4_preest
DataFieldDescription: The previous estimation of finanicial item
DataField: anl4_cfo_median
DataFieldDescription: Cash Flow From Operations - median of estimations
DataField: anl4_netprofit_std
DataFieldDescription: Net profit - standard deviation of estimations
DataField: anl4_qf_az_eps_number
DataFieldDescription: Earnings per share - number of estimations
DataField: anl4_basicconafv110_high
DataFieldDescription: The highest estimation
DataField: pv13_revere_parent
DataFieldDescription: Code of parent sector
DataField: pv13_hierarchy_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min51_f3_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_hub_rank
DataFieldDescription: the HITS hub score of customers
DataField: pv13_h_min20_top3000_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_auth_rank
DataFieldDescription: the HITS authority score of customers
DataField: rel_ret_cust
DataFieldDescription: averaged one-day-return of the instrument's customers
DataField: pv13_h_min10_top3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rcsed_6l
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_only_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy23_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h2_min2_1k_sector
DataFieldDescription: Grouping fields for top 1000
DataField: pv13_new_5l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_1k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_key_sector_total
DataFieldDescription: Number of key focus sectors for the company
DataField: pv13_hierarchy_min10_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min2_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min2_focused_pureplay_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_region
DataFieldDescription: Unique code of the region
DataField: rel_ret_part
DataFieldDescription: Averaged one-day return of the instrument's partners
DataField: pv13_h_min24_500_sector
DataFieldDescription: Grouping fields for top 500
DataField: rel_ret_all
DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
DataField: pv13_rha2_min30_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy2_min2_1k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f1_513_sector
DataFieldDescription: grouping fields
DataField: parkinson_volatility_30
DataFieldDescription: Parkinson model's historical volatility over 30 days
DataField: implied_volatility_call_120
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
DataField: implied_volatility_call_1080
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
DataField: implied_volatility_put_360
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
DataField: implied_volatility_call_30
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
DataField: implied_volatility_put_90
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
DataField: implied_volatility_put_1080
DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
DataField: historical_volatility_20
DataFieldDescription: Close-to-close Historical volatility over 20 days
DataField: implied_volatility_call_60
DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
DataField: implied_volatility_mean_skew_90
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
DataField: historical_volatility_30
DataFieldDescription: Close-to-close Historical volatility over 30 days
DataField: historical_volatility_120
DataFieldDescription: Close-to-close Historical volatility over 120 days
DataField: parkinson_volatility_60
DataFieldDescription: Parkinson model's historical volatility over 60 days
DataField: parkinson_volatility_180
DataFieldDescription: Parkinson model's historical volatility over 180 days
DataField: implied_volatility_mean_150
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
DataField: implied_volatility_mean_skew_30
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
DataField: implied_volatility_mean_270
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: implied_volatility_mean_skew_60
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
DataField: implied_volatility_mean_skew_10
DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
DataField: implied_volatility_mean_skew_150
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
DataField: implied_volatility_call_90
DataFieldDescription: At-the-money option-implied volatility for call Option for 90 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: implied_volatility_call_360
DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
DataField: implied_volatility_mean_30
DataFieldDescription: At-the-money option-implied volatility mean for 30 days
DataField: implied_volatility_mean_120
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
DataField: implied_volatility_mean_90
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
DataField: implied_volatility_mean_skew_180
DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
DataField: implied_volatility_mean_skew_360
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
DataField: parkinson_volatility_150
DataFieldDescription: Parkinson model's historical volatility over 150 days
DataField: nws12_afterhsz_60_min
DataFieldDescription: The percent change in price in the first 60 minutes following the news release
DataField: nws12_afterhsz_57l
DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
DataField: nws12_prez_1s
DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
DataField: nws12_afterhsz_41rta
DataFieldDescription: 14-day Average True Range
DataField: nws12_prez_prevclose
DataFieldDescription: Previous trading day's close price
DataField: nws12_mainz_2l
DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
DataField: nws12_prez_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: nws12_prez_range
DataFieldDescription: Session High Price - Session Low Price) / Session Low Price.
DataField: nws12_afterhsz_result_vs_index
DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
DataField: nws12_mainz_newrecord
DataFieldDescription: Tracks whether the news is first instance or a duplicate
DataField: nws12_afterhsz_02l
DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
DataField: news_mins_2_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
DataField: news_pe_ratio
DataFieldDescription: Reported price-to-earnings ratio for the calendar day of the session
DataField: news_max_dn_ret
DataFieldDescription: Percent change from the price at the time of the news to the after the news low
DataField: nws12_prez_curr_vol
DataFieldDescription: Current day's session volume
DataField: nws12_afterhsz_120_min
DataFieldDescription: The percent change in price in the first 120 minutes following the news release
DataField: nws12_prez_57s
DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
DataField: news_mins_1_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_mainz_prevclose
DataFieldDescription: Previous trading day's close price
DataField: news_dividend_yield
DataFieldDescription: Annual yield
DataField: news_prev_vol
DataFieldDescription: Previous day's session volume
DataField: nws12_prez_vol_ratio
DataFieldDescription: Curr_Vol / Mov_Vol
DataField: nws12_mainz_tonhigh
DataFieldDescription: Highest price reached during the session before the time of news
DataField: nws12_prez_1_minute
DataFieldDescription: The percent change in price in the first minute following the news release
DataField: nws12_afterhsz_tonhigh
DataFieldDescription: Highest price reached during the session before the time of news
DataField: nws12_afterhsz_result2
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
DataField: news_session_range_pct
DataFieldDescription: (Session High Price - Session Low Price) / Session Low Price.
DataField: news_pct_5_min
DataFieldDescription: The percent change in price in the first 5 minutes following the news release
DataField: nws12_mainz_mktcap
DataFieldDescription: Reported market capitalization for the calendar day of the session
DataField: nws12_afterhsz_eodhigh
DataFieldDescription: Highest price reached between the time of news and the end of the session
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: nws18_event_relevance
DataFieldDescription: Relevance of the event to the story
DataField: rp_css_society
DataFieldDescription: Composite sentiment score of society-related news
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: rp_css_labor
DataFieldDescription: Composite sentiment score of labor issues news
DataField: rp_css_partner
DataFieldDescription: Composite sentiment score of partnership news
DataField: rp_css_product
DataFieldDescription: Composite sentiment score of product and service-related news
DataField: rp_css_credit_ratings
DataFieldDescription: Composite sentiment score of credit ratings news
DataField: rp_css_marketing
DataFieldDescription: Composite sentiment score of marketing news
DataField: rp_ess_ptg
DataFieldDescription: Event sentiment score of price target news
DataField: rp_ess_business
DataFieldDescription: Event sentiment score of business-related news
DataField: rp_nip_mna
DataFieldDescription: News impact projection of mergers and acquisitions-related news
DataField: rp_css_insider
DataFieldDescription: Composite sentiment score of insider trading news
DataField: rp_nip_ptg
DataFieldDescription: News impact projection of price target news
DataField: nws18_bam
DataFieldDescription: News sentiment specializing in mergers and acquisitions
DataField: nws18_qcm
DataFieldDescription: News sentiment of relevant news with high confidence
DataField: nws18_event_similarity_days
DataFieldDescription: Days since a similar event was detected
DataField: rp_css_revenue
DataFieldDescription: Composite sentiment score of revenue news
DataField: rp_ess_credit_ratings
DataFieldDescription: Event sentiment score of credit ratings news
DataField: rp_ess_legal
DataFieldDescription: Event sentiment score of legal news
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: rp_css_assets
DataFieldDescription: Composite sentiment score of assets news
DataField: rp_css_ratings
DataFieldDescription: Composite sentiment score of analyst ratings-related news
DataField: rp_nip_credit
DataFieldDescription: News impact projection of credit news
DataField: rp_css_equity
DataFieldDescription: Composite sentiment score of equity action news
DataField: rp_ess_revenue
DataFieldDescription: Event sentiment score of revenue news
DataField: rp_ess_technical
DataFieldDescription: Event sentiment score based on technical analysis
DataField: rp_nip_business
DataFieldDescription: News impact projection of business-related news
DataField: rp_css_credit
DataFieldDescription: Composite sentiment score of credit news
DataField: rp_css_inverstor
DataFieldDescription: Composite sentiment score of investor relations news
DataField: rp_nip_inverstor
DataFieldDescription: News impact projection of investor relations news
DataField: fnd2_a_frtandfixturesg
DataFieldDescription: Amount before accumulated depreciation of equipment commonly used in offices and stores that have no permanent connection to the structure of a building or utilities. Examples include, but are not limited to, desks, chairs, tables, and bookcases.
DataField: fnd2_a_sbcpnargmpmtwstgm
DataFieldDescription: As of the balance sheet date, the number of shares into which fully vested and expected to vest stock options outstanding can be converted under the option plan.
DataField: fn_business_combination_assets_aquired_goodwill_a
DataFieldDescription: Business Combination, Portion of Purchase Price Allocated to Goodwill
DataField: fn_comp_options_forfeitures_and_expirations_q
DataFieldDescription: For presentations that combine terminations, the number of shares under options that were cancelled during the reporting period as a result of occurrence of a terminating event specified in contractual agreements pertaining to the stock option plan or that expired.
DataField: fnd2_dbplanepdfbnfpy5
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_comp_options_grants_weighted_avg_a
DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options that were terminated.
DataField: fnd2_a_stkrpeprogramardamt
DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
DataField: fn_interest_paid_net_q
DataFieldDescription: Net interest
DataField: fn_comp_options_exercisable_number_q
DataFieldDescription: The number of shares into which fully or partially vested stock options outstanding as of the balance sheet date can be currently converted under the option plan.
DataField: fn_def_tax_assets_liab_net_q
DataFieldDescription: Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.
DataField: fnd2_ebitdm
DataFieldDescription: EBIT, Domestic
DataField: fnd2_dbplanepdfbnfpythree
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 3rd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fnd2_a_flintasamt1expyfour
DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_liab_fair_val_q
DataFieldDescription: Liabilities Fair Value, Recurring, Total
DataField: fnd2_q_flintasamt1expyfour
DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fnd2_sbcpnshardpreops
DataFieldDescription: Share-based compensation shares authorized under stock option plans exercise price range number of exercisable options
DataField: fnd2_a_inventoryfinishedgoods
DataFieldDescription: Amount before valuation and LIFO reserves of completed merchandise or goods expected to be sold within 1 year or operating cycle, if longer.
DataField: fnd2_dbplanbnfol
DataFieldDescription: 1) For defined benefit pension plans, the benefit obligation is the projected benefit obligation, which is the actuarial present value as of a date of all benefits attributed by the pension benefit formula to employee service rendered prior to that date. 2) For other postretirement defined benefit plans, the benefit obligation is the accumulated postretirement benefit obligation, which is the actuarial present value of benefits attributed to employee service rendered to a particular date.
DataField: fn_effect_of_exchange_rate_on_cash_and_equiv_a
DataFieldDescription: Amount of increase (decrease) from the effect of exchange rate changes on cash and cash equivalent balances held in foreign currencies.
DataField: fnd2_a_dbplanservicecst
DataFieldDescription: The actuarial present value of benefits attributed by the pension benefit formula to services rendered by employees during the period. The portion of the expected postretirement benefit obligation attributed to employee service during the period. The service cost component is a portion of the benefit obligation and is unaffected by the funded status of the plan.
DataField: fnd2_a_flintasamt1expytwo
DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date
DataField: fn_debt_instrument_interest_rate_stated_percentage_a
DataFieldDescription: Stated percentage of interest rate on debt
DataField: fnd2_propplteqmuflmblgland
DataFieldDescription: PPE, Buildings & land, Useful Life, Minimum
DataField: fnd2_a_sbcpnargmsawpfipwerpr
DataFieldDescription: Weighted average price of options that were either forfeited or expired.
DataField: fn_liab_fair_val_l2_a
DataFieldDescription: Liabilities Fair Value, Recurring, Level 2
DataField: fn_payments_for_repurchase_of_common_stock_a
DataFieldDescription: Value reported on Cash Flow Statement. May include shares repurchased as part of a buyback plan, as well as shares purchased for employee compensation, etc.
DataField: fn_avg_diluted_sharesout_adj_a
DataFieldDescription: The sum of dilutive potential common shares or units used in the calculation of the diluted per-share or per-unit computation.
DataField: fn_employee_related_liab_q
DataFieldDescription: Total of the carrying values as of the balance sheet date of obligations incurred through that date and payable for obligations related to services received from employees, such as accrued salaries and bonuses, payroll taxes and fringe benefits. For classified balance sheets, used to reflect the current portion of the liabilities (due within 1 year or within the normal operating cycle if longer); for unclassified balance sheets, used to reflect the total liabilities (regardless of due date).
DataField: fn_debt_instrument_interest_rate_stated_percentage_q
DataFieldDescription: Stated percentage of interest rate on debt
DataField: fn_accum_oth_income_loss_fx_adj_net_of_tax_q
DataFieldDescription: Accumulated adjustment, net of tax, that results from the process of translating subsidiary financial statements and foreign equity investments into the reporting currency from the functional currency of the reporting entity, net of reclassification of realized foreign currency translation gains or losses.
DataField: adv20
DataFieldDescription: Average daily volume in past 20 days
DataField: cap
DataFieldDescription: Daily market capitalization (in millions)
DataField: close
DataFieldDescription: Daily close price
DataField: country
DataFieldDescription: Country grouping
DataField: currency
DataFieldDescription: Currency
DataField: cusip
DataFieldDescription: CUSIP Value
DataField: dividend
DataFieldDescription: Dividend
DataField: exchange
DataFieldDescription: Exchange grouping
DataField: high
DataFieldDescription: Daily high price
DataField: industry
DataFieldDescription: Industry grouping
DataField: isin
DataFieldDescription: ISIN Value
DataField: low
DataFieldDescription: Daily low price
DataField: market
DataFieldDescription: Market grouping
DataField: open
DataFieldDescription: Daily open price
DataField: returns
DataFieldDescription: Daily returns
DataField: sector
DataFieldDescription: Sector grouping
DataField: sedol
DataFieldDescription: Sedol
DataField: sharesout
DataFieldDescription: Daily outstanding shares (in millions)
DataField: split
DataFieldDescription: Stock split ratio
DataField: subindustry
DataFieldDescription: Subindustry grouping
DataField: ticker
DataFieldDescription: Ticker
DataField: volume
DataFieldDescription: Daily volume
DataField: vwap
DataFieldDescription: Daily volume weighted average price
========================= 数据字段结束 =======================================