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）
目标：识别价格趋势的强度、速度和持续性
可用的具体构建方法：
1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
3. 动量加速度：ts_delta(ts_delta(close, d1), d2) [避免嵌套ts_regression]
4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
5. 动量衰减：ts_decay_linear(returns, d)
6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
建议组合：使用不同d参数创建短期/中期/长期动量
维度2：横截面领导力（CL）
目标：识别行业内的龙头股和相对强度
具体构建方法：
1. 龙头股筛选：if_else(rank(volume) > 0.7, 龙头值, 其他值) [使用volume代替market_cap]
2. 龙头组合：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4")) [使用volume排序]
3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
4. 相对排名稳定性：ts_mean(rank(returns), d)
维度3：市场状态适应性（MS）
目标：根据波动率、趋势状态调整参数
具体构建方法：
1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
2. 状态条件选择：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
3. 参数动态化：if_else(ts_std_dev(returns,20) > 阈值, 5, 20) [作为d参数]
4. 趋势状态识别：ts_rank(ts_mean(returns,20), 60) > 0.5
基本结构：
复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
=== 关键语法规则（必须遵守） ===
1. 数据字段规范：
   - 可使用字段：close, volume, returns
   - ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
   - ✅ 正确：使用volume作为规模代理，close作为价格
   - returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
2. ts_regression使用规范：
   - 避免深度嵌套ts_regression，特别是作为其他函数的参数
   - ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
   - ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
   - 替代方案：用ts_delta组合计算动量变化
3. if_else使用规范：
   - 条件必须是简单布尔表达式
   - 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
   - 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
4. bucket函数使用规范：
   - bucket()返回分组ID，可用于条件判断
   - ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
   - ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
   - 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
=== 关键语法规则结束 ===
*=====*
注意事项:
1. 避免过度复杂的嵌套（建议不超过3层）
2. 每个表达式应有明确的经济逻辑
3. 考虑实际交易可行性（避免未来函数）
4. 包含风险控制元素（如波动率调整）
5. 只能使用可用的数据字段：close, volume, returns等
*=====*
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。
构建框架指导（请按此逻辑创造新因子）：
维度融合模板（选择至少2个）：
A. 领导力动量 = 时序动量 × 横截面调整
   逻辑：大成交量股票的动量更强
   结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
B. 状态自适应动量 = 条件选择动量
   逻辑：高波动用短期动量，低波动用长期动量
   结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
C. 行业传导因子 = 领先行业动量 × 相关性强度
   逻辑：与强势行业相关性高的行业未来表现好
   结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
D. 情绪反转 = 过度交易信号 × 基础趋势
   逻辑：过度交易时反转，趋势延续时跟随
   结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
关键组件库（可自由组合）：
1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
5. 相关性类：ts_corr({x},{y},{d})
6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
*=====*
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。
**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, 进行生成,组合因子：

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: call_breakeven_180
DataFieldDescription: Price at which a stock's call options with expiration 180 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_150
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.
DataField: option_breakeven_180
DataFieldDescription: Price at which a stock's options with expiration 180 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: 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: put_breakeven_1080
DataFieldDescription: Price at which a stock's put options with expiration 1080 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_oi_all
DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
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_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: option_breakeven_10
DataFieldDescription: Price at which a stock's options with expiration 10 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: call_breakeven_360
DataFieldDescription: Price at which a stock's call options with expiration 360 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_10
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
DataField: pcr_vol_1080
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.
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: call_breakeven_30
DataFieldDescription: Price at which a stock's call options with expiration 30 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: 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: pcr_vol_60
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
DataField: pcr_vol_150
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
DataField: pcr_oi_30
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 30 days in the future.
DataField: forward_price_10
DataFieldDescription: Forward price at 10 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_720
DataFieldDescription: Price at which a stock's options with expiration 720 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_60
DataFieldDescription: Forward price at 60 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_270
DataFieldDescription: Forward price at 270 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: option_breakeven_150
DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
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: fnd6_newa2v1300_opeps
DataFieldDescription: Earnings Per Share from Operations
DataField: fnd6_newqv1300_dilavq
DataFieldDescription: Dilution Available - Excluding Extraordinary Items
DataField: fnd6_newqeventv110_wdaq
DataFieldDescription: Writedowns After-tax
DataField: fnd6_mkvaltq
DataFieldDescription: Market Value - Total
DataField: fnd6_newqv1300_chq
DataFieldDescription: Cash
DataField: fnd6_esubc
DataFieldDescription: Equity in Net Loss - Earnings
DataField: fnd6_cptnewqeventv110_saleq
DataFieldDescription: Sales/Turnover (Net)
DataField: fnd6_newqeventv110_glaq
DataFieldDescription: Gain/Loss After-Tax
DataField: fnd6_cimii
DataFieldDescription: Comprehensive Income - Noncontrolling Interest
DataField: working_capital
DataFieldDescription: Working Capital (Balance Sheet)
DataField: fnd6_newa1v1300_ceq
DataFieldDescription: Common/Ordinary Equity - Total
DataField: fnd6_newqeventv110_dcomq
DataFieldDescription: Deferred Compensation
DataField: fnd6_cptnewqeventv110_epsfxq
DataFieldDescription: Earnings Per Share (Diluted) - Excluding Extraordinary items
DataField: fnd6_newqeventv110_tfvlq
DataFieldDescription: Total Fair Value Liabilities
DataField: fnd6_txds
DataFieldDescription: Deferred Taxes - State
DataField: fnd6_newqeventv110_esoprq
DataFieldDescription: Preferred ESOP Obligation - Redeemable
DataField: fnd6_newqv1300_drcq
DataFieldDescription: Deferred Revenue - Current
DataField: fnd6_newa1v1300_aociother
DataFieldDescription: Accum Other Comp Inc - Other Adjustments
DataField: fnd6_fatp
DataFieldDescription: Plant, Property and Equipment at Cost - Land & Improvements
DataField: fnd6_newqv1300_rcpq
DataFieldDescription: Restructuring Cost Pretax
DataField: fnd6_intan
DataFieldDescription: Intangible Assets - Total
DataField: cashflow_fin
DataFieldDescription: Financing Activities - Net Cash Flow
DataField: fnd6_newa1v1300_apalch
DataFieldDescription: Accounts Payable and Accrued Liabilities - Increase/(Decrease)
DataField: fnd6_prcl
DataFieldDescription: Price Low - Annual
DataField: fnd6_newqv1300_altoq
DataFieldDescription: Other Long-term Assets
DataField: fnd6_newqeventv110_ibq
DataFieldDescription: Income Before Extraordinary Items
DataField: cogs
DataFieldDescription: Cost of Goods Sold
DataField: fnd6_newa1v1300_gdwl
DataFieldDescription: Goodwill
DataField: fnd6_newa1v1300_capx
DataFieldDescription: Capital Expenditures
DataField: fnd6_newqeventv110_pncwidpq
DataFieldDescription: Core Pension w/o Interest Adjustment Diluted EPS Effect Preliminary
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: dividend_estimate_value
DataFieldDescription: Dividend per share - estimated value
DataField: anl4_fcfps_low
DataFieldDescription: Free Cash Flow Per Share - the lowest estimation
DataField: anl4_ady_down
DataFieldDescription: Number of lower estimations
DataField: anl4_eaz1lqfv110_person
DataFieldDescription: Broker Id
DataField: anl4_bvps_value
DataFieldDescription: Book value per share - announced financial value
DataField: anl4_dez1basicqfv4_preest
DataFieldDescription: The previous estimation of finanicial item
DataField: actual_cashflow_per_share_value_quarterly
DataFieldDescription: Cash Flow Per Share - actual value for the quarter
DataField: sales_estimate_median_value
DataFieldDescription: Sales - Median value among forecasts
DataField: anl4_ads1detailafv110_estvalue
DataFieldDescription: Estimation value
DataField: sales_guidance_value_quarterly
DataFieldDescription: Sales - guidance value
DataField: anl4_adjusted_netincome_ft
DataFieldDescription: Adjusted net income - forecast type (revision/new/...)
DataField: anl4_ebit_std
DataFieldDescription: Earnings before interest and taxes - standard deviation of estimations
DataField: anl4_qfv4_div_median
DataFieldDescription: Dividend per share - median of estimations
DataField: sales_estimate_minimum_quarterly
DataFieldDescription: Sales - The lowest estimation
DataField: max_tangible_book_value_per_share_guidance
DataFieldDescription: Tangible Book Value per Share - maximum guidance value
DataField: anl4_epsr_mean
DataFieldDescription: GAAP Earnings per share - mean of estimations
DataField: anl4_qfv4_median_eps
DataFieldDescription: Earnings per share - median of estimations
DataField: max_adjusted_eps_guidance_2
DataFieldDescription: The maximum guidance value for adjusted earnings per share on an annual basis.
DataField: earnings_per_share_median_value
DataFieldDescription: Earnings per share - median of estimations
DataField: min_net_debt_guidance
DataFieldDescription: The minimum guidance value for Net Debt on an annual basis.
DataField: anl4_qf_az_wol_spe
DataFieldDescription: Earnings per share - The lowest estimation
DataField: max_adjusted_net_profit_guidance
DataFieldDescription: The maximum guidance value for adjusted net profit on an annual basis.
DataField: anl4_netprofit_high
DataFieldDescription: Net Profit - The highest estimation
DataField: anl4_totgw_high
DataFieldDescription: Total Goodwill - The highest estimation
DataField: minimum_guidance_value
DataFieldDescription: Minimum guidance value for basic annual financials
DataField: anl4_medianepsbfam
DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - median of estimations
DataField: net_debt_min_guidance_qtr
DataFieldDescription: Minimum guidance value for Net Debt
DataField: shareholders_equity_actual_value
DataFieldDescription: Shareholders' Equity - Total Value
DataField: eps_adjusted_min_guidance_value
DataFieldDescription: The minimum guidance value for adjusted earnings per share excluding extraordinary items and stock option expenses on an annual basis.
DataField: shareholders_equity_min_guidance
DataFieldDescription: Minimum guidance value for Share Equity
DataField: pv13_r2_liquid_min10_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_3k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f4_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min30_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_pureplay_only_sector
DataFieldDescription: grouping fields
DataField: pv13_6l_scibr
DataFieldDescription: grouping fields
DataField: pv13_h_min51_f3_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min50_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f4_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f1_513_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_liquid_min2_sector
DataFieldDescription: grouping fields
DataField: pv13_1l_scibr
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_city
DataFieldDescription: City code
DataField: pv13_hierarchy_min51_f1_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_new_5l_scibr
DataFieldDescription: grouping fields
DataField: pv13_r2_min5_1000_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_3000_513_sector
DataFieldDescription: grouping fields
DataField: rel_num_cust
DataFieldDescription: number of the instrument's customers
DataField: pv13_new_4l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_2k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min40_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min10_all_sector
DataFieldDescription: grouping fields
DataField: historical_volatility_60
DataFieldDescription: Close-to-close Historical volatility over 60 days
DataField: implied_volatility_mean_skew_270
DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
DataField: implied_volatility_call_720
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
DataField: implied_volatility_put_60
DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
DataField: implied_volatility_put_90
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
DataField: implied_volatility_mean_skew_30
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: implied_volatility_put_180
DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
DataField: implied_volatility_call_30
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
DataField: implied_volatility_mean_skew_90
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
DataField: implied_volatility_mean_20
DataFieldDescription: At-the-money option-implied volatility mean for 20 days
DataField: historical_volatility_180
DataFieldDescription: Close-to-close Historical volatility over 180 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: historical_volatility_20
DataFieldDescription: Close-to-close Historical volatility over 20 days
DataField: implied_volatility_call_90
DataFieldDescription: At-the-money option-implied volatility for call Option 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_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
DataField: historical_volatility_120
DataFieldDescription: Close-to-close Historical volatility over 120 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: implied_volatility_call_180
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: parkinson_volatility_150
DataFieldDescription: Parkinson model's historical volatility over 150 days
DataField: implied_volatility_call_360
DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
DataField: historical_volatility_90
DataFieldDescription: Close-to-close Historical volatility over 90 days
DataField: implied_volatility_mean_skew_720
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
DataField: parkinson_volatility_90
DataFieldDescription: Parkinson model's historical volatility over 90 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: implied_volatility_call_150
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
DataField: parkinson_volatility_30
DataFieldDescription: Parkinson model's historical volatility over 30 days
DataField: news_ratio_vol
DataFieldDescription: Curr_Vol / Mov_Vol
DataField: news_session_range
DataFieldDescription: Session High Price - Session Low Price
DataField: nws12_mainz_mainvwap
DataFieldDescription: Main session volume weighted average price
DataField: nws12_afterhsz_tonlow
DataFieldDescription: Lowest price reached during the session before the time of the news
DataField: nws12_prez_opengap
DataFieldDescription: (DayOpen - PrevClose) / PrevClose.
DataField: nws12_afterhsz_curr_vol
DataFieldDescription: Current day's session volume
DataField: nws12_prez_01p
DataFieldDescription: The minimum of L or S above for 10-minute bucket
DataField: nws12_prez_tonlow
DataFieldDescription: Lowest price reached during the session before the time of the news
DataField: nws12_afterhsz_90_min
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
DataField: nws12_afterhsz_1p
DataFieldDescription: The minimum of L or S above for 1-minute bucket
DataField: nws12_afterhsz_maxdown
DataFieldDescription: Percent change from the price at the time of the news to the after the news low
DataField: news_ls
DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS= 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
DataField: nws12_mainz_curr_vol
DataFieldDescription: Current day's session volume
DataField: news_pct_30sec
DataFieldDescription: The percent change in price in the 30 seconds following the news release
DataField: nws12_afterhsz_4s
DataFieldDescription: Number of minutes that elapsed before price went down 4 percentage points
DataField: nws12_mainz_prevwap
DataFieldDescription: Pre session volume weighted average price
DataField: nws12_prez_eodvwap
DataFieldDescription: Volume-weighted average price between the time of news and the end of the session
DataField: news_mins_20_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
DataField: nws12_afterhsz_volstddev
DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
DataField: nws12_afterhsz_5p
DataFieldDescription: The minimum of L or S above for 5-minute bucket
DataField: nws12_prez_prevclose
DataFieldDescription: Previous trading day's close price
DataField: news_mins_10_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
DataField: nws12_mainz_5s
DataFieldDescription: Number of minutes that elapsed before price went down 5 percentage points
DataField: news_spy_close
DataFieldDescription: Price of SPY at close of session
DataField: news_eod_vwap
DataFieldDescription: Volume weighted average price between the time of news and the end of the session
DataField: nws12_afterhsz_3s
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
DataField: nws12_afterhsz_01s
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
DataField: nws12_prez_volstddev
DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
DataField: nws12_prez_57p
DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
DataField: nws12_mainz_02s
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: rp_ess_credit
DataFieldDescription: Event sentiment score of credit news
DataField: rp_ess_dividends
DataFieldDescription: Event sentiment score of dividends news
DataField: rp_css_revenue
DataFieldDescription: Composite sentiment score of revenue news
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: rp_css_assets
DataFieldDescription: Composite sentiment score of assets news
DataField: nws18_relevance
DataFieldDescription: Relevance of news to the company
DataField: rp_nip_insider
DataFieldDescription: News impact projection of insider trading news
DataField: nws18_ghc_lna
DataFieldDescription: Change in analyst recommendation
DataField: rp_css_mna
DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
DataField: rp_ess_price
DataFieldDescription: Event sentiment score of stock price news
DataField: rp_css_technical
DataFieldDescription: Composite sentiment score based on technical analysis
DataField: rp_nip_credit
DataFieldDescription: News impact projection of credit news
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: rp_nip_dividends
DataFieldDescription: News impact projection of dividends news
DataField: rp_nip_technical
DataFieldDescription: News impact projection based on technical analysis
DataField: rp_css_price
DataFieldDescription: Composite sentiment score of stock price news
DataField: rp_css_credit_ratings
DataFieldDescription: Composite sentiment score of credit ratings news
DataField: rp_nip_mna
DataFieldDescription: News impact projection of mergers and acquisitions-related news
DataField: rp_css_inverstor
DataFieldDescription: Composite sentiment score of investor relations news
DataField: rp_css_ptg
DataFieldDescription: Composite sentiment score of price target news
DataField: rp_css_marketing
DataFieldDescription: Composite sentiment score of marketing news
DataField: nws18_sse
DataFieldDescription: Sentiment of phrases impacting the company
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: rp_ess_society
DataFieldDescription: Event sentiment score of society-related news
DataField: rp_nip_credit_ratings
DataFieldDescription: News impact projection of credit ratings news
DataField: rp_ess_earnings
DataFieldDescription: Event sentiment score of earnings news
DataField: rp_ess_business
DataFieldDescription: Event sentiment score of business-related news
DataField: rp_css_society
DataFieldDescription: Composite sentiment score of society-related news
DataField: rp_nip_labor
DataFieldDescription: News impact projection of labor issues news
DataField: rp_css_legal
DataFieldDescription: Composite sentiment score of legal news
DataField: fn_line_of_credit_facility_amount_out_q
DataFieldDescription: Amount borrowed under the credit facility as of the balance sheet date.
DataField: fn_comp_fair_value_assumptions_weighted_avg_vol_rate_a
DataFieldDescription: Weighted average expected volatility rate of share-based compensation awards.
DataField: fn_accum_oth_income_loss_net_of_tax_q
DataFieldDescription: Accumulated change in equity from transactions and other events and circumstances from non-owner sources, net of tax effect, at period end. Excludes Net Income (Loss), and accumulated changes in equity from transactions resulting from investments by owners and distributions to owners. Includes foreign currency translation items, certain pension adjustments, unrealized gains and losses on certain investments in debt and equity securities, other than temporary impairment (OTTI) losses related to factors other than credit losses on available-for-sale and held-to-maturity debt securities that an entity does not intend to sell and it is not more likely than not that the entity will be required to sell before recovery of the amortized cost basis, as well as changes in the fair value of derivatives related to the effective portion of a designated cash flow hedge.
DataField: fn_business_combination_purchase_price_q
DataFieldDescription: Business Combination, Purchase Price
DataField: fn_oth_comp_forfeitures_fair_value_a
DataFieldDescription: Annual Share Based Compensation Equity Instruments Other Than Options Forfeitures Weighted Average Grant Date Fair Value
DataField: fn_repayments_of_lt_debt_a
DataFieldDescription: The cash outflow for debt initially having maturity due after 1 year or beyond the normal operating cycle, if longer.
DataField: fn_business_combination_purchase_price_a
DataFieldDescription: Business Combination, Purchase Price
DataField: fn_unrecognized_tax_benefits_a
DataFieldDescription: Amount of unrecognized tax benefits.
DataField: fn_employee_related_liab_a
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_finite_lived_intangible_assets_net_a
DataFieldDescription: Finite Lived Intangible Assets, Net
DataField: fn_oth_comp_grants_weighted_avg_grant_date_fair_value_q
DataFieldDescription: Quarterly Share-Based Compensation Equity Instruments Other Than Options Nonvested Weighted Average Grant Date Fair Value
DataField: fn_incremental_shares_attributable_to_share_based_payment_a
DataFieldDescription: Additional shares included in the calculation of diluted EPS as a result of the potentially dilutive effect of share-based payment arrangements using the treasury stock method.
DataField: fn_accum_depr_depletion_and_amortization_ppne_q
DataFieldDescription: Amount of accumulated depreciation, depletion and amortization for physical assets used in the normal conduct of business to produce goods and services.
DataField: fnd2_a_acmopclcchngcfectnt
DataFieldDescription: Accumulated change, net of tax, in accumulated gains and losses from derivative instruments designated and qualifying as the effective portion of cash flow hedges. Includes an entity's share of an equity investee's Increase or Decrease in deferred hedging gains or losses.
DataField: fn_allocated_share_based_compensation_expense_q
DataFieldDescription: Represents the expense recognized during the period arising from equity-based compensation arrangements (for example, shares of stock, units, stock options, or other equity instruments) with employees, directors, and certain consultants qualifying for treatment as employees.
DataField: fn_incremental_shares_attributable_to_share_based_payment_q
DataFieldDescription: Additional shares included in the calculation of diluted EPS as a result of the potentially dilutive effect of share-based payment arrangements using the treasury stock method.
DataField: fn_income_from_equity_investments_q
DataFieldDescription: Income From Equity Method Investments
DataField: fn_income_taxes_paid_a
DataFieldDescription: The amount of cash paid during the current period to foreign, federal, state, and local authorities as taxes on income.
DataField: fnd2_oprlsfmpdcurr
DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the next 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_allocated_share_based_compensation_expense_a
DataFieldDescription: Represents the expense recognized during the period arising from equity-based compensation arrangements (for example, shares of stock, unit, stock options or other equity instruments) with employees, directors and certain consultants qualifying for treatment as employees.
DataField: fn_repurchased_shares_value_a
DataFieldDescription: Shares repurchased and either retired or put into treasury stock, likely as part of a share buyback plan.
DataField: fn_line_of_credit_facility_amount_out_a
DataFieldDescription: Amount borrowed under the credit facility as of the balance sheet date.
DataField: fn_liab_fair_val_l3_a
DataFieldDescription: Liabilities Fair Value, Recurring, Level 3
DataField: fn_comp_options_grants_fair_value_a
DataFieldDescription: Annual Share-Based Compensation Arrangement by Share-Based Payment Award Options Grants in Period Weighted Average Grant Date Fair Value
DataField: fnd2_dfdtxasoprlcarryfwd
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible operating loss carryforwards.
DataField: fnd2_q_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: fn_comp_options_exercises_weighted_avg_a
DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
DataField: fnd2_a_inventoryrawmaterials
DataFieldDescription: Amount before valuation and LIFO reserves of raw materials expected to be sold, or consumed within 1 year or operating cycle, if longer.
DataField: fn_debt_instrument_interest_rate_stated_percentage_q
DataFieldDescription: Stated percentage of interest rate on debt
DataField: fn_eff_income_tax_rate_continuing_operations_q
DataFieldDescription: Percentage of current income tax expense (benefit) and deferred income tax expense (benefit) pertaining to continuing operations.
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 =======================================