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: 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_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: call_breakeven_150
DataFieldDescription: Price at which a stock's call options with expiration 150 days in the future break even based on its recent bid/ask mean.
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: 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: pcr_oi_60
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 60 days in the future.
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: 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: pcr_oi_720
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 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_vol_10
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
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: 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: 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: 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: 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_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: pcr_vol_720
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 720 days in the future.
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: put_breakeven_20
DataFieldDescription: Price at which a stock's put options with expiration 20 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: pcr_vol_180
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
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: pcr_oi_360
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 360 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: 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: forward_price_20
DataFieldDescription: Forward price at 20 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_all
DataFieldDescription: Ratio of put volume to call volume for all maturities on stock's options.
DataField: fnd6_rea
DataFieldDescription: Retained Earnings - Restatement
DataField: fnd6_newa2v1300_ppegt
DataFieldDescription: Property, Plant and Equipment - Total (Gross)
DataField: fnd6_newqv1300_mibtq
DataFieldDescription: Noncontrolling Interests - Total - Balance Sheet - Quarterly
DataField: ebit
DataFieldDescription: Earnings Before Interest and Taxes
DataField: fnd6_newqeventv110_ppegtq
DataFieldDescription: Property, Plant and Equipment - Total (Gross) - Quarterly
DataField: fnd6_newqeventv110_gdwlipq
DataFieldDescription: Impairment of Goodwill Pretax
DataField: fnd6_newqeventv110_spceepsq
DataFieldDescription: S&P Core Earnings EPS Basic
DataField: fnd6_newqeventv110_glcedq
DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS
DataField: fnd6_newqeventv110_spceeps12
DataFieldDescription: S&P Core Earnings EPS Basic 12MM
DataField: fnd6_txdfed
DataFieldDescription: Deferred Taxes - Federal
DataField: fnd6_fatb
DataFieldDescription: Plant, Property and Equipment at Cost - Buildings
DataField: fnd6_newa1v1300_dp
DataFieldDescription: Depreciation and Amortization
DataField: fnd6_newa2v1300_prsho
DataFieldDescription: Redeem Pfd Shares Outs (000)
DataField: fnd6_newqv1300_aol2q
DataFieldDescription: Assets Level 2 (Observable)
DataField: fnd6_mfma1_dpc
DataFieldDescription: Depreciation and Amortization (Cash Flow)
DataField: fnd6_ptis
DataFieldDescription: Pretax Income
DataField: fnd6_cptnewqv1300_ceqq
DataFieldDescription: Common/Ordinary Equity - Total
DataField: fnd6_newqv1300_cogsq
DataFieldDescription: Cost of Goods Sold
DataField: fnd6_newa1v1300_dltt
DataFieldDescription: Long-Term Debt - Total
DataField: fnd6_newqv1300_invrmq
DataFieldDescription: Inventory - Raw Materials
DataField: fnd6_newqeventv110_pncq
DataFieldDescription: Core Pension Adjustment
DataField: fnd6_txtubtxtr
DataFieldDescription: Impact on Effective Tax Rate
DataField: fnd6_newa1v1300_dcom
DataFieldDescription: Deferred Compensation
DataField: fnd6_newa1v1300_ebit
DataFieldDescription: Earnings Before Interest and Taxes
DataField: fnd6_dd5
DataFieldDescription: Debt Due in 5th Year
DataField: fnd6_newqv1300_cshfdq
DataFieldDescription: Common Shares for Diluted EPS
DataField: fnd6_newa1v1300_dv
DataFieldDescription: Cash Dividends (Cash Flow)
DataField: cash
DataFieldDescription: Cash
DataField: fnd6_newqeventv110_seteps12
DataFieldDescription: Settlement (Litigation/Insurance) Basic EPS Effect 12MM
DataField: fnd6_mfma2_opeps
DataFieldDescription: Earnings Per Share from Operations
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: 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: anl4_fsguidanceafv4_minguidance
DataFieldDescription: Min guidance value
DataField: anl4_afv4_eps_high
DataFieldDescription: Earnings per share - The highest estimation
DataField: anl4_basicdetailqfv110_prevval
DataFieldDescription: The previous estimation of financial item
DataField: anl4_basicconltv110_high
DataFieldDescription: The highest estimation
DataField: dividend_previous_estimate_value
DataFieldDescription: The previous estimation of dividend
DataField: anl4_bac1conafv110_item
DataFieldDescription: Financial item
DataField: anl4_fsguidanceafv4_maxguidance
DataFieldDescription: Maximum guidance value
DataField: anl4_eaz2lafv110_prevval
DataFieldDescription: The previous estimation of financial item
DataField: anl4_fsdetailltv4v104_item
DataFieldDescription: Financial item
DataField: selling_general_admin_expense_reported_value
DataFieldDescription: Selling, General & Administrative Expense value
DataField: max_free_cashflow_guidance
DataFieldDescription: The maximum guidance value for Free Cash Flow.
DataField: anl4_cfo_value
DataFieldDescription: Cash Flow From Operations - announced financial value
DataField: anl4_fsdtlestmtbscv104_item
DataFieldDescription: Financial item
DataField: min_total_goodwill_guidance
DataFieldDescription: Total Goodwill - The lowest guidance value
DataField: anl4_qfd1_az_wol_spfc
DataFieldDescription: Cash Flow Per Share - The lowest estimation
DataField: eps_reported_min_guidance_qtr
DataFieldDescription: Reported Earnings Per Share - Minimum guidance value
DataField: anl4_gric_value
DataFieldDescription: Gross income- announced financial value
DataField: anl4_detailltv4_est
DataFieldDescription: Long term estimation value
DataField: max_pretax_profit_guidance
DataFieldDescription: The maximum guidance value for Pretax income on an annual basis.
DataField: anl4_fsguidancebasicqfv4_item
DataFieldDescription: Financial item
DataField: anl4_afv4_div_std
DataFieldDescription: Dividend per share - standard deviation of estimations
DataField: cashflow_per_share_median_value
DataFieldDescription: Cash Flow Per Share - Median value among forecasts
DataField: anl4_dei3lafv110_item
DataFieldDescription: Financial item
DataField: anl4_ady_high
DataFieldDescription: The highest estimation
DataField: anl4_epsa_flag
DataFieldDescription: Earnings per share adjusted by excluding extraordinary items and stock option expenses - forecast type (revision/new/...)
DataField: max_share_buyback_guidance
DataFieldDescription: Maximum guidance value for Shares Basic - Annual
DataField: anl4_netdebt_flag
DataFieldDescription: Net debt - forecast type (revision/new/...)
DataField: anl4_qfd1_az_cfps_median
DataFieldDescription: Cash Flow Per Share - Median value among forecasts
DataField: anl4_qfv4_minguidance
DataFieldDescription: Min guidance value
DataField: rel_ret_comp
DataFieldDescription: Averaged one-day return of the competing companies
DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min40_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min54_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchys32_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f1_513_sector
DataFieldDescription: grouping fields
DataField: pv13_3l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_2k_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_top3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_sector
DataFieldDescription: grouping fields
DataField: pv13_2l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min25_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_company_total
DataFieldDescription: Total number of companies in the sector
DataField: pv13_rha2_min2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_term_sector_total
DataFieldDescription: Number of terminal sectors for the company
DataField: pv13_hierarchy23_513_sector
DataFieldDescription: grouping fields
DataField: pv13_custretsig_retsig
DataFieldDescription: Sign of customer return
DataField: pv13_hierarchy_min20_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_term
DataFieldDescription: Indicates when a sector is the terminal sector (i.e., no sub-sectors)
DataField: pv13_hierarchy_min100_corr21_sector
DataFieldDescription: grouping fields
DataField: rel_ret_all
DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
DataField: pv13_h_min2_focused_pureplay_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f4_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min30_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min2_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f4_513_sector
DataFieldDescription: grouping fields
DataField: implied_volatility_mean_skew_720
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
DataField: implied_volatility_mean_skew_20
DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: implied_volatility_call_1080
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
DataField: implied_volatility_call_30
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: implied_volatility_call_720
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
DataField: parkinson_volatility_120
DataFieldDescription: Parkinson model's historical volatility over 120 days
DataField: implied_volatility_put_120
DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
DataField: historical_volatility_20
DataFieldDescription: Close-to-close Historical volatility over 20 days
DataField: parkinson_volatility_60
DataFieldDescription: Parkinson model's historical volatility over 60 days
DataField: implied_volatility_call_150
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
DataField: implied_volatility_put_270
DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
DataField: implied_volatility_mean_120
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
DataField: implied_volatility_mean_10
DataFieldDescription: At-the-money option-implied volatility mean for 10 days
DataField: implied_volatility_put_90
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
DataField: implied_volatility_mean_270
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
DataField: implied_volatility_mean_skew_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
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: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: parkinson_volatility_10
DataFieldDescription: Parkinson model's historical volatility over 2 weeks
DataField: implied_volatility_mean_skew_150
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
DataField: parkinson_volatility_90
DataFieldDescription: Parkinson model's historical volatility over 90 days
DataField: implied_volatility_mean_skew_360
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
DataField: historical_volatility_180
DataFieldDescription: Close-to-close Historical volatility over 180 days
DataField: implied_volatility_call_180
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
DataField: implied_volatility_put_360
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: news_indx_perf
DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
DataField: nws12_afterhsz_3p
DataFieldDescription: The minimum of L or S above for 3-minute bucket
DataField: news_prev_day_ret
DataFieldDescription: Percent change between the previous day's open and close
DataField: nws12_mainz_01s
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
DataField: nws12_prez_57s
DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
DataField: nws12_afterhsz_120_min
DataFieldDescription: The percent change in price in the first 120 minutes following the news release
DataField: nws12_afterhsz_41rta
DataFieldDescription: 14-day Average True Range
DataField: nws12_afterhsz_mov_vol
DataFieldDescription: 30-day moving average session volume
DataField: nws12_allz_newssess
DataFieldDescription: Index of session in which the news was reported
DataField: nws12_mainz_2l
DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
DataField: news_eps_actual
DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
DataField: news_mins_3_chg
DataFieldDescription: The minimum of L or S above for 3-minute bucket
DataField: news_mins_7_5_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
DataField: nws12_prez_maxdnamt
DataFieldDescription: The price at the time of the news minus the after the news low
DataField: nws12_afterhsz_maxdnamt
DataFieldDescription: The price at the time of the news minus the after the news low
DataField: nws12_afterhsz_02l
DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
DataField: nws12_prez_dayopen
DataFieldDescription: Price at the session open
DataField: news_mins_5_chg
DataFieldDescription: The minimum of L or S above for 5-minute bucket
DataField: nws12_mainz_30_min
DataFieldDescription: The percent change in price in the first 30 minutes following the news release
DataField: nws12_mainz_peratio
DataFieldDescription: Reported price-to-earnings ratio for the calendar day of the session
DataField: news_pct_10min
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
DataField: news_mins_5_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 5 percentage points
DataField: nws12_mainz_newrecord
DataFieldDescription: Tracks whether the news is first instance or a duplicate
DataField: nws12_prez_eodlow
DataFieldDescription: Lowest price reached between the time of news and the end of the session.
DataField: nws12_mainz_prevday
DataFieldDescription: Percent change between the previous day's open and close
DataField: nws12_prez_02p
DataFieldDescription: The minimum of L or S above for 20-minute bucket
DataField: nws12_afterhsz_div_y
DataFieldDescription: Annual yield
DataField: nws12_afterhsz_lowexcstddev
DataFieldDescription: (TONLast - EODLow) / StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
DataField: nws12_afterhsz_1s
DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
DataField: news_mins_4_chg
DataFieldDescription: The minimum of L or S above for 4-minute bucket
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: rp_nip_assets
DataFieldDescription: News impact projection of assets news
DataField: rp_ess_technical
DataFieldDescription: Event sentiment score based on technical analysis
DataField: nws18_event_relevance
DataFieldDescription: Relevance of the event to the story
DataField: rp_ess_insider
DataFieldDescription: Event sentiment score of insider trading news
DataField: rp_nip_society
DataFieldDescription: News impact projection of society-related news
DataField: nws18_bam
DataFieldDescription: News sentiment specializing in mergers and acquisitions
DataField: rp_nip_marketing
DataFieldDescription: News impact projection of marketing news
DataField: nws18_sse
DataFieldDescription: Sentiment of phrases impacting the company
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: nws18_event_similarity_days
DataFieldDescription: Days since a similar event was detected
DataField: nws18_relevance
DataFieldDescription: Relevance of news to the company
DataField: rp_ess_credit
DataFieldDescription: Event sentiment score of credit news
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: nws18_ssc
DataFieldDescription: Sentiment of the news calculated using multiple techniques
DataField: rp_ess_earnings
DataFieldDescription: Event sentiment score of earnings news
DataField: rp_ess_equity
DataFieldDescription: Event sentiment score of equity action news
DataField: rp_ess_society
DataFieldDescription: Event sentiment score of society-related news
DataField: rp_nip_inverstor
DataFieldDescription: News impact projection of investor relations news
DataField: rp_ess_price
DataFieldDescription: Event sentiment score of stock price news
DataField: rp_ess_ptg
DataFieldDescription: Event sentiment score of price target news
DataField: rp_css_partner
DataFieldDescription: Composite sentiment score of partnership news
DataField: rp_nip_partner
DataFieldDescription: News impact projection of partnership news
DataField: rp_nip_credit
DataFieldDescription: News impact projection of credit news
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: rp_ess_dividends
DataFieldDescription: Event sentiment score of dividends news
DataField: nws18_acb
DataFieldDescription: News sentiment specializing in corporate action announcements
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: nws18_nip
DataFieldDescription: Degree of impact of the news
DataField: rp_nip_labor
DataFieldDescription: News impact projection of labor issues news
DataField: rp_css_business
DataFieldDescription: Composite sentiment score of business-related news
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_comp_non_opt_forfeited_q
DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that were forfeited during the reporting period.
DataField: fn_proceeds_from_stock_options_exercised_q
DataFieldDescription: The cash inflow associated with the amount received from holders exercising their stock options. This item inherently excludes any excess tax benefit, which the entity may have realized and reported separately.
DataField: fn_profit_loss_q
DataFieldDescription: The consolidated profit or loss for the period, net of income taxes, including the portion attributable to the noncontrolling interest.
DataField: fnd2_a_sbcpnargmsawpfipwerpr
DataFieldDescription: Weighted average price of options that were either forfeited or expired.
DataField: fnd2_a_sbcpnargmpmwggil
DataFieldDescription: Amount by which the current fair value of the underlying stock exceeds the exercise price of fully vested and expected to vest options outstanding.
DataField: fn_finite_lived_intangible_assets_net_q
DataFieldDescription: Finite Lived Intangible Assets, Net
DataField: fnd2_dbplanepdfbnfpnext12m
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid 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: fnd2_a_sbcpnargmsptawervl
DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
DataField: fn_finite_lived_intangible_assets_gross_q
DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
DataField: fn_comp_non_opt_vested_q
DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that vested during the reporting period.
DataField: fnd2_dfdtxastxdfdexprssaccrs
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible temporary differences from reserves and accruals.
DataField: fnd2_a_stkrpeprogramardamt
DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
DataField: fnd2_a_curritxexp
DataFieldDescription: Income Tax Expense, Current
DataField: fn_comp_non_opt_nonvested_number_a
DataFieldDescription: The number of non-vested equity-based payment instruments, excluding stock (or unit) options, that validly exist and are outstanding as of the balance sheet date.
DataField: fn_comp_options_out_number_q
DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
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: fnd2_q_atdlsecexfcepsastkos
DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
DataField: fn_accum_depr_depletion_and_amortization_ppne_a
DataFieldDescription: Amount of accumulated depreciation, depletion and amortization for physical assets used in the normal conduct of business to produce goods and services.
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: fn_finite_lived_intangible_assets_gross_a
DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
DataField: fnd2_a_gwllimrml
DataFieldDescription: Amount of loss from the write-down of an asset representing the future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized.
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_comp_not_rec_a
DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
DataField: fn_income_taxes_paid_q
DataFieldDescription: The amount of cash paid during the current period to foreign, federal, state, and local authorities as taxes on income.
DataField: fn_comp_options_out_intrinsic_value_a
DataFieldDescription: The intrinsic value of a stock option is the amount by which the market value of the underlying stock exceeds the exercise price of the option.
DataField: fn_income_tax_expense_q
DataFieldDescription: Income Tax Expense (Benefit)
DataField: fnd2_a_atdlsecexfcepsastkos
DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
DataField: fnd2_a_flintasacmamtzcsrld
DataFieldDescription: Finite Lived Intangible Assets Accumulated Amortization, Customer Related
DataField: fnd2_a_ltrmdmrepoplinnext12m
DataFieldDescription: Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing 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: 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 =======================================