AlphaGenerator/manual_prompt/manual_prompt_20251213162445.txt

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: pcr_vol_60
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
DataField: option_breakeven_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: forward_price_120
DataFieldDescription: Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: forward_price_90
DataFieldDescription: Forward price at 90 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_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: pcr_oi_180
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 180 days in the future.
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: pcr_vol_150
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
DataField: option_breakeven_20
DataFieldDescription: Price at which a stock's 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: 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: pcr_vol_120
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
DataField: call_breakeven_270
DataFieldDescription: Price at which a stock's call options with expiration 270 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_1080
DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
DataField: 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: pcr_vol_1080
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.
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: option_breakeven_30
DataFieldDescription: Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.
DataField: call_breakeven_120
DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_30
DataFieldDescription: Forward price at 30 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: 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: 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: forward_price_360
DataFieldDescription: Forward price at 360 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: option_breakeven_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: 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_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_90
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
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_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: option_breakeven_270
DataFieldDescription: Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.
DataField: fnd6_newqeventv110_wdaq
DataFieldDescription: Writedowns After-tax
DataField: fnd6_newqv1300_xrdq
DataFieldDescription: Research and Development Expense
DataField: fnd6_newqeventv110_xoptd12
DataFieldDescription: Implied Option EPS Diluted 12MM
DataField: fnd6_cptnewqv1300_ceqq
DataFieldDescription: Common/Ordinary Equity - Total
DataField: fnd6_cld5
DataFieldDescription: Capitalized Leases - Due in 5th Year
DataField: fnd6_newqeventv110_ibq
DataFieldDescription: Income Before Extraordinary Items
DataField: fnd6_ivch
DataFieldDescription: Increase in Investments
DataField: fnd6_cptnewqeventv110_apq
DataFieldDescription: Accounts Payable/Creditors - Trade
DataField: fnd6_newqeventv110_setpq
DataFieldDescription: Settlement (Litigation/Insurance) Pretax
DataField: fnd6_recta
DataFieldDescription: Retained Earnings - Cumulative Translation Adjustment
DataField: fnd6_lifr
DataFieldDescription: LIFO Reserve
DataField: fnd6_zipcode
DataFieldDescription: ZIP code related to the company
DataField: fnd6_aqi
DataFieldDescription: Acquisitions - Income Contribution
DataField: fnd6_newa2v1300_tstkn
DataFieldDescription: Treasury Stock - Number of Common Shares
DataField: fnd6_ptis
DataFieldDescription: Pretax Income
DataField: fnd6_newqeventv110_drcq
DataFieldDescription: Deferred Revenue - Current
DataField: fnd6_newqv1300_aul3q
DataFieldDescription: Assets Level 3 (Unobservable)
DataField: fnd6_caxts
DataFieldDescription: Cost and Expenses - Total
DataField: fnd6_ibs
DataFieldDescription: Income before Extraordinary Items
DataField: fnd6_newa1v1300_lt
DataFieldDescription: Liabilities - Total
DataField: fnd6_newqv1300_csh12q
DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - 12 Months Moving
DataField: fnd6_invo
DataFieldDescription: Inventories - Other
DataField: fnd6_newqeventv110_xoprq
DataFieldDescription: Operating Expense - Total
DataField: fnd6_newqv1300_epsfiq
DataFieldDescription: Earnings Per Share (Diluted) - Including Extraordinary Items
DataField: fnd6_newa2v1300_re
DataFieldDescription: Retained Earnings
DataField: fnd6_newqeventv110_rdipepsq
DataFieldDescription: In-Process R&D Expense Basic EPS Effect
DataField: fnd6_eventv110_setdq
DataFieldDescription: Settlement (Litigation/Insurance) Diluted EPS Effect
DataField: fnd6_capxv
DataFieldDescription: Capital Expend Property, Plant and Equipment Schd V
DataField: fnd6_recco
DataFieldDescription: Receivables - Current - Other
DataField: fnd6_newqv1300_esopnrq
DataFieldDescription: Preferred ESOP Obligation - Non-Redeemable
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: min_reported_eps_guidance
DataFieldDescription: Reported Earnings Per Share - Minimum guidance value for the annual period
DataField: anl4_basicconqfv110_low
DataFieldDescription: The lowest estimation
DataField: shareholders_equity_reported_value
DataFieldDescription: Shareholders' Equity - Total Value
DataField: anl4_ads1detailafv110_prevval
DataFieldDescription: The Previous Estimation of Financial Item
DataField: anl4_qf_az_div_median
DataFieldDescription: Dividend per share - median of estimations
DataField: total_goodwill_reported_value
DataFieldDescription: Total Goodwill - Actual Value in Millions
DataField: anl4_fsdtlestmtsafv4_item
DataFieldDescription: Financial item
DataField: anl4_qf_az_cfps_number
DataFieldDescription: Cash Flow Per Share - number of estimations
DataField: anl4_dez1qfv4_est
DataFieldDescription: Estimation value
DataField: anl4_dei3lafv110_item
DataFieldDescription: Financial item
DataField: anl4_basicconltv110_numest
DataFieldDescription: The number of forecasts counted in aggregation
DataField: anl4_ady_median
DataFieldDescription: Median of estimations
DataField: max_financing_cashflow_guidance
DataFieldDescription: Cash Flow From Financing - Maximum guidance value
DataField: pretax_income_reported
DataFieldDescription: Reported Pretax income - actual value for the annual fiscal period
DataField: dividend_max_guidance_value
DataFieldDescription: The maximum guidance value for Dividend per share on an annual basis.
DataField: anl4_baz1v110_estvalue
DataFieldDescription: Estimation value
DataField: anl4_qfd1_azeps
DataFieldDescription: EPS - aggregation on estimations, 50th percentile
DataField: anl4_dts_ptp
DataFieldDescription: Pretax income - std of estimations
DataField: anl4_eaz2lafv110_bk
DataFieldDescription: Broker name (int)
DataField: anl4_capex_low
DataFieldDescription: Capital Expenditures - The lowest estimation
DataField: min_financing_cashflow_guidance
DataFieldDescription: Minimum guidance value for Cash Flow From Financing
DataField: anl4_qfv4_div_low
DataFieldDescription: Dividend per share - The lowest estimation
DataField: anl4_totassets_high
DataFieldDescription: Total Assets - The highest estimation
DataField: anl4_eaz1laf_person
DataFieldDescription: Broker Id
DataField: anl4_qf_az_cfps_median
DataFieldDescription: Cash Flow Per Share - Median value among forecasts
DataField: anl4_eaz2lrec_person
DataFieldDescription: Broker Id
DataField: min_net_profit_guidance
DataFieldDescription: Minimum guidance value for Net Profit on an annual basis
DataField: anl4_ady_pu
DataFieldDescription: The number of upper estimations
DataField: max_stock_option_expense_guidance
DataFieldDescription: Stock option expense - Maximum guidance value for the annual period
DataField: anl4_fsguidanceafv4_item
DataFieldDescription: Financial item
DataField: pv13_hierarchy_min2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min30_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_513_sector
DataFieldDescription: grouping fields
DataField: pv13_reportperiodlen
DataFieldDescription: The number of units which the report covers prior to the stated end date
DataField: pv13_rha2_min5_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min10_top3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_sector
DataFieldDescription: grouping fields
DataField: pv13_reportperiodend
DataFieldDescription: Stated end date for the report
DataField: pv13_h_min2_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_min20_1000_sector
DataFieldDescription: grouping fields
DataField: rel_num_cust
DataFieldDescription: number of the instrument's customers
DataField: pv13_h2_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_3k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_di_6l
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min22_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min40_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f1_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f4_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f4_sector
DataFieldDescription: grouping fields
DataField: rel_ret_cust
DataFieldDescription: averaged one-day-return of the instrument's customers
DataField: pv13_hierarchy_min10_sector_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_liquid_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_comproduct_company
DataFieldDescription: Company product
DataField: pv13_hierarchy_min30_3000_mapped_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_2k_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min24_500_sector
DataFieldDescription: Grouping fields for top 500
DataField: historical_volatility_90
DataFieldDescription: Close-to-close Historical volatility over 90 days
DataField: implied_volatility_put_1080
DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
DataField: implied_volatility_put_360
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
DataField: implied_volatility_put_20
DataFieldDescription: At-the-money option-implied volatility for Put Option for 20 days
DataField: implied_volatility_mean_180
DataFieldDescription: At-the-money option-implied volatility mean for 180 days
DataField: implied_volatility_mean_skew_10
DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
DataField: implied_volatility_put_30
DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
DataField: implied_volatility_put_180
DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
DataField: implied_volatility_mean_90
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
DataField: parkinson_volatility_150
DataFieldDescription: Parkinson model's historical volatility over 150 days
DataField: parkinson_volatility_120
DataFieldDescription: Parkinson model's historical volatility over 120 days
DataField: historical_volatility_20
DataFieldDescription: Close-to-close Historical volatility over 20 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: implied_volatility_mean_20
DataFieldDescription: At-the-money option-implied volatility mean for 20 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: historical_volatility_60
DataFieldDescription: Close-to-close Historical volatility over 60 days
DataField: implied_volatility_mean_skew_60
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
DataField: implied_volatility_mean_10
DataFieldDescription: At-the-money option-implied volatility mean for 10 days
DataField: implied_volatility_mean_skew_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_put_270
DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
DataField: implied_volatility_call_180
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
DataField: implied_volatility_mean_skew_720
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
DataField: implied_volatility_mean_120
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
DataField: parkinson_volatility_180
DataFieldDescription: Parkinson model's historical volatility over 180 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: implied_volatility_call_1080
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
DataField: historical_volatility_10
DataFieldDescription: Close-to-close Historical volatility over 10 days
DataField: nws12_prez_30_seconds
DataFieldDescription: The percent change in price in the 30 seconds following the news release
DataField: nws12_prez_mainvwap
DataFieldDescription: Main session volume-weighted average price
DataField: nws12_afterhsz_rangeamt
DataFieldDescription: Session High Price - Session Low Price
DataField: nws12_afterhsz_10_min
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
DataField: nws12_mainz_57p
DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
DataField: nws12_mainz_open_vol
DataFieldDescription: Main open volume
DataField: nws12_prez_prev_vol
DataFieldDescription: Previous day's session volume
DataField: news_pct_60min
DataFieldDescription: The percent change in price in the first 60 minutes following the news release
DataField: nws12_mainz_30_min
DataFieldDescription: The percent change in price in the first 30 minutes following the news release
DataField: nws12_mainz_3p
DataFieldDescription: The minimum of L or S above for 3-minute bucket
DataField: nws12_afterhsz_prevclose
DataFieldDescription: Previous trading day's close price
DataField: nws12_mainz_peratio
DataFieldDescription: Reported price-to-earnings ratio for the calendar day of the session
DataField: nws12_prez_1l
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_prez_result1
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
DataField: nws12_mainz_prevday
DataFieldDescription: Percent change between the previous day's open and close
DataField: nws12_mainz_10_min
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
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_mainz_allvwap
DataFieldDescription: Volume weighted average price of all sessions
DataField: news_spy_last
DataFieldDescription: Last Price of the SPY at the time of the news
DataField: nws12_afterhsz_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: news_mins_10_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
DataField: nws12_mainz_tonhigh
DataFieldDescription: Highest price reached during the session before the time of news
DataField: nws12_allz_newssess
DataFieldDescription: Index of session in which the news was reported
DataField: news_max_up_ret
DataFieldDescription: Percent change from the price at the time of the news to the after the news high
DataField: news_session_range_pct
DataFieldDescription: (Session High Price - Session Low Price) / Session Low Price.
DataField: nws12_mainz_provider
DataFieldDescription: index of name of the news provider
DataField: news_all_vwap
DataFieldDescription: Volume weighted average price of all sessions
DataField: nws12_prez_4l
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
DataField: news_eod_vwap
DataFieldDescription: Volume weighted average price between the time of news and the end of the session
DataField: nws12_mainz_30_seconds
DataFieldDescription: The percent change in price in the 30 seconds following the news release
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: nws18_ssc
DataFieldDescription: Sentiment of the news calculated using multiple techniques
DataField: rp_ess_society
DataFieldDescription: Event sentiment score of society-related news
DataField: rp_ess_earnings
DataFieldDescription: Event sentiment score of earnings news
DataField: nws18_bee
DataFieldDescription: News sentiment specializing in growth of earnings
DataField: rp_nip_assets
DataFieldDescription: News impact projection of assets news
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: rp_ess_equity
DataFieldDescription: Event sentiment score of equity action news
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: nws18_qcm
DataFieldDescription: News sentiment of relevant news with high confidence
DataField: rp_ess_technical
DataFieldDescription: Event sentiment score based on technical analysis
DataField: rp_nip_ptg
DataFieldDescription: News impact projection of price target news
DataField: rp_nip_business
DataFieldDescription: News impact projection of business-related news
DataField: rp_nip_price
DataFieldDescription: News impact projection of stock price news
DataField: rp_nip_legal
DataFieldDescription: News impact projection of legal news
DataField: rp_nip_earnings
DataFieldDescription: News impact projection of earnings news
DataField: rp_css_legal
DataFieldDescription: Composite sentiment score of legal news
DataField: rp_ess_ptg
DataFieldDescription: Event sentiment score of price target news
DataField: rp_nip_credit
DataFieldDescription: News impact projection of credit news
DataField: rp_css_ratings
DataFieldDescription: Composite sentiment score of analyst ratings-related news
DataField: rp_css_partner
DataFieldDescription: Composite sentiment score of partnership news
DataField: nws18_ber
DataFieldDescription: News sentiment specializing in earnings result
DataField: rp_css_dividends
DataFieldDescription: Composite sentiment score of dividends news
DataField: rp_ess_legal
DataFieldDescription: Event sentiment score of legal news
DataField: rp_ess_insider
DataFieldDescription: Event sentiment score of insider trading news
DataField: rp_nip_inverstor
DataFieldDescription: News impact projection of investor relations news
DataField: nws18_ghc_lna
DataFieldDescription: Change in analyst recommendation
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: rp_nip_revenue
DataFieldDescription: News impact projection of revenue news
DataField: rp_css_price
DataFieldDescription: Composite sentiment score of stock price news
DataField: rp_css_ptg
DataFieldDescription: Composite sentiment score of price target news
DataField: fnd2_currstatelocaltxexp
DataFieldDescription: Income Tax Expense, Current - State & Local
DataField: fnd2_dbplanartonplas
DataFieldDescription: Defined Benefit Plan, Benefits Paid, Plan Assets
DataField: fn_repayments_of_lines_of_credit_q
DataFieldDescription: Amount of cash outflow for payment of an obligation from a lender, including but not limited to, letter of credit, standby letter of credit and revolving credit arrangements.
DataField: fnd2_dfdtxlbspropplteqm
DataFieldDescription: Amount of deferred tax liability attributable to taxable temporary differences from property, plant, and equipment.
DataField: fnd2_eixrtreclstatelocalitxes
DataFieldDescription: Percentage of the difference between reported income tax expense (benefit) and expected income tax expense (benefit) computed by applying the domestic federal statutory income tax rates to pretax income (loss) from continuing operations applicable to state and local income tax expense (benefit), net of federal tax expense (benefit).
DataField: fnd2_a_flintasamt1expy5
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 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_goodwill_acquired_during_period_a
DataFieldDescription: Amount of increase in asset representing future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized resulting from a business combination.
DataField: fn_def_tax_assets_net_a
DataFieldDescription: Deferred Tax Assets Net Of Valuation Allowance
DataField: fn_accum_oth_income_loss_net_of_tax_a
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: fnd2_a_seniornotes
DataFieldDescription: Including the current and noncurrent portions, carrying value as of the balance sheet date of Notes with the highest claim on the assets of the issuer in case of bankruptcy or liquidation (with maturities initially due after 1 year or beyond the operating cycle if longer). Senior note holders are paid off in full before any payments are made to junior note holders.
DataField: fnd2_dbplanbnfol
DataFieldDescription: 1) For defined benefit pension plans, the benefit obligation is the projected benefit obligation, which is the actuarial present value as of a date of all benefits attributed by the pension benefit formula to employee service rendered prior to that date. 2) For other postretirement defined benefit plans, the benefit obligation is the accumulated postretirement benefit obligation, which is the actuarial present value of benefits attributed to employee service rendered to a particular date.
DataField: fn_comp_options_exercises_weighted_avg_a
DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
DataField: fn_comp_options_exercises_weighted_avg_q
DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
DataField: fn_unrecognized_tax_benefits_a
DataFieldDescription: Amount of unrecognized tax benefits.
DataField: fn_interest_payable_a
DataFieldDescription: Carrying value as of the balance sheet date of [accrued] interest payable on all forms of debt, including trade payables, that has been incurred and is unpaid. 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_comp_not_rec_stock_options_a
DataFieldDescription: Unrecognized cost of unvested stock option awards.
DataField: fn_comprehensive_income_net_of_tax_a
DataFieldDescription: Amount after tax of increase (decrease) in equity from transactions and other events and circumstances from net income and other comprehensive income, attributable to parent entity. Excludes changes in equity resulting from investments by owners and distributions to owners.
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: fn_proceeds_from_issuance_of_debt_a
DataFieldDescription: The cash inflow during the period from additional borrowings in aggregate debt. Includes proceeds from short-term and long-term debt.
DataField: fn_comp_not_rec_a
DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
DataField: fnd2_a_rvndm
DataFieldDescription: Revenue, Domestic
DataField: fn_finite_lived_intangible_assets_net_q
DataFieldDescription: Finite Lived Intangible Assets, Net
DataField: fnd2_a_eplsbvdcpcstnrgsbaoo
DataFieldDescription: Unrecognized cost of unvested other share-based compensation awards.
DataField: fnd2_a_lhdiprtsg
DataFieldDescription: Amount before accumulated depreciation of additions or improvements to assets held under a lease arrangement.
DataField: fn_line_of_credit_facility_max_borrowing_capacity_a
DataFieldDescription: Maximum borrowing capacity under the credit facility without consideration of any current restrictions on the amount that could be borrowed or the amounts currently outstanding under the facility.
DataField: fn_repayments_of_debt_q
DataFieldDescription: The cash outflow during the period from the repayment of aggregate short-term and long-term debt. Excludes payment of capital lease obligations.
DataField: fnd2_unremittedfrer
DataFieldDescription: Unremitted Foreign Earnings
DataField: fnd2_a_sbcpnargmpmtwopsffesip
DataFieldDescription: The number of shares under options that were cancelled during the reporting period as a result of occurrence of a terminating event specified in contractual agreements pertaining to the stock option plan.
DataField: fn_ppne_gross_q
DataFieldDescription: Amount before accumulated depreciation, depletion and amortization of physical assets used in the normal conduct of business and not intended for resale. Examples include, but are not limited to, land, buildings, machinery and equipment, office equipment, and furniture and fixtures.
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: 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 =======================================