任务指令
你是一个WorldQuant WebSim因子工程师。你的任务是生成 20 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
视角一：市场摩擦的横截面成像 (Cross-sectional Imaging of Market Frictions)
核心洞见：传统因子假设市场无摩擦。真正的Alpha可能藏于“摩擦本身”——即指令流（order flow）转化为价格运动时，在不同股票上表现出的效率差异图谱。我们不为消除摩擦建模，而是主动测绘它。
研究提示词 (用于指导因子构建)：
指令流冲击的“消化速率”：测量单位异常交易量（可定义为其z-score）所引发的瞬时价格冲击（如未来1-5分钟收益率），以及该冲击在随后较短时间（如30分钟、1小时）的衰减半衰期。寻找“冲击大但衰减慢”（高摩擦、低流动性）与“冲击小但衰减快”（低摩擦、高流动性）的股票，并研究其横截面收益预测能力。
买卖失衡的“路径依赖”：考察买单流与卖单流（可用正负交易额近似）的净额在时间序列上的自相关性模式。是呈现“均值回归”（失衡迅速被套利消化）还是“趋势持续”（失衡自我强化）？这种模式在不同波动率、不同市值股票中如何变化？能否构建一个度量“订单流趋势持续性”的指标？
价格发现的“领地性”：将价格变化分解为“由自身交易驱动”的部分和“由同行业龙头/指数驱动”的部分（可通过日内收益率与龙头/指数收益率的滚动协方差分解）。计算“自身解释比例”。该比例高的股票，其价格发现是“内生性”的；比例低的则是“外生性”或“跟随性”的。这两类股票在不同市场环境（牛市/熊市/震荡市）下的表现有何系统性差异？
视角二：投资者注意力的生态学建模 (Ecology of Investor Attention)
核心洞见：注意力是金融市场最稀缺的资源。市场不是信息的聚合器，而是注意力资源的分配系统。因子应捕捉注意力的“聚集-分散”、“转移-停滞”动态，而非信息本身。
研究提示词 (用于指导因子构建)：
注意力“聚焦度”与“涣散度”：用交易量在时间序列上的分布熵来衡量。例如，过去20个交易日内，交易量分布的基尼系数或赫芬达尔指数。高集中度（某几天放巨量）意味着注意力短期爆发；低集中度（量能均匀）意味着持续平淡的关注。研究这两种状态转换前后，股票的收益特征。
行业内注意力的“捕食-被捕食”关系：定义“注意力领导者”（如当日涨幅前3的股票）和“注意力追随者”（同行业其他股票）。计算领导股出现后，追随者的成交量与价格在随后N个时间段内的响应速度和强度。这种响应的不对称性（有些股票迅速响应，有些滞后）能否预测未来的相对强弱？
“注意力惯性”与“注意力反转”：度量一个股票在失去短期催化剂（如公告、事件）后，其异常成交量（或搜索量、社交媒体活跃度）回落至基线水平的速度。回落慢的股票具有“注意力惯性”，可能存在定价偏差的持续；回落快的股票则“注意力反转”迅速。构建一个“注意力衰减时间”因子。
视角三：价格运动的“形态语法”与“叙事连贯性” (Morphological Syntax & Narrative Coherence of Price Movements)
核心洞见：价格运动不仅是有方向的，更是有“形状”和“语法”的。如同语言，一段价格走势是否存在合“语法”的叙事结构（如“筑底-突破-回踩-主升”），还是杂乱无章的“噪音词汇”？市场参与者会潜意识地识别并交易这些“连贯叙事”。
研究提示词 (用于指导因子构建)：
K线序列的“可压缩性”：使用一种简化的算法（如趋势线拟合后的残差大小，或一定容忍度下的分段线性近似所需节点数），来衡量过去一段价格序列的信息复杂度。低复杂度（高可压缩性）意味着价格运动清晰、有规律；高复杂度则意味着混乱。研究“从混乱转向清晰”或“从清晰转向混乱”的拐点。
关键价位互动的“叙事逻辑”：识别近期的显著高点、低点、缺口、密集成交区作为“关键叙事节点”。分析当前价格在接近这些节点时的行为：是“尊重”（反弹/受阻）还是“无视”（直接穿越）？一系列对历史节点的“尊重”行为构成了一个连贯的“支撑阻力叙事”。能否量化这种叙事逻辑的强度？
多尺度运动的“相位同步”：选取短期（如5日）、中期（20日）、长期（60日）的价格滤波序列（如移动平均线）。计算它们之间方向变化的领先滞后关系与同步性（可类似相位角分析）。例如，短期均线是否总是率先转向并引导中长期？还是三者经常背离？“多周期共振向上/向下”这种高度同步的状态，其形成过程和瓦解过程有何预测意义？

=== 关键语法规则（必须遵守） ===
1. 数据字段规范：
   - 可使用字段：close, volume, returns
   - ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
   - ✅ 正确：使用volume作为规模代理，close作为价格
   - returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
2. ts_regression使用规范：
   - 避免深度嵌套ts_regression，特别是作为其他函数的参数
   - ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
   - ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
   - 替代方案：用ts_delta组合计算动量变化
3. if_else使用规范：
   - 条件必须是简单布尔表达式
   - 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
   - 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
4. bucket函数使用规范：
   - bucket()返回分组ID，可用于条件判断
   - ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
   - ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
   - 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
=== 关键语法规则结束 ===
*=====*
注意事项:
1. 避免过度复杂的嵌套（建议不超过3层）
2. 每个表达式应有明确的经济逻辑
3. 考虑实际交易可行性（避免未来函数）
4. 包含风险控制元素（如波动率调整）
5. 只能使用可用的数据字段：close, volume, returns等
*=====*
参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现"行业"逻辑。
构建框架指导（请按此逻辑创造新因子）：
维度融合模板（选择至少2个）：
A. 领导力动量 = 时序动量 × 横截面调整
   逻辑：大成交量股票的动量更强
   结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
B. 状态自适应动量 = 条件选择动量
   逻辑：高波动用短期动量，低波动用长期动量
   结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
C. 行业传导因子 = 领先行业动量 × 相关性强度
   逻辑：与强势行业相关性高的行业未来表现好
   结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
D. 情绪反转 = 过度交易信号 × 基础趋势
   逻辑：过度交易时反转，趋势延续时跟随
   结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
关键组件库（可自由组合）：
1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
5. 相关性类：ts_corr({x},{y},{d})
6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
*=====*
输出格式：
输出必须是且仅是纯文本。
每一行是一个完整、独立、语法正确的WebSim表达式。
严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
===================== !!! 重点(输出方式) !!! =====================
现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
表达式
表达式
表达式
...
表达式
=================================================================
重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子：

以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子

========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
Description: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
Description: Absolute value of x
Operator: add(x, y, filter = false)
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
Operator: densify(x)
Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
Operator: divide(x, y)
Description: x / y
Operator: inverse(x)
Description: 1 / x
Operator: log(x)
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
Operator: max(x, y, ..)
Description: Maximum value of all inputs. At least 2 inputs are required
Operator: min(x, y ..)
Description: Minimum value of all inputs. At least 2 inputs are required
Operator: multiply(x ,y, ... , filter=false)
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
Operator: power(x, y)
Description: x ^ y
Operator: reverse(x)
Description: - x
Operator: sign(x)
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
Operator: signed_power(x, y)
Description: x raised to the power of y such that final result preserves sign of x
Operator: sqrt(x)
Description: Square root of x
Operator: subtract(x, y, filter=false)
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
Operator: and(input1, input2)
Description: Logical AND operator, returns true if both operands are true and returns false otherwise
Operator: if_else(input1, input2, input 3)
Description: If input1 is true then return input2 else return input3.
Operator: input1 < input2
Description: If input1 < input2 return true, else return false
Operator: input1 <= input2
Description: Returns true if input1 <= input2, return false otherwise
Operator: input1 == input2
Description: Returns true if both inputs are same and returns false otherwise
Operator: input1 > input2
Description: Logic comparison operators to compares two inputs
Operator: input1 >= input2
Description: Returns true if input1 >= input2, return false otherwise
Operator: input1!= input2
Description: Returns true if both inputs are NOT the same and returns false otherwise
Operator: is_nan(input)
Description: If (input == NaN) return 1 else return 0
Operator: not(x)
Description: Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1).
Operator: or(input1, input2)
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
Operator: days_from_last_change(x)
Description: Amount of days since last change of x
Operator: hump(x, hump = 0.01)
Description: Limits amount and magnitude of changes in input (thus reducing turnover)
Operator: kth_element(x, d, k)
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
Operator: last_diff_value(x, d)
Description: Returns last x value not equal to current x value from last d days
Operator: ts_arg_max(x, d)
Description: Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1
Operator: ts_arg_min(x, d)
Description: Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1.
Operator: ts_av_diff(x, d)
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
Operator: ts_corr(x, y, d)
Description: Returns correlation of x and y for the past d days
Operator: ts_count_nans(x ,d)
Description: Returns the number of NaN values in x for the past d days
Operator: ts_covariance(y, x, d)
Description: Returns covariance of y and x for the past d days
Operator: ts_decay_linear(x, d, dense = false)
Description: Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not.
Operator: ts_delay(x, d)
Description: Returns x value d days ago
Operator: ts_delta(x, d)
Description: Returns x - ts_delay(x, d)
Operator: ts_mean(x, d)
Description: Returns average value of x for the past d days.
Operator: ts_product(x, d)
Description: Returns product of x for the past d days
Operator: ts_quantile(x,d, driver="gaussian" )
Description: It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default.
Operator: ts_rank(x, d, constant = 0)
Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
Description: Returns various parameters related to regression function
Operator: ts_scale(x, d, constant = 0)
Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
Operator: ts_std_dev(x, d)
Description: Returns standard deviation of x for the past d days
Operator: ts_step(1)
Description: Returns days' counter
Operator: ts_sum(x, d)
Description: Sum values of x for the past d days.
Operator: ts_zscore(x, d)
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
Operator: normalize(x, useStd = false, limit = 0.0)
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
Operator: quantile(x, driver = gaussian, sigma = 1.0)
Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector
Operator: rank(x, rate=2)
Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0
Operator: scale(x, scale=1, longscale=1, shortscale=1)
Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
Operator: winsorize(x, std=4)
Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
Operator: zscore(x)
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
Operator: vec_avg(x)
Description: Taking mean of the vector field x
Operator: vec_sum(x)
Description: Sum of vector field x
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
Operator: trade_when(x, y, z)
Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition
Operator: group_backfill(x, group, d, std = 4.0)
Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
Operator: group_mean(x, weight, group)
Description: All elements in group equals to the mean
Operator: group_neutralize(x, group)
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
Operator: group_rank(x, group)
Description: Each elements in a group is assigned the corresponding rank in this group
Operator: group_scale(x, group)
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
Operator: group_zscore(x, group)
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
========================= 操作符结束 =======================================

========================= 数据字段开始 =======================================注意: DataField: 后面的是数据字段, DataFieldDescription: 此字段后面的是数据字段对应的描述或使用说明, DataFieldDescription字段后面的内容是使用说明, 不是数据字段

DataField: call_breakeven_20
DataFieldDescription: Price at which a stock's call options with expiration 20 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_60
DataFieldDescription: Price at which a stock's put options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_all
DataFieldDescription: Ratio of put volume to call volume for all maturities on stock's options.
DataField: forward_price_180
DataFieldDescription: Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.
DataField: 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: 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: pcr_vol_20
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 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: 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: 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: 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: 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: pcr_vol_1080
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 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_vol_10
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
DataField: pcr_vol_270
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 270 days in the future.
DataField: pcr_vol_90
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 90 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: call_breakeven_10
DataFieldDescription: Price at which a stock's call options with expiration 10 days in the future break even based on its recent bid/ask mean.
DataField: call_breakeven_60
DataFieldDescription: Price at which a stock's call options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_60
DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: forward_price_270
DataFieldDescription: Forward price at 270 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: 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: 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_120
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 120 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: 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_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_10
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
DataField: put_breakeven_150
DataFieldDescription: Price at which a stock's put options with expiration 150 days in the future break even based on its recent bid/ask mean.
DataField: fnd6_cidergl
DataFieldDescription: Comp Inc - Derivative Gains/Losses
DataField: fnd6_newqv1300_spceepspq
DataFieldDescription: S&P Core Earnings EPS Basic - Preliminary
DataField: fnd6_txo
DataFieldDescription: Income Taxes - Other
DataField: fnd6_txts
DataFieldDescription: Income Taxes
DataField: fnd6_newqeventv110_oepf12
DataFieldDescription: Earnings Per Share - Diluted - from Operations - 12MM
DataField: fnd6_newa1v1300_ebitda
DataFieldDescription: Earnings Before Interest
DataField: fnd6_newqv1300_txtq
DataFieldDescription: Income Taxes - Total
DataField: fnd6_currencyqv1300_curcd
DataFieldDescription: ISO Currency Code - Company Annual Market
DataField: fnd6_newqeventv110_dteaq
DataFieldDescription: Extinguishment of Debt After-tax
DataField: fnd6_prclq
DataFieldDescription: Price Low - Quarter
DataField: fnd6_newqv1300_reunaq
DataFieldDescription: Unadjusted Retained Earnings
DataField: fnd6_newqeventv110_drltq
DataFieldDescription: Deferred Revenue - Long-term
DataField: fnd6_newa1v1300_epspi
DataFieldDescription: Earnings Per Share (Basic) - Including Extraordinary Items
DataField: fnd6_newqeventv110_rdipq
DataFieldDescription: In Process R&D
DataField: fnd6_ivch
DataFieldDescription: Increase in Investments
DataField: fnd6_newa1v1300_ceqt
DataFieldDescription: Common Equity - Tangible
DataField: fnd6_newa1v1300_dvc
DataFieldDescription: Dividends Common/Ordinary
DataField: fnd6_newqeventv110_cipenq
DataFieldDescription: Comp Inc - Minimum Pension Adj
DataField: fnd6_newa2v1300_xopteps
DataFieldDescription: Implied Option EPS Basic
DataField: fnd6_state
DataFieldDescription: integer for identifying the state of the company
DataField: fnd6_newqeventv110_intanoq
DataFieldDescription: Other Intangibles
DataField: fnd6_txdfed
DataFieldDescription: Deferred Taxes - Federal
DataField: fnd6_newa1v1300_emp
DataFieldDescription: Employees
DataField: fnd6_txtubpospinc
DataFieldDescription: Increase - Prior Tax Positions
DataField: fnd6_newqeventv110_rdipepsq
DataFieldDescription: In-Process R&D Expense Basic EPS Effect
DataField: fnd6_newqeventv110_spiq
DataFieldDescription: Special Items
DataField: fnd6_cptnewqv1300_ltq
DataFieldDescription: Liabilities - Total
DataField: fnd6_acdo
DataFieldDescription: Current Assets of Discontinued Operations
DataField: fnd6_cshr
DataFieldDescription: Common/Ordinary Shareholders
DataField: fnd6_mfma2_oancf
DataFieldDescription: Operating Activities - Net Cash Flow
DataField: scl12_alltype_buzzvec
DataFieldDescription: sentiment volume
DataField: scl12_alltype_sentvec
DataFieldDescription: sentiment
DataField: scl12_alltype_typevec
DataFieldDescription: instrument type index
DataField: scl12_buzz
DataFieldDescription: relative sentiment volume
DataField: scl12_buzz_fast_d1
DataFieldDescription: relative sentiment volume
DataField: scl12_buzzvec
DataFieldDescription: sentiment volume
DataField: scl12_sentiment
DataFieldDescription: sentiment
DataField: scl12_sentiment_fast_d1
DataFieldDescription: sentiment
DataField: scl12_sentvec
DataFieldDescription: sentiment
DataField: scl12_typevec
DataFieldDescription: instrument type index
DataField: snt_buzz
DataFieldDescription: negative relative sentiment volume, fill nan with 0
DataField: snt_buzz_bfl
DataFieldDescription: negative relative sentiment volume, fill nan with 1
DataField: snt_buzz_bfl_fast_d1
DataFieldDescription: negative relative sentiment volume, fill nan with 1
DataField: snt_buzz_fast_d1
DataFieldDescription: negative relative sentiment volume, fill nan with 0
DataField: snt_buzz_ret
DataFieldDescription: negative return of relative sentiment volume
DataField: snt_buzz_ret_fast_d1
DataFieldDescription: negative return of relative sentiment volume
DataField: snt_value
DataFieldDescription: negative sentiment, fill nan with 0
DataField: snt_value_fast_d1
DataFieldDescription: negative sentiment, fill nan with 0
DataField: analyst_revision_rank_derivative
DataFieldDescription: Change in ranking for analyst revisions and momentum compared to previous period.
DataField: cashflow_efficiency_rank_derivative
DataFieldDescription: Change in ranking for cash flow generation and profitability compared to previous period.
DataField: composite_factor_score_derivative
DataFieldDescription: Change in overall composite factor score from the prior period.
DataField: earnings_certainty_rank_derivative
DataFieldDescription: Change in ranking for earnings sustainability and certainty compared to previous period.
DataField: fscore_bfl_growth
DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
DataField: fscore_bfl_momentum
DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
DataField: fscore_bfl_profitability
DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
DataField: fscore_bfl_quality
DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
DataField: fscore_bfl_surface
DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
DataField: fscore_bfl_surface_accel
DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
DataField: fscore_bfl_total
DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
DataField: fscore_bfl_value
DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
DataField: fscore_growth
DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
DataField: fscore_momentum
DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
DataField: fscore_profitability
DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
DataField: fscore_quality
DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
DataField: fscore_surface
DataFieldDescription: The static score. An index between 0 & 100 is applied for each stock and each composite factor - The first ranking is a pentagon surface-based score. The larger the surface, the higher the rank.
DataField: fscore_surface_accel
DataFieldDescription: The derivative score. In a second step, we calculate the derivative of this score (ie: Is the surface of the pentagon increasing or decreasing from the previous month?).
DataField: fscore_total
DataFieldDescription: The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.
DataField: fscore_value
DataFieldDescription: The purpose of this metric is to see if the stock is under or overpriced given several well known valuation standards.
DataField: growth_potential_rank_derivative
DataFieldDescription: Change in ranking for medium-term growth potential compared to previous period.
DataField: multi_factor_acceleration_score_derivative
DataFieldDescription: Change in the acceleration of multi-factor score compared to previous period.
DataField: multi_factor_static_score_derivative
DataFieldDescription: Change in static multi-factor score compared to previous period.
DataField: relative_valuation_rank_derivative
DataFieldDescription: Change in ranking for valuation metrics compared to previous period.
DataField: snt_social_value
DataFieldDescription: Z score of sentiment
DataField: snt_social_volume
DataFieldDescription: Normalized tweet volume
DataField: beta_last_30_days_spy
DataFieldDescription: Beta to SPY in 30 Days
DataField: beta_last_360_days_spy
DataFieldDescription: Beta to SPY in 360 Days
DataField: beta_last_60_days_spy
DataFieldDescription: Beta to SPY in 60 Days
DataField: beta_last_90_days_spy
DataFieldDescription: Beta to SPY in 90 Days
DataField: correlation_last_30_days_spy
DataFieldDescription: Correlation to SPY in 30 Days
DataField: correlation_last_360_days_spy
DataFieldDescription: Correlation to SPY in 360 Days
DataField: correlation_last_60_days_spy
DataFieldDescription: Correlation to SPY in 60 Days
DataField: correlation_last_90_days_spy
DataFieldDescription: Correlation to SPY in 90 Days
DataField: systematic_risk_last_30_days
DataFieldDescription: Systematic Risk Last 30 Days
DataField: systematic_risk_last_360_days
DataFieldDescription: Systematic Risk Last 360 Days
DataField: systematic_risk_last_60_days
DataFieldDescription: Systematic Risk Last 60 Days
DataField: systematic_risk_last_90_days
DataFieldDescription: Systematic Risk Last 90 Days
DataField: unsystematic_risk_last_30_days
DataFieldDescription: Unsystematic Risk Last 30 Days - Relative to SPY
DataField: unsystematic_risk_last_360_days
DataFieldDescription: Unsystematic Risk Last 360 Days - Relative to SPY
DataField: unsystematic_risk_last_60_days
DataFieldDescription: Unsystematic Risk Last 60 Days - Relative to SPY
DataField: unsystematic_risk_last_90_days
DataFieldDescription: Unsystematic Risk Last 90 Days - Relative to SPY
DataField: anl4_tbvps_number
DataFieldDescription: Tangible Book Value per Share - number of estimations
DataField: anl4_adxqfv110_low
DataFieldDescription: The lowest estimation
DataField: anl4_netprofita_value
DataFieldDescription: Adjusted net income- announced financial value
DataField: pretax_income_reported_min_guidance_qtr
DataFieldDescription: Reported Pretax income- minimum guidance value
DataField: anl4_qfd1_azeps
DataFieldDescription: EPS - aggregation on estimations, 50th percentile
DataField: cashflow_per_share_estimate_count
DataFieldDescription: Cash Flow Per Share - number of estimations - delay1
DataField: anl4_netdebt_flag
DataFieldDescription: Net debt - forecast type (revision/new/...)
DataField: earnings_per_share_standard_deviation
DataFieldDescription: Earnings per share - standard deviation of estimations
DataField: anl4_netdebt_high
DataFieldDescription: Net debt - the highest estimation
DataField: min_shareholders_equity_guidance
DataFieldDescription: Minimum guidance value for Shareholders' Equity
DataField: est_tbv_ps
DataFieldDescription: Tangible Book Value per Share - mean of estimations
DataField: anl4_ads1detailqfv110_person
DataFieldDescription: Broker Id
DataField: anl4_cfo_number
DataFieldDescription: Cash Flow From Operations - number of estimations
DataField: operating_profit_before_depr_amort_min_guidance_qtr
DataFieldDescription: Minimum guidance value for Earnings before interest, taxes, depreciation and amortization
DataField: anl4_ebit_number
DataFieldDescription: Earnings before interest and taxes - number of estimations
DataField: anl4_dei3lltv110_item
DataFieldDescription: Financial item
DataField: est_rd_expense
DataFieldDescription: Research and Development Expense - mean of estimations
DataField: earnings_per_share_estimate_count
DataFieldDescription: Earnings per share - number of estimations
DataField: est_fcf
DataFieldDescription: Free Cash Flow - Mean of Estimations
DataField: anl4_eaz2lqfv110_person
DataFieldDescription: Broker Id
DataField: anl4_dei3lqfv110_item
DataFieldDescription: Financial item
DataField: max_adjusted_funds_from_operations_guidance_2
DataFieldDescription: Adjusted funds from operation - maximum guidance value for the annual period
DataField: anl4_rd_exp_median
DataFieldDescription: Research and Development Expense - Median of estimations
DataField: anl4_cuo1conafv110_item
DataFieldDescription: Financial item
DataField: anl4_cfo_mean
DataFieldDescription: Cash Flow From Operations - mean of estimations
DataField: anl4_ptp_high
DataFieldDescription: Pretax income - the highest estimation
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_adxqfv110_mean
DataFieldDescription: Mean of estimations
DataField: anl4_basicconqfv110_mean
DataFieldDescription: Mean of estimations
DataField: min_free_cashflow_guidance
DataFieldDescription: Minimum guidance value for Free Cash Flow
DataField: pv13_h_min10_all_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min25_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f2_sector
DataFieldDescription: grouping fields
DataField: pv13_new_5l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_only_sector
DataFieldDescription: grouping fields
DataField: pv13_di_5l
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_sector
DataFieldDescription: grouping fields
DataField: pv13_reportperiodlen
DataFieldDescription: The number of units which the report covers prior to the stated end date
DataField: pv13_revere_country
DataFieldDescription: Country code
DataField: pv13_revere_company_total
DataFieldDescription: Total number of companies in the sector
DataField: pv13_r2_liquid_min2_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min30_sector
DataFieldDescription: grouping fields
DataField: rel_num_all
DataFieldDescription: number of the companies whose product overlapped with the instrument
DataField: pv13_new_2l_scibr
DataFieldDescription: grouping fields
DataField: single_sector_pureplay_company_count
DataFieldDescription: Number of companies exclusively operating in a single sector.
DataField: pv13_rha2_min20_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_1k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_new_1l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_level
DataFieldDescription: Level of the sector within the hierarchy
DataField: pv13_new_3l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_sector_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min100_corr21_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min40_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_parent
DataFieldDescription: Code of parent sector
DataField: pv13_ustomergraphrank_auth_rank
DataFieldDescription: the HITS authority score of customers
DataField: pv13_rha2_min10_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_top3000_513_sector
DataFieldDescription: grouping fields
DataField: historical_volatility_60
DataFieldDescription: Close-to-close Historical volatility over 60 days
DataField: implied_volatility_mean_60
DataFieldDescription: At-the-money option-implied volatility mean for 60 days
DataField: implied_volatility_call_120
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: implied_volatility_put_1080
DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
DataField: implied_volatility_call_30
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
DataField: implied_volatility_mean_skew_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
DataField: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: historical_volatility_30
DataFieldDescription: Close-to-close Historical volatility over 30 days
DataField: implied_volatility_mean_skew_720
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
DataField: implied_volatility_put_10
DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
DataField: implied_volatility_call_720
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
DataField: implied_volatility_put_120
DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
DataField: implied_volatility_mean_30
DataFieldDescription: At-the-money option-implied volatility mean for 30 days
DataField: implied_volatility_put_30
DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
DataField: implied_volatility_mean_10
DataFieldDescription: At-the-money option-implied volatility mean for 10 days
DataField: parkinson_volatility_150
DataFieldDescription: Parkinson model's historical volatility over 150 days
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_mean_270
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
DataField: implied_volatility_put_720
DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
DataField: implied_volatility_call_60
DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 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: parkinson_volatility_120
DataFieldDescription: Parkinson model's historical volatility over 120 days
DataField: implied_volatility_mean_skew_60
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
DataField: parkinson_volatility_180
DataFieldDescription: Parkinson model's historical volatility over 180 days
DataField: historical_volatility_180
DataFieldDescription: Close-to-close Historical volatility over 180 days
DataField: implied_volatility_mean_150
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: nws12_allz_newssess
DataFieldDescription: Index of session in which the news was reported
DataField: nws12_prez_result_vs_index
DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
DataField: nws12_afterhsz_5p
DataFieldDescription: The minimum of L or S above for 5-minute bucket
DataField: nws12_mainz_allvwap
DataFieldDescription: Volume weighted average price of all sessions
DataField: nws12_afterhsz_02p
DataFieldDescription: The minimum of L or S above for 20-minute bucket
DataField: news_mins_10_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
DataField: nws12_mainz_prevclose
DataFieldDescription: Previous trading day's close price
DataField: nws12_prez_maxupamt
DataFieldDescription: The after-the-news high minus the price at the time of the news
DataField: nws12_prez_maxdown
DataFieldDescription: Percent change from the price at the time of the news to the after the news low
DataField: nws12_afterhsz_highexcstddev
DataFieldDescription: (EODHigh - TONLast)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
DataField: nws12_afterhsz_open_vol
DataFieldDescription: Main open volume
DataField: nws12_allz_provider
DataFieldDescription: index of name of the news provider
DataField: nws12_allz_result1
DataFieldDescription: Percent change between the price at the time of the news release and the price at the close of the session
DataField: nws12_afterhsz_4p
DataFieldDescription: The minimum of L or S above for 4-minute bucket
DataField: nws12_prez_02s
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
DataField: nws12_afterhsz_1l
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage points
DataField: news_mins_3_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
DataField: news_mins_1_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_prez_1l
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_prez_01s
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
DataField: nws12_prez_tonlow
DataFieldDescription: Lowest price reached during the session before the time of the news
DataField: nws12_mainz_120_min
DataFieldDescription: The percent change in price in the first 120 minutes following the news release
DataField: news_mins_5_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 5 percentage points
DataField: nws12_prez_eodvwap
DataFieldDescription: Volume-weighted average price between the time of news and the end of the session
DataField: news_mins_1_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
DataField: nws12_prez_rangestddev
DataFieldDescription: (RangeAmt-AvgRange)/RangeStdDev, where AvgRange is the average of the daily range, and RangeStdDev is one standard deviation for the daily range, both for 30 calendar days
DataField: nws12_mainz_5s
DataFieldDescription: Number of minutes that elapsed before price went down 5 percentage points
DataField: nws12_prez_eodhigh
DataFieldDescription: Highest price reached between the time of news and the end of the session
DataField: nws12_mainz_vol_ratio
DataFieldDescription: Curr_Vol / Mov_Vol
DataField: nws12_prez_curr_vol
DataFieldDescription: Current day's session volume
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
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_css_marketing
DataFieldDescription: Composite sentiment score of marketing news
DataField: rp_css_partner
DataFieldDescription: Composite sentiment score of partnership news
DataField: rp_ess_labor
DataFieldDescription: Event sentiment score of labor issues news
DataField: rp_nip_labor
DataFieldDescription: News impact projection of labor issues news
DataField: rp_ess_equity
DataFieldDescription: Event sentiment score of equity action news
DataField: rp_ess_credit
DataFieldDescription: Event sentiment score of credit news
DataField: rp_nip_dividends
DataFieldDescription: News impact projection of dividends news
DataField: nws18_qcm
DataFieldDescription: News sentiment of relevant news with high confidence
DataField: rp_ess_dividends
DataFieldDescription: Event sentiment score of dividends news
DataField: rp_nip_business
DataFieldDescription: News impact projection of business-related news
DataField: rp_ess_ratings
DataFieldDescription: Event sentiment score of analyst ratings-related news
DataField: rp_nip_insider
DataFieldDescription: News impact projection of insider trading news
DataField: rp_ess_partner
DataFieldDescription: Event sentiment score of partnership news
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: rp_nip_price
DataFieldDescription: News impact projection of stock price news
DataField: rp_ess_society
DataFieldDescription: Event sentiment score of society-related news
DataField: nws18_acb
DataFieldDescription: News sentiment specializing in corporate action announcements
DataField: rp_nip_partner
DataFieldDescription: News impact projection of partnership news
DataField: rp_css_technical
DataFieldDescription: Composite sentiment score based on technical analysis
DataField: rp_ess_business
DataFieldDescription: Event sentiment score of business-related news
DataField: rp_css_product
DataFieldDescription: Composite sentiment score of product and service-related news
DataField: nws18_relevance
DataFieldDescription: Relevance of news to the company
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: rp_ess_legal
DataFieldDescription: Event sentiment score of legal news
DataField: rp_nip_society
DataFieldDescription: News impact projection of society-related news
DataField: rp_nip_ptg
DataFieldDescription: News impact projection of price target news
DataField: rp_css_insider
DataFieldDescription: Composite sentiment score of insider trading news
DataField: nws18_bee
DataFieldDescription: News sentiment specializing in growth of earnings
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_effect_of_exchange_rate_on_cash_and_equiv_a
DataFieldDescription: Amount of increase (decrease) from the effect of exchange rate changes on cash and cash equivalent balances held in foreign currencies.
DataField: fn_debt_issuance_costs_q
DataFieldDescription: Amount of debt issuance costs (for example, but not limited to, legal, accounting, broker, and regulatory fees).
DataField: fnd2_unrgtxbnfdcfpprdtxpss
DataFieldDescription: Amount of decrease in unrecognized tax benefits resulting from tax positions that have been or will be taken in current period tax return.
DataField: fn_liab_fair_val_q
DataFieldDescription: Liabilities Fair Value, Recurring, Total
DataField: fnd2_a_fedstyitxrt
DataFieldDescription: Effective Income Tax Rate Reconciliation - Federal Statutory Income Tax Rate %
DataField: fn_payments_for_repurchase_of_common_stock_a
DataFieldDescription: Value reported on Cash Flow Statement. May include shares repurchased as part of a buyback plan, as well as shares purchased for employee compensation, etc.
DataField: fn_incremental_shares_attributable_to_share_based_payment_a
DataFieldDescription: Additional shares included in the calculation of diluted EPS as a result of the potentially dilutive effect of share-based payment arrangements using the treasury stock method.
DataField: fn_op_lease_min_pay_due_after_5y_a
DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of one year due after 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: fnd2_a_dbplanepdrtnplas
DataFieldDescription: An amount calculated as a basis for determining the extent of delayed recognition of the effects of changes in the fair value of assets. The expected return on plan assets is determined based on the expected long-term rate of return on plan assets and the market-related value of plan assets.
DataField: fnd2_dfdfritxexp
DataFieldDescription: Income Tax Expense, Deferred - Foreign
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_options_exercisable_number_a
DataFieldDescription: The number of shares into which fully or partially vested stock options outstanding as of the balance sheet date can be currently converted under the option plan.
DataField: fn_comp_options_exercisable_weighted_avg_a
DataFieldDescription: The weighted-average price as of the balance sheet date at which grantees can acquire the shares reserved for issuance on vested portions of options outstanding and currently exercisable under the stock option plan.
DataField: fnd2_a_sbcpnatqsttotnsvdptfv
DataFieldDescription: Fair value of share-based awards for which the grantee gained the right by satisfying service and performance requirements, to receive or retain shares or units, other instruments, or cash.
DataField: fnd2_dbplanbnfpaid
DataFieldDescription: The amount of payments made for which participants are entitled under a pension plan, including pension benefits, death benefits, and benefits due on termination of employment. Also includes payments made under a postretirement benefit plan, including prescription drug benefits, health care benefits, life insurance benefits, and legal, educational and advisory services. This item represents a periodic decrease to the plan obligations and a decrease to plan assets.
DataField: fnd2_dbplanepdfbnfpytwo
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_def_income_tax_expense_q
DataFieldDescription: Income Tax Expense, Deferred
DataField: fn_treasury_stock_shares_q
DataFieldDescription: Number of common and preferred shares that were previously issued and that were repurchased by the issuing entity and held in treasury on the financial statement date. This stock has no voting rights and receives no dividends.
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: fn_comp_options_out_number_q
DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
DataField: fn_comp_not_rec_a
DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
DataField: fn_liab_fair_val_l2_a
DataFieldDescription: Liabilities Fair Value, Recurring, Level 2
DataField: fn_debt_issuance_costs_a
DataFieldDescription: Amount of debt issuance costs (for example, but not limited to, legal, accounting, broker, and regulatory fees).
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: fnd2_dfdlocalitxexp
DataFieldDescription: Income Tax Expense, Deferred
DataField: fn_repurchased_shares_a
DataFieldDescription: Number of shares that have been repurchased during the period.
DataField: fn_interest_payable_q
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_debt_instrument_interest_rate_stated_percentage_a
DataFieldDescription: Stated percentage of interest rate on debt
DataField: fn_comprehensive_income_net_of_tax_q
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: 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
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