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: option_breakeven_120
DataFieldDescription: Price at which a stock's options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_180
DataFieldDescription: Price at which a stock's put options with expiration 180 days in the future break even based on its recent bid/ask mean.
DataField: 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_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: 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_90
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
DataField: put_breakeven_270
DataFieldDescription: Price at which a stock's put options with expiration 270 days in the future break even based on its recent bid/ask mean.
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: call_breakeven_720
DataFieldDescription: Price at which a stock's call options with expiration 720 days in the future break even based on its recent bid/ask mean.
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: 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: put_breakeven_360
DataFieldDescription: Price at which a stock's put options with expiration 360 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_90
DataFieldDescription: Price at which a stock's put options with expiration 90 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_20
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 20 days in the future.
DataField: call_breakeven_1080
DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
DataField: 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_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: 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: 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: 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_60
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
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: forward_price_720
DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: pcr_vol_150
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
DataField: pcr_oi_all
DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
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: put_breakeven_120
DataFieldDescription: Price at which a stock's put options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: 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_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: capex
DataFieldDescription: Capital Expenditures
DataField: fnd6_eventv110_optlifeq
DataFieldDescription: Life of Options - Assumption (# yrs)
DataField: fnd6_newqv1300_stkcoq
DataFieldDescription: Stock Compensation Expense
DataField: debt_lt
DataFieldDescription: Long-Term Debt - Total
DataField: fnd6_newqeventv110_xidoq
DataFieldDescription: Extraordinary Items and Discontinued Operations
DataField: fnd6_newa1v1300_lt
DataFieldDescription: Liabilities - Total
DataField: fnd6_cptnewqv1300_oeps12
DataFieldDescription: Earnings Per Share from Operations - 12 Months Moving
DataField: fnd6_ci
DataFieldDescription: Comprehensive Income - Total
DataField: fnd6_eventv110_dteepsq
DataFieldDescription: Extinguishment of Debt Basic EPS Effect
DataField: fnd6_newqeventv110_gdwlamq
DataFieldDescription: Amortization of Goodwill
DataField: fnd6_newqv1300_cshiq
DataFieldDescription: Common Shares Issued
DataField: fnd6_capxv
DataFieldDescription: Capital Expend Property, Plant and Equipment Schd V
DataField: fnd6_newqeventv110_prcpepsq
DataFieldDescription: Core Post-Retirement Adjustment Basic EPS Effect Preliminary
DataField: fnd6_stkcpa
DataFieldDescription: After-tax stock compensation
DataField: fnd6_oiadps
DataFieldDescription: Operating Income after Depreciation
DataField: fnd6_xidos
DataFieldDescription: Extraordinary Items and Discontinued Operations
DataField: fnd6_am
DataFieldDescription: Amortization of Intangibles
DataField: fnd6_txdfed
DataFieldDescription: Deferred Taxes - Federal
DataField: fnd6_newqv1300_cshprq
DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - Basic
DataField: fnd6_newqeventv110_ciq
DataFieldDescription: Comprehensive Income - Total
DataField: fnd6_cptnewqv1300_dpq
DataFieldDescription: Depreciation and Amortization - Total
DataField: fnd6_newqeventv110_cstkeq
DataFieldDescription: Common Stock Equivalents - Dollar Savings
DataField: fnd6_newa1v1300_dv
DataFieldDescription: Cash Dividends (Cash Flow)
DataField: fnd6_pidom
DataFieldDescription: Pretax Income - Domestic
DataField: fnd6_mfmq_dlcq
DataFieldDescription: Debt in Current Liabilities
DataField: fnd6_newa1v1300_dcom
DataFieldDescription: Deferred Compensation
DataField: fnd6_dd1
DataFieldDescription: Long-Term Debt Due in 1 Year
DataField: fnd6_newa1v1300_aul3
DataFieldDescription: Assets Level 3 (Unobservable)
DataField: fnd6_eventv110_pncdq
DataFieldDescription: Core Pension Adjustment Diluted EPS Effect
DataField: fnd6_newqv1300_ppegtq
DataFieldDescription: Property, Plant and Equipment - Total (Gross) - Quarterly
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_fcfps_number
DataFieldDescription: Free Cash Flow per Share - number of estimations
DataField: cash_flow_from_operations
DataFieldDescription: Cash Flow from Operations - Value for the annual period
DataField: anl4_cuo1actualqfv110_item
DataFieldDescription: Financial item
DataField: anl4_basicconltv110_low
DataFieldDescription: The lowest estimation
DataField: anl4_ads1detailqfv110_prevval
DataFieldDescription: The previous estimation of financial item
DataField: max_research_development_expense_guidance
DataFieldDescription: The maximum guidance value for Research and Development Expense.
DataField: anl4_netprofita_mean
DataFieldDescription: Adjusted net income - mean of estimations
DataField: est_netprofit_adj
DataFieldDescription: Adjusted net income - Mean of estimations
DataField: anl4_basicconltv110_median
DataFieldDescription: Median of estimations
DataField: max_book_value_per_share_guidance
DataFieldDescription: Book value per share - Maximum value among forecasts
DataField: anl4_qfd1_az_div_number
DataFieldDescription: Dividend per share - number of estimations
DataField: anl4_basicconltv110_down
DataFieldDescription: Number of lower estimations
DataField: anl4_qfv4_maxguidance
DataFieldDescription: Max guidance value
DataField: anl4_afv4_median_eps
DataFieldDescription: Earnings per share - median of estimations
DataField: anl4_qf_az_div_mean
DataFieldDescription: Dividend per share - average of estimations
DataField: anl4_basicconqfv110_high
DataFieldDescription: The highest estimation
DataField: anl4_ptp_mean
DataFieldDescription: Pretax income - mean of estimations
DataField: sales_max_guidance_quarterly
DataFieldDescription: The maximum guidance value for sales.
DataField: anl4_ptp_number
DataFieldDescription: Pretax Income - number of estimations
DataField: anl4_dez1afv4_est
DataFieldDescription: Estimation value
DataField: cashflow_per_share_maximum
DataFieldDescription: Cash Flow - The highest estimation, per share, with a delay of 1 quarter
DataField: est_rd_expense
DataFieldDescription: Research and Development Expense - mean of estimations
DataField: anl4_cfo_low
DataFieldDescription: Cash Flow From Operations - The lowest estimation
DataField: est_epsa
DataFieldDescription: Earnings per share adjusted by excluding extraordinary items and stock option expenses - average of estimations
DataField: anl4_qfv4_dts_spe
DataFieldDescription: Earnings per share - standard deviation of estimations
DataField: anl4_eaz2lafv110_bk
DataFieldDescription: Broker name (int)
DataField: anl4_qf_az_cfps_median
DataFieldDescription: Cash Flow Per Share - Median value among forecasts
DataField: max_tangible_book_value_per_share_guidance
DataFieldDescription: Tangible Book Value per Share - maximum guidance value
DataField: anl4_netprofita_std
DataFieldDescription: Adjusted net income - std of estimations
DataField: anl4_eaz1lqfv110_estvalue
DataFieldDescription: Estimation value
DataField: pv13_reportperiodend
DataFieldDescription: Stated end date for the report
DataField: pv13_r2_min5_1000_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_auth_rank
DataFieldDescription: the HITS authority score of customers
DataField: pv13_reportperiodlen
DataFieldDescription: The number of units which the report covers prior to the stated end date
DataField: pv13_rha2_min10_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy23_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_liquid_min10_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_index_value
DataFieldDescription: Value of specified index for the date
DataField: pv13_r2_min5_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_hub_rank
DataFieldDescription: the HITS hub score of customers
DataField: pv13_di_6l
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_top3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min2_focused_sector
DataFieldDescription: Grouping fields for top 200
DataField: pv13_hierarchy_min5_corr21_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_new_4l_scibr
DataFieldDescription: grouping fields
DataField: rel_num_comp
DataFieldDescription: number of the instrument's competitors
DataField: pv13_hierarchy_min10_3k_all_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min2_focused_pureplay_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_term
DataFieldDescription: Indicates when a sector is the terminal sector (i.e., no sub-sectors)
DataField: pv13_rha2_min10_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchys32_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_key_sector_total
DataFieldDescription: Number of key focus sectors for the company
DataField: pv13_r2_liquid_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min52_1k_sector
DataFieldDescription: Grouping fields for top 1000
DataField: pv13_hierarchy_min30_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_1000_513_sector
DataFieldDescription: grouping fields
DataField: historical_volatility_180
DataFieldDescription: Close-to-close Historical volatility over 180 days
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_mean_90
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
DataField: implied_volatility_call_180
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
DataField: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: implied_volatility_put_720
DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
DataField: implied_volatility_mean_skew_60
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: implied_volatility_put_270
DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
DataField: implied_volatility_mean_skew_720
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
DataField: implied_volatility_mean_skew_150
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
DataField: implied_volatility_call_1080
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: implied_volatility_mean_1080
DataFieldDescription: At-the-money option-implied volatility mean for 3 years
DataField: implied_volatility_call_720
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
DataField: historical_volatility_90
DataFieldDescription: Close-to-close Historical volatility over 90 days
DataField: implied_volatility_mean_60
DataFieldDescription: At-the-money option-implied volatility mean for 60 days
DataField: implied_volatility_put_30
DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
DataField: implied_volatility_mean_skew_30
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
DataField: implied_volatility_mean_skew_180
DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
DataField: implied_volatility_mean_150
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
DataField: parkinson_volatility_180
DataFieldDescription: Parkinson model's historical volatility over 180 days
DataField: implied_volatility_mean_skew_360
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
DataField: parkinson_volatility_150
DataFieldDescription: Parkinson model's historical volatility over 150 days
DataField: implied_volatility_mean_180
DataFieldDescription: At-the-money option-implied volatility mean for 180 days
DataField: implied_volatility_mean_skew_90
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
DataField: implied_volatility_call_150
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
DataField: historical_volatility_120
DataFieldDescription: Close-to-close Historical volatility over 120 days
DataField: implied_volatility_call_20
DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
DataField: nws12_prez_02s
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
DataField: nws12_mainz_4p
DataFieldDescription: The minimum of L or S above for 4 minute bucket
DataField: news_mins_4_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 4 percentage points
DataField: nws12_prez_range
DataFieldDescription: Session High Price - Session Low Price) / Session Low Price.
DataField: nws12_afterhsz_allticks
DataFieldDescription: Total number of ticks for the trading day
DataField: nws12_prez_newrecord
DataFieldDescription: Tracks whether the news is the first instance or a duplicate
DataField: news_eod_low
DataFieldDescription: Lowest price reached between the time of news and the end of the session
DataField: nws12_mainz_mktcap
DataFieldDescription: Reported market capitalization for the calendar day of the session
DataField: news_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: nws12_afterhsz_3p
DataFieldDescription: The minimum of L or S above for 3-minute bucket
DataField: news_mins_10_chg
DataFieldDescription: The minimum of L or S above for 10-minute bucket
DataField: nws12_prez_1p
DataFieldDescription: The minimum of L or S above for 1-minute bucket
DataField: nws12_mainz_4l
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
DataField: news_ton_low
DataFieldDescription: Lowest price reached during the session before the time of the news
DataField: nws12_afterhsz_41rta
DataFieldDescription: 14-day Average True Range
DataField: news_mins_3_chg
DataFieldDescription: The minimum of L or S above for 3-minute bucket
DataField: nws12_prez_57p
DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
DataField: nws12_afterhsz_close_vol
DataFieldDescription: Main close volume
DataField: nws12_mainz_tonlast
DataFieldDescription: Price at the time of news
DataField: nws12_afterhsz_02p
DataFieldDescription: The minimum of L or S above for 20-minute bucket
DataField: nws12_mainz_eodvwap
DataFieldDescription: Volume weighted average price between the time of news and the end of the session
DataField: news_pct_10min
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
DataField: nws12_mainz_sl
DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS = 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
DataField: nws12_afterhsz_mov_vol
DataFieldDescription: 30-day moving average session volume
DataField: news_atr_ratio
DataFieldDescription: Ratio of today's range to 20-day average true range
DataField: nws12_mainz_1l
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_mainz_close_vol
DataFieldDescription: Main close volume
DataField: nws12_mainz_prev_vol
DataFieldDescription: Previous day's session volume
DataField: news_mins_5_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 5 percentage points
DataField: news_mins_2_chg
DataFieldDescription: The minimum of L or S above for 2-minute bucket
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: nws18_bee
DataFieldDescription: News sentiment specializing in growth of earnings
DataField: rp_ess_price
DataFieldDescription: Event sentiment score of stock price news
DataField: rp_nip_ptg
DataFieldDescription: News impact projection of price target news
DataField: rp_ess_earnings
DataFieldDescription: Event sentiment score of earnings news
DataField: rp_nip_business
DataFieldDescription: News impact projection of business-related news
DataField: rp_ess_technical
DataFieldDescription: Event sentiment score based on technical analysis
DataField: rp_nip_insider
DataFieldDescription: News impact projection of insider trading news
DataField: rp_ess_equity
DataFieldDescription: Event sentiment score of equity action news
DataField: rp_css_society
DataFieldDescription: Composite sentiment score of society-related news
DataField: rp_css_marketing
DataFieldDescription: Composite sentiment score of marketing news
DataField: nws18_qmb
DataFieldDescription: News sentiment specializing in editorials on global markets
DataField: nws18_ssc
DataFieldDescription: Sentiment of the news calculated using multiple techniques
DataField: nws18_sse
DataFieldDescription: Sentiment of phrases impacting the company
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: rp_nip_marketing
DataFieldDescription: News impact projection of marketing news
DataField: rp_nip_revenue
DataFieldDescription: News impact projection of revenue news
DataField: nws18_nip
DataFieldDescription: Degree of impact of the news
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: rp_css_labor
DataFieldDescription: Composite sentiment score of labor issues news
DataField: nws18_ber
DataFieldDescription: News sentiment specializing in earnings result
DataField: nws18_qcm
DataFieldDescription: News sentiment of relevant news with high confidence
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: rp_css_mna
DataFieldDescription: Composite sentiment score of mergers and acquisitions-related 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_nip_ratings
DataFieldDescription: News impact projection of analyst ratings-related news
DataField: rp_ess_society
DataFieldDescription: Event sentiment score of society-related news
DataField: rp_css_inverstor
DataFieldDescription: Composite sentiment score of investor relations news
DataField: rp_nip_dividends
DataFieldDescription: News impact projection of dividends news
DataField: rp_ess_revenue
DataFieldDescription: Event sentiment score of revenue news
DataField: fn_accum_oth_income_loss_fx_adj_net_of_tax_a
DataFieldDescription: Accumulated adjustment, net of tax, that results from the process of translating subsidiary financial statements and foreign equity investments into the reporting currency from the functional currency of the reporting entity, net of reclassification of realized foreign currency translation gains or losses.
DataField: fn_intangible_assets_accum_amort_q
DataFieldDescription: Accumulated amount of amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
DataField: fnd2_a_fedstyitxrt
DataFieldDescription: Effective Income Tax Rate Reconciliation - Federal Statutory Income Tax Rate %
DataField: fnd2_unrgtxbnfinregfcrps
DataFieldDescription: Amount of increase in unrecognized tax benefits resulting from tax positions that have been or will be taken in current period tax return.
DataField: fn_finite_lived_intangible_assets_gross_a
DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
DataField: fnd2_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: fnd2_dbplanbnfpaid_ast
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_q_atdlsecexfcepsastkos
DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
DataField: fn_comp_not_rec_a
DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
DataField: fnd2_a_unrgtxbnfitxpenlintacd
DataFieldDescription: Amount accrued for interest on an underpayment of income taxes and penalties related to a tax position claimed or expected to be claimed in the tax return.
DataField: fn_prepaid_expense_a
DataFieldDescription: Carrying amount for an unclassified balance sheet date of expenditures made in advance of when the economic benefit of the cost will be realized, and which will be expensed in future periods with the passage of time or when a triggering event occurs. For a classified balance sheet, represents the noncurrent portion of prepaid expenses (the current portion has a separate concept).
DataField: fn_excess_tax_benefit_from_share_based_comp_fin_activities_q
DataFieldDescription: Amount of cash inflow from realized tax benefit related to deductible compensation cost reported on the entity's tax return for equity instruments in excess of the compensation cost for those instruments recognized for financial reporting purposes.
DataField: fnd2_a_sbcpnargmpmtwgtm
DataFieldDescription: The weighted average period between the balance sheet date and expiration for all awards outstanding under the plan, which may be expressed in a decimal value for number of years.
DataField: fn_oth_comp_grants_weighted_avg_grant_date_fair_value_a
DataFieldDescription: Annual Share-Based Compensation Equity Instruments Other Than Options Nonvested Weighted Average Grant Date Fair Value
DataField: fnd2_a_stkdrgprdvalnewissues
DataFieldDescription: Equity impact of the value of new stock issued during the period. Includes shares issued in an initial public offering or a secondary public offering.
DataField: fnd2_a_ltrmdmrepoplinyfour
DataFieldDescription: Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing in the 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_treasury_stock_shares_a
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: fn_debt_instrument_interest_rate_stated_percentage_a
DataFieldDescription: Stated percentage of interest rate on debt
DataField: fn_comp_options_grants_weighted_avg_q
DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options that were terminated.
DataField: fnd2_a_opclpsnprtmbnfplansajnt
DataFieldDescription: Amount after tax and reclassification adjustments, of (increase) decrease in accumulated other comprehensive (income) loss related to pension and other postretirement defined benefit plans.
DataField: fn_oth_income_loss_derivatives_qualifying_as_hedges_of_tax_q
DataFieldDescription: Amount after tax and reclassification adjustments, of increase (decrease) in accumulated gain (loss) from derivative instruments designated and qualifying as the effective portion of cash flow hedges and an entity's share of an equity investee's increase (decrease) in deferred hedging gain (loss).
DataField: fn_mne_a
DataFieldDescription: Amount before accumulated depreciation of tangible personal property used to produce goods and services, including, but is not limited to, tools, dies and molds, computer and office equipment.
DataField: fnd2_a_sbcpnargmpmwggil
DataFieldDescription: Amount by which the current fair value of the underlying stock exceeds the exercise price of fully vested and expected to vest options outstanding.
DataField: fn_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: fn_comp_not_rec_stock_options_q
DataFieldDescription: Unrecognized cost of unvested stock option awards.
DataField: fn_new_shares_options_a
DataFieldDescription: Number of share options (or share units) exercised during the current period.
DataField: fnd2_a_eplsbvdcpcstnrgprg
DataFieldDescription: The weighted average period over which unrecognized compensation is expected to be recognized for equity-based compensation plans, using a decimal to express in number of years.
DataField: fn_comp_not_rec_q
DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
DataField: fnd2_propplteqmuflmeqmt
DataFieldDescription: PPE, Equipment, Useful Life, Minimum
DataField: fnd2_itxreclstatelocalitxes
DataFieldDescription: Amount 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 attributable to state and local income tax expense (benefit).
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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