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_20
DataFieldDescription: Price at which a stock's options with expiration 20 days in the future break even based on its recent bid/ask mean.
DataField: 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_10
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
DataField: call_breakeven_120
DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_150
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
DataField: option_breakeven_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: 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: 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_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_vol_270
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 270 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_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: pcr_vol_1080
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.
DataField: forward_price_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: call_breakeven_20
DataFieldDescription: Price at which a stock's call options with expiration 20 days in the future break even based on its recent bid/ask mean.
DataField: call_breakeven_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: 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: 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: pcr_vol_720
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 720 days in the future.
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: put_breakeven_20
DataFieldDescription: Price at which a stock's put options with expiration 20 days in the future break even based on its recent bid/ask mean.
DataField: call_breakeven_180
DataFieldDescription: Price at which a stock's call options with expiration 180 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_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: option_breakeven_90
DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
DataField: 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: pcr_oi_1080
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
DataField: 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_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: 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: fnd6_newqeventv110_ivltq
DataFieldDescription: Total Long-term Investments
DataField: fnd6_newqeventv110_rdipq
DataFieldDescription: In Process R&D
DataField: fnd6_txdfo
DataFieldDescription: Deferred Taxes - Foreign
DataField: fnd6_newqeventv110_prcdq
DataFieldDescription: Core Post-Retirement Adjustment Diluted EPS Effect
DataField: fnd6_cptnewqeventv110_actq
DataFieldDescription: Current Assets - Total
DataField: fnd6_newqeventv110_prce12
DataFieldDescription: Core Post Retirement Adjustment 12MM
DataField: fnd6_pncdq
DataFieldDescription: Core Pension Adjustment Diluted EPS Effect
DataField: fnd6_eventv110_optdrq
DataFieldDescription: Dividend Rate - Assumption (%)
DataField: rd_expense
DataFieldDescription: Research And Development (Quarterly)
DataField: fnd6_dn
DataFieldDescription: Debt - Notes
DataField: fnd6_dm
DataFieldDescription: Debt - Mortgages & Other Secured
DataField: fnd6_xaccq
DataFieldDescription: Accrued Expenses
DataField: fnd6_newa1v1300_gdwl
DataFieldDescription: Goodwill
DataField: fnd6_dudd
DataFieldDescription: Debt - Unamortized Debt Discount and Other
DataField: fnd6_mfma1_dpc
DataFieldDescription: Depreciation and Amortization (Cash Flow)
DataField: fnd6_newqv1300_csh12q
DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - 12 Months Moving
DataField: fnd6_xopr
DataFieldDescription: Operating Expenses - Total
DataField: fnd6_newqeventv110_dpactq
DataFieldDescription: Depreciation, Depletion and Amortization (Accumulated)
DataField: fnd6_cibegni
DataFieldDescription: Comp Inc - Beginning Net Income
DataField: fnd6_dps
DataFieldDescription: Depreciation and Amortization
DataField: fnd6_newqv1300_xrdq
DataFieldDescription: Research and Development Expense
DataField: fnd6_xad
DataFieldDescription: Advertising Expense
DataField: fnd6_cik
DataFieldDescription: nonimportant technical code
DataField: fnd6_newa2v1300_xopteps
DataFieldDescription: Implied Option EPS Basic
DataField: sales_ps
DataFieldDescription: Sales per Share (Quarterly)
DataField: inventory_turnover
DataFieldDescription: Inventory Turnover
DataField: fnd6_pnrsho
DataFieldDescription: Nonred Pfd Shares Outs (000)
DataField: fnd6_newqv1300_ciq
DataFieldDescription: Comprehensive Income - Total
DataField: fnd6_newqv1300_glcea12
DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) After-tax 12MM
DataField: debt
DataFieldDescription: Debt
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_guibasicqfv4_est
DataFieldDescription: Estimation value
DataField: anl4_epsr_mean
DataFieldDescription: GAAP Earnings per share - mean of estimations
DataField: min_reported_eps_guidance
DataFieldDescription: Reported Earnings Per Share - Minimum guidance value for the annual period
DataField: anl4_qfd1_az_div_number
DataFieldDescription: Dividend per share - number of estimations
DataField: anl4_cff_median
DataFieldDescription: Cash Flow From Financing Activities - Median value among forecasts
DataField: est_bookvalue_ps
DataFieldDescription: Book value per share - average of estimations
DataField: anl4_qf_az_eps_number
DataFieldDescription: Earnings per share - number of estimations
DataField: anl4_fsguidanceqfv4_maxguidance
DataFieldDescription: Max guidance value
DataField: max_net_debt_guidance
DataFieldDescription: The maximum guidance value for Net Debt on an annual basis.
DataField: anl4_baz1v110_bk
DataFieldDescription: Broker name (int)
DataField: anl4_totassets_median
DataFieldDescription: Total Assets - median of estimations
DataField: anl4_ads1detailafv110_prevval
DataFieldDescription: The Previous Estimation of Financial Item
DataField: actual_sales_value_quarterly
DataFieldDescription: Sales - Value in financial services income statement (in millions)
DataField: max_total_assets_guidance
DataFieldDescription: The maximum guidance value for Total Assets.
DataField: net_debt_min_guidance_qtr
DataFieldDescription: Minimum guidance value for Net Debt
DataField: earnings_per_share_average
DataFieldDescription: Earnings per share - mean of estimations
DataField: anl4_ptpr_low
DataFieldDescription: Reported Pretax Income - The Lowest Estimation
DataField: earnings_per_share_reported
DataFieldDescription: Reported Earnings Per Share - Actual Value
DataField: anl4_fsdtlestmtbscqv104_item
DataFieldDescription: Financial item
DataField: anl4_qfv4_eps_low
DataFieldDescription: Earnings per share - The lowest estimation
DataField: max_adjusted_funds_from_operations_guidance_2
DataFieldDescription: Adjusted funds from operation - maximum guidance value for the annual period
DataField: anl4_basicconafv110_pu
DataFieldDescription: The number of upper estimations
DataField: sales_estimate_standard_deviation
DataFieldDescription: Sales - standard deviation of estimations
DataField: anl4_ptpr_flag
DataFieldDescription: Reported Pretax income - forecast type (revision/new/...)
DataField: est_tot_goodwill
DataFieldDescription: Total Goodwill - mean of estimations
DataField: anl4_basicdetailqfv110_bk
DataFieldDescription: Broker name (int)
DataField: anl4_ads1detailqfv110_bk
DataFieldDescription: Broker name (int)
DataField: min_pretax_profit_guidance
DataFieldDescription: Minimum guidance value for Pretax income
DataField: anl4_dez1qfv4_preest
DataFieldDescription: The previous estimation of finanicial item
DataField: anl4_basicconqfv110_numest
DataFieldDescription: The number of forecasts counted in aggregation
DataField: pv13_hierarchy_min2_pureplay_only_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min5_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy23_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_513_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_auth_rank
DataFieldDescription: the HITS authority score of customers
DataField: pv13_hierarchy_min5_corr21_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_com_page_rank
DataFieldDescription: the PageRank of competitors
DataField: pv13_4l_scibr
DataFieldDescription: grouping fields
DataField: pv13_reveremap
DataFieldDescription: Mapping data
DataField: pv13_hierarchy_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min2_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_3k_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_hub_rank
DataFieldDescription: the HITS hub score of customers
DataField: pv13_new_3l_scibr
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min50_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_min20_1000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min100_corr21_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min24_500_sector
DataFieldDescription: Grouping fields for top 500
DataField: pv13_reportperiodend
DataFieldDescription: Stated end date for the report
DataField: pv13_hierarchy_min25_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_min20_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_f3g2_sector
DataFieldDescription: grouping fields
DataField: pv13_reporttype
DataFieldDescription: Type of report
DataField: pv13_hierarchy_min5_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f1_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min5_500_sector
DataFieldDescription: Grouping fields
DataField: pv13_hierarchy_min52_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min54_3000_sector
DataFieldDescription: grouping fields
DataField: implied_volatility_mean_120
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: historical_volatility_120
DataFieldDescription: Close-to-close Historical volatility over 120 days
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_mean_skew_360
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
DataField: parkinson_volatility_30
DataFieldDescription: Parkinson model's historical volatility over 30 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: historical_volatility_60
DataFieldDescription: Close-to-close Historical volatility over 60 days
DataField: implied_volatility_mean_90
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
DataField: implied_volatility_mean_skew_180
DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: implied_volatility_mean_270
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
DataField: implied_volatility_put_360
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
DataField: historical_volatility_90
DataFieldDescription: Close-to-close Historical volatility over 90 days
DataField: implied_volatility_call_720
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
DataField: implied_volatility_mean_60
DataFieldDescription: At-the-money option-implied volatility mean for 60 days
DataField: implied_volatility_mean_skew_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
DataField: parkinson_volatility_10
DataFieldDescription: Parkinson model's historical volatility over 2 weeks
DataField: implied_volatility_mean_skew_90
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
DataField: implied_volatility_mean_150
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: implied_volatility_call_360
DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: implied_volatility_call_90
DataFieldDescription: At-the-money option-implied volatility for call Option for 90 days
DataField: parkinson_volatility_90
DataFieldDescription: Parkinson model's historical volatility over 90 days
DataField: implied_volatility_call_120
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
DataField: implied_volatility_mean_skew_20
DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
DataField: implied_volatility_mean_skew_270
DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
DataField: historical_volatility_10
DataFieldDescription: Close-to-close Historical volatility over 10 days
DataField: nws12_prez_2p
DataFieldDescription: The minimum of L or S above for 2-minute bucket
DataField: nws12_prez_01p
DataFieldDescription: The minimum of L or S above for 10-minute bucket
DataField: nws12_afterhsz_spylast
DataFieldDescription: Last Price of the SPY at the time of the news
DataField: nws12_prez_02s
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
DataField: nws12_prez_mov_vol
DataFieldDescription: 30-day moving average session volume
DataField: news_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: nws12_mainz_newrecord
DataFieldDescription: Tracks whether the news is first instance or a duplicate
DataField: news_all_vwap
DataFieldDescription: Volume weighted average price of all sessions
DataField: nws12_afterhsz_90_min
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
DataField: nws12_mainz_result_vs_index
DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
DataField: nws12_mainz_spylast
DataFieldDescription: Last Price of the SPY at the time of the news
DataField: nws12_mainz_tonlast
DataFieldDescription: Price at the time of news
DataField: news_curr_vol
DataFieldDescription: Current day's session volume
DataField: nws12_afterhsz_prevwap
DataFieldDescription: Pre session volume weighted average price
DataField: news_mins_3_chg
DataFieldDescription: The minimum of L or S above for 3-minute bucket
DataField: nws12_afterhsz_reportsess
DataFieldDescription: Index of Session on which the spreadsheet is reporting
DataField: nws12_prez_1p
DataFieldDescription: The minimum of L or S above for 1-minute bucket
DataField: news_mins_10_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
DataField: nws12_afterhsz_eodhigh
DataFieldDescription: Highest price reached between the time of news and the end of the session
DataField: nws12_afterhsz_tonhigh
DataFieldDescription: Highest price reached during the session before the time of news
DataField: nws12_prez_peratio
DataFieldDescription: Reported price to earnings ratio for the calendar day of the session
DataField: nws12_prez_open_vol
DataFieldDescription: Main open volume
DataField: news_eps_actual
DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
DataField: nws12_prez_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_prevclose
DataFieldDescription: Previous trading day's close price
DataField: news_max_up_ret
DataFieldDescription: Percent change from the price at the time of the news to the after the news high
DataField: nws12_prez_allvwap
DataFieldDescription: Volume weighted average price of all sessions
DataField: nws12_prez_prev_vol
DataFieldDescription: Previous day's session volume
DataField: nws12_prez_result1
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
DataField: news_mins_20_chg
DataFieldDescription: The minimum of L or S above for 20-minute bucket
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: rp_css_business
DataFieldDescription: Composite sentiment score of business-related news
DataField: rp_ess_product
DataFieldDescription: Event sentiment score of product and service-related news
DataField: rp_nip_insider
DataFieldDescription: News impact projection of insider trading news
DataField: rp_css_mna
DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
DataField: rp_nip_legal
DataFieldDescription: News impact projection of legal news
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: nws18_qcm
DataFieldDescription: News sentiment of relevant news with high confidence
DataField: rp_css_insider
DataFieldDescription: Composite sentiment score of insider trading news
DataField: rp_ess_insider
DataFieldDescription: Event sentiment score of insider trading news
DataField: rp_ess_revenue
DataFieldDescription: Event sentiment score of revenue news
DataField: rp_ess_price
DataFieldDescription: Event sentiment score of stock price news
DataField: rp_css_price
DataFieldDescription: Composite sentiment score of stock price news
DataField: rp_css_society
DataFieldDescription: Composite sentiment score of society-related news
DataField: rp_css_product
DataFieldDescription: Composite sentiment score of product and service-related news
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: nws18_ber
DataFieldDescription: News sentiment specializing in earnings result
DataField: rp_ess_earnings
DataFieldDescription: Event sentiment score of earnings news
DataField: nws18_bam
DataFieldDescription: News sentiment specializing in mergers and acquisitions
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: rp_nip_inverstor
DataFieldDescription: News impact projection of investor relations news
DataField: rp_nip_revenue
DataFieldDescription: News impact projection of revenue news
DataField: rp_nip_labor
DataFieldDescription: News impact projection of labor issues news
DataField: nws18_sse
DataFieldDescription: Sentiment of phrases impacting the company
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: rp_ess_credit_ratings
DataFieldDescription: Event sentiment score of credit ratings news
DataField: rp_css_legal
DataFieldDescription: Composite sentiment score of legal news
DataField: nws18_relevance
DataFieldDescription: Relevance of news to the company
DataField: nws18_ghc_lna
DataFieldDescription: Change in analyst recommendation
DataField: rp_nip_ratings
DataFieldDescription: News impact projection of analyst ratings-related news
DataField: rp_css_technical
DataFieldDescription: Composite sentiment score based on technical analysis
DataField: fnd2_a_sbcpnargmpmtwvadpgwepr
DataFieldDescription: As of the balance sheet date, the weighted-average exercise price for outstanding stock options that are fully vested or expected to vest.
DataField: fnd2_a_ltrmdmrepopliny5
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 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_ebitdm
DataFieldDescription: EBIT, Domestic
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: fn_finite_lived_intangible_assets_net_q
DataFieldDescription: Finite Lived Intangible Assets, Net
DataField: fnd2_a_curritxexp
DataFieldDescription: Income Tax Expense, Current
DataField: fnd2_q_flintasamt1expy5
DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fnd2_a_sbcpnargmsptawervl
DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
DataField: 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_currstatelocaltxexp
DataFieldDescription: Income Tax Expense, Current - State & Local
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_a_blgandiprtsg
DataFieldDescription: Amount before accumulated depreciation of building structures held for productive use including addition, improvement, or renovation to the structure, including, but not limited to, interior masonry, interior flooring, electrical, and plumbing.
DataField: fn_allowance_for_doubtful_accounts_receivable_q
DataFieldDescription: For an unclassified balance sheet, a valuation allowance for receivables due a company that are expected to be uncollectible.
DataField: fn_assets_fair_val_q
DataFieldDescription: Asset Fair Value, Recurring, Total
DataField: fn_repurchased_shares_value_a
DataFieldDescription: Shares repurchased and either retired or put into treasury stock, likely as part of a share buyback plan.
DataField: fn_assets_fair_val_l1_q
DataFieldDescription: Asset Fair Value, Recurring, Level 1
DataField: fn_op_lease_min_pay_due_in_4y_a
DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fnd2_dfdtxasoprlcarryfwd
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible operating loss carryforwards.
DataField: fn_repayments_of_lines_of_credit_q
DataFieldDescription: Amount of cash outflow for payment of an obligation from a lender, including but not limited to, letter of credit, standby letter of credit and revolving credit arrangements.
DataField: fnd2_a_lineofcrfcyrmbrgcap
DataFieldDescription: Amount of borrowing capacity currently available under the credit facility (current borrowing capacity less the amount of borrowings outstanding).
DataField: fnd2_q_ptoacqbnsesg
DataFieldDescription: The cash outflow associated with the acquisition of business during the period. The cash portion only of the acquisition price.
DataField: fnd2_a_flintasamt1expnext12m
DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_accum_oth_income_loss_fx_adj_net_of_tax_q
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_oth_comp_forfeitures_fair_value_a
DataFieldDescription: Annual Share Based Compensation Equity Instruments Other Than Options Forfeitures Weighted Average Grant Date Fair Value
DataField: fnd2_a_stkrpeprogramardamt
DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
DataField: fn_allocated_share_based_compensation_expense_a
DataFieldDescription: Represents the expense recognized during the period arising from equity-based compensation arrangements (for example, shares of stock, unit, stock options or other equity instruments) with employees, directors and certain consultants qualifying for treatment as employees.
DataField: fn_goodwill_acquired_during_period_a
DataFieldDescription: Amount of increase in asset representing future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized resulting from a business combination.
DataField: fn_liab_fair_val_l1_q
DataFieldDescription: Liabilities Fair Value, Recurring, Level 1
DataField: fnd2_a_dbplctrbyemp
DataFieldDescription: Amount of contributions made by the employer to defined benefit plans.
DataField: fn_interest_paid_net_q
DataFieldDescription: Net interest
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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