任务指令
一、核心设计理念
你是一名WorldQuant WebSim因子工程师，需要设计用于行业轮动策略的复合型Alpha因子。所有因子必须基于以下三个创新视角构建，每个视角提供独特的研究框架：
视角一：市场摩擦的横截面测绘 (Cross-sectional Imaging of Market Frictions)
核心思想：市场摩擦（流动性差异、交易冲击、价格发现延迟）不是需要消除的噪音，而是Alpha的直接来源。主动测绘不同股票对相同指令流冲击的差异化反应模式。
关键研究维度：
    指令流冲击的"消化速率"图谱：测量单位异常交易量引发的价格冲击及其衰减速度。构建"冲击-衰减"二维坐标系，识别高摩擦（冲击大、衰减慢）与低摩擦（冲击小、衰减快）的股票集群。
    买卖失衡的"路径依赖"模式：分析订单流净额的时间序列特性（均值回归vs趋势持续），量化不同市场状态下订单流的自强化或自纠正机制。
    价格发现的"领地性"划分：分解价格变动的驱动来源（自身交易驱动vs行业/指数驱动），计算"价格发现自主权"指标，研究内生性与外生性股票在不同市场环境中的轮动规律。
视角二：投资者注意力的生态学系统 (Ecology of Investor Attention)
核心思想：金融市场是注意力资源的分配系统而非信息聚合器。Alpha来源于对注意力"聚集-分散-转移"动态的精准捕捉。
关键研究维度：
    注意力分布的"聚焦度"谱系：量化交易量/活跃度在时间维度上的集中程度（基尼系数、赫芬达尔指数），识别注意力爆发期、持续关注期和注意力真空期。
    行业内注意力的"级联传导"网络：建立领导者-追随者注意力传导模型，测量强势股票出现后，同行业其他股票的响应速度、响应强度和响应延迟。
    注意力惯性的"衰减曲线"：度量催化事件结束后，异常关注度回归基线的速度，构建"注意力记忆时长"因子，捕捉定价偏差的持续性。
视角三：价格运动的"形态语法"解析 (Morphological Syntax of Price Movements)
核心思想：价格运动具有类似语言的"语法结构"和"叙事连贯性"。市场参与者潜意识地识别并交易这些形态模式，为系统性形态识别提供Alpha机会。
关键研究维度：
    价格序列的"可压缩性"度量：使用简化算法（分段线性近似、趋势线拟合残差）量化价格运动的规律性程度，识别从混沌转向有序（或相反）的临界状态。
    关键价位的"叙事逻辑"强度：分析价格在历史关键节点（前高、前低、缺口、密集区）的行为一致性，量化"支撑阻力叙事"的连贯性得分。
    多时间尺度的"相位同步"分析：研究不同周期滤波序列（如5日、20日、60日均线）之间的领先滞后关系和同步程度，识别多周期共振的形成与瓦解过程。
二、因子构建方法论
2.1 数据字段使用规范
可用字段：
    close: 收盘价（唯一价格字段）
    volume: 成交量（用于规模代理、活跃度度量）
    returns: 收益率序列，定义为 ts_delta(close, 1) 或 divide(close, ts_delay(close, 1)) - 1
禁止字段：
    ❌ market_cap, marketcap, mkt_cap（不存在）
    ✅ 使用volume作为规模代理，必要时进行横截面排序和分组
2.2 复合因子构建框架
维度融合模板（至少选择2个维度组合）：
A. 领导力动量 = 时序动量 × 横截面领导力调整
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逻辑：大成交量股票的动量信号更强、更持续
结构示例：group_mean(ts_delta(close, 20), 1, bucket(rank(volume), range="0,3,0.4"))
经济解释：测量不同成交量分组内价格变化的均值，捕捉大成交量群体的主导方向
B. 状态自适应动量 = 市场状态 × 动量周期选择
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逻辑：高波动环境使用短期动量，低波动环境使用长期动量
结构示例：if_else(ts_std_dev(returns, 20) > 0.02, ts_delta(close, 5), ts_delta(close, 20))
经济解释：根据波动率状态动态调整动量计算窗口，适应不同市场环境
C. 行业传导因子 = 行业间相关性 × 领先滞后关系
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逻辑：与强势行业保持高相关性且略有滞后的行业可能迎来轮动机会
结构示例：multiply(ts_corr(group_mean(returns, 1, industry_A), group_mean(returns, 1, industry_B), 30), ts_delta(close, 10))
经济解释：测量行业间联动强度与自身动量的协同效应
D. 情绪反转因子 = 过度交易信号 × 趋势强度
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逻辑：在过度交易区域，强势趋势可能面临反转；在交易清淡区域，趋势可能延续
结构示例：multiply(reverse(ts_rank(divide(volume, ts_mean(volume, 20)), 10)), ts_delta(close, 20))
经济解释：交易活跃度异常高时反转动量信号，异常低时增强动量信号
2.3 关键操作符使用规范
1. ts_regression使用规范：
    ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
    ❌ 错误：避免深度嵌套，如ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
    ✅ 替代方案：先计算回归斜率，再对其应用ts_delta
2. if_else条件表达式规范：
    ✅ 正确：if_else(ts_rank(ts_std_dev(returns, 60), 120) > 0.7, 短期动量, 长期动量)
    ❌ 错误：避免复杂序列比较，如ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 120)
3. bucket分组函数规范：
    ✅ 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"
4. 行业处理函数：
    group_mean(x, weight, group): 计算组内加权平均
    group_neutralize(x, group): 对组内进行中性化处理
    group_rank(x, group): 计算组内排序
    group_scale(x, group): 组内标准化到[0,1]
    group_zscore(x, group): 计算组内z-score
2.4 参数选择逻辑
    回顾期d应从以下具有市场意义的数值中选择：[5, 10, 20, 30, 60, 120]
        5: 周度（5个交易日）
        10: 双周
        20: 月度（约20个交易日）
        30: 月半
        60: 季度
        120: 半年
    阈值参数从[0.5, 0.7, 0.8]中选择
    同一因子内不同组件的参数应差异化，体现多时间尺度融合
三、因子组件库（可自由组合）
3.1 动量类组件
    简单动量：ts_delta(close, {d})
    回归动量：ts_regression(close, ts_step(1), {d}, 0, 1)（返回斜率）
    加速动量：ts_delta(ts_delta(close, 5), 5)
    排名动量：ts_rank(ts_delta(close, 20), 60)
3.2 波动性与风险调整组件
    波动率：ts_std_dev(returns, {d})
    平均绝对收益：ts_mean(abs(returns), {d})
    波动率调整：divide(ts_delta(close, 20), ts_std_dev(returns, 20))
    波动率状态：ts_rank(ts_std_dev(returns, 20), 60)
3.3 成交量与活跃度组件
    成交量异常：divide(volume, ts_mean(volume, {d}))
    成交量z-score：ts_zscore(volume, {d})
    成交量排名：rank(volume)
    成交量分布：bucket(rank(volume), range="0,3,0.4")
3.4 横截面调整组件
    规模分组：if_else(rank(volume) > 0.7, 大市值组信号, 小市值组信号)
    相对强弱：divide(ts_delta(close, 10), group_mean(ts_delta(close, 10), 1, industry))
    行业中性化：group_neutralize(原始信号, industry)
3.5 相关性与时序关系组件
    时间序列相关性：ts_corr({x}, {y}, {d})
    协方差：ts_covariance({y}, {x}, {d})
    领先滞后关系：ts_corr(ts_delay(x, 1), y, d)
四、因子构建原则
4.1 复杂度控制原则
    嵌套层数建议不超过3层
    每个表达式应有清晰的经济逻辑解释
    避免过度优化和数据挖掘偏差
4.2 交易可行性原则
    严格避免未来函数（只能使用历史信息）
    考虑实际交易成本（避免高换手率因子）
    使用hump(x, hump=0.01)平滑信号变化，降低换手
4.3 风险控制原则
    包含波动率调整元素
    考虑极端值处理（使用winsorize(x, std=4)）
    进行适当的标准化（normalize()或zscore()）
4.4 行业轮动特异性
    必须包含行业维度处理（group_*函数）
    体现行业间传导、轮动、分化逻辑
    考虑行业相对强弱与绝对动量的结合
五、表达式构建示例框架
示例1：行业注意力传导因子
text
经济逻辑：捕捉强势行业对弱势行业的注意力传导效应，测量追随行业对领导行业信号的响应速度和强度。
组件分解：
1. 识别领导行业：过去5日行业动量排名前30%
2. 测量响应强度：自身收益率与领导行业收益率的滞后相关性
3. 调整响应延迟：根据成交量调整，大成交量股票响应更快
4. 行业相对位置：在自身行业内的动量排名
示例2：摩擦差异化的动量因子
text
经济逻辑：在高摩擦（低流动性）股票中寻找未被充分消化的动量，在低摩擦股票中寻找快速衰减的反转机会。
组件分解：
1. 摩擦测量：成交量冲击的价格影响半衰期
2. 动量计算：不同摩擦环境下的最优动量窗口
3. 横截面调整：同摩擦水平股票间的相对强弱
4. 行业中性化：控制行业风格暴露
示例3：多周期形态共振因子
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经济逻辑：识别短期、中期、长期价格趋势进入同步状态（共振）的股票，这些股票往往有更强的趋势持续性。
组件分解：
1. 多周期滤波：5日、20日、60日价格序列
2. 相位同步测量：不同周期序列方向一致性的时间比例
3. 共振强度：同步期的动量加速度
4. 行业调整：与行业共振状态的相对差异
*=====*
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_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_360
DataFieldDescription: Price at which a stock's call options with expiration 360 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_360
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 360 days in the future.
DataField: pcr_oi_90
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
DataField: pcr_vol_270
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 270 days in the future.
DataField: pcr_vol_all
DataFieldDescription: Ratio of put volume to call volume for all maturities on stock's options.
DataField: option_breakeven_30
DataFieldDescription: Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_60
DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: 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: forward_price_30
DataFieldDescription: Forward price at 30 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: pcr_vol_30
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.
DataField: option_breakeven_10
DataFieldDescription: Price at which a stock's options with expiration 10 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_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: pcr_oi_30
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 30 days in the future.
DataField: option_breakeven_120
DataFieldDescription: Price at which a stock's options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_180
DataFieldDescription: Price at which a stock's options with expiration 180 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_360
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 360 days in the future.
DataField: option_breakeven_150
DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
DataField: 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: 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: put_breakeven_30
DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
DataField: 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: 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: 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: pcr_oi_all
DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
DataField: pcr_oi_270
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 270 days in the future.
DataField: forward_price_720
DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: option_breakeven_270
DataFieldDescription: Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.
DataField: forward_price_1080
DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: pcr_oi_150
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.
DataField: fnd6_oelim
DataFieldDescription: Other Eliminations (Income)
DataField: fnd6_txws
DataFieldDescription: Excise Taxes
DataField: fnd6_newqeventv110_lol2q
DataFieldDescription: Liabilities Level 2 (Observable)
DataField: fnd6_dpvieb
DataFieldDescription: Depreciation (Accumulated) - Ending Balance (Schedule VI)
DataField: income_beforeextra
DataFieldDescription: Income Before Extraordinary Items
DataField: fnd6_newqeventv110_ancq
DataFieldDescription: Non-Current Assets - Total
DataField: fnd6_optgr
DataFieldDescription: Options - Granted
DataField: fnd6_optvol
DataFieldDescription: Volatility - Assumption (%)
DataField: fnd6_newa2v1300_xint
DataFieldDescription: Interest and Related Expense - Total
DataField: fnd6_xacc
DataFieldDescription: Accrued Expenses
DataField: fnd6_newqeventv110_rcpq
DataFieldDescription: Restructuring Cost Pretax
DataField: fnd6_newqeventv110_txdbq
DataFieldDescription: Deferred Taxes - Balance Sheet
DataField: sga_expense
DataFieldDescription: Selling, General and Administrative Expenses
DataField: assets
DataFieldDescription: Assets - Total
DataField: fnd6_newqeventv110_ltmibq
DataFieldDescription: Liabilities - Total and Noncontrolling Interest
DataField: fnd6_newqv1300_glceeps12
DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Basic EPS Effect 12MM
DataField: fnd6_aqi
DataFieldDescription: Acquisitions - Income Contribution
DataField: fnd6_newqv1300_acoq
DataFieldDescription: Current Assets - Other - Total
DataField: fnd6_newqeventv110_pnciepsq
DataFieldDescription: Core Pension Interest Adjustment Basic EPS Effect
DataField: fnd6_newqv1300_ppegtq
DataFieldDescription: Property, Plant and Equipment - Total (Gross) - Quarterly
DataField: fnd6_newqv1300_drcq
DataFieldDescription: Deferred Revenue - Current
DataField: fnd6_mfmq_mibtq
DataFieldDescription: Noncontrolling Interests - Total - Balance Sheet - Quarterly
DataField: fnd6_cptnewqv1300_nopiq
DataFieldDescription: Non-Operating Income (Expense) - Total
DataField: fnd6_newqv1300_pncq
DataFieldDescription: Core Pension Adjustment
DataField: fnd6_newa1v1300_ebit
DataFieldDescription: Earnings Before Interest and Taxes
DataField: fnd6_newqv1300_epspiq
DataFieldDescription: Earnings Per Share (Basic) - Including Extraordinary Items
DataField: fnd6_dcvsub
DataFieldDescription: Debt - Subordinated Convertible
DataField: fnd6_cptnewqeventv110_epsx12
DataFieldDescription: Earnings Per Share (Basic) - Excluding Extraordinary Items - 12 Months Moving
DataField: fnd6_newa1v1300_dv
DataFieldDescription: Cash Dividends (Cash Flow)
DataField: fnd6_newqeventv110_teqq
DataFieldDescription: Stockholders' Equity - Total - 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_qfd1_az_hgih_spfc
DataFieldDescription: Cash Flow - The highest estimation, per share
DataField: anl4_eaz1laf_prevval
DataFieldDescription: The previous estimation of financial item
DataField: max_free_cashflow_per_share_guidance
DataFieldDescription: The maximum guidance value for free cash flow per share.
DataField: anl4_cuo1guidaf_maxguidance
DataFieldDescription: Max guidance value
DataField: pretax_income_reported_min_guidance_qtr
DataFieldDescription: Reported Pretax income- minimum guidance value
DataField: cashflow_per_share_estimate_count
DataFieldDescription: Cash Flow Per Share - number of estimations - delay1
DataField: anl4_fcfps_number
DataFieldDescription: Free Cash Flow per Share - number of estimations
DataField: min_pretax_profit_guidance
DataFieldDescription: Minimum guidance value for Pretax income
DataField: anl4_fsdtlestmtqfv4_item
DataFieldDescription: Financial item
DataField: capital_expenditure_amount
DataFieldDescription: Capital Expenditures - Total value
DataField: anl4_fsactualafv4_actual
DataFieldDescription: Announced financial data
DataField: anl4_adxqfv110_high
DataFieldDescription: The highest estimation
DataField: anl4_af_cfps_value
DataFieldDescription: Cash Flow Per Share - Actual Value
DataField: estimate_value_currency_code_detail_qtr
DataFieldDescription: Home currency of instrument
DataField: anl4_qf_az_eps
DataFieldDescription: EPS - aggregation on estimations, 50th percentile
DataField: net_debt_amount
DataFieldDescription: Net debt - actual value for the annual period
DataField: anl4_totassets_mean
DataFieldDescription: Total Assets - mean of estimations
DataField: anl4_detailrecv4v104_est
DataFieldDescription: Estimation value
DataField: anl4_capex_std
DataFieldDescription: Capital Expenditures - standard deviation of estimations
DataField: anl4_qf_az_wol_vid
DataFieldDescription: Dividend per share - The lowest value among forecasts
DataField: anl4_ebitda_high
DataFieldDescription: Earnings before interest, taxes, depreciation, and amortization - the highest estimation
DataField: min_operating_cashflow_guidance
DataFieldDescription: Minimum guidance value for Cash Flow from Operations
DataField: min_sg_and_a_expense_guidance
DataFieldDescription: Selling, General & Administrative Expense - Minimum guidance value
DataField: estimate_value_currency_code
DataFieldDescription: Home currency of instrument
DataField: max_custom_eps_guidance
DataFieldDescription: Custom Earnings per share - The highest guidance value
DataField: max_net_profit_guidance
DataFieldDescription: The maximum guidance value for net profit on an annual basis.
DataField: maximum_guidance_value
DataFieldDescription: Maximum guidance value for basic annual financials
DataField: min_total_goodwill_guidance
DataFieldDescription: Total Goodwill - The lowest guidance value
DataField: anl4_bvps_median
DataFieldDescription: Book value per share - Median value among forecasts
DataField: anl4_epsr_low
DataFieldDescription: GAAP Earnings per share - The lowest estimation
DataField: pv13_hierarchy_min5_3000_513_sector
DataFieldDescription: grouping fields
DataField: primary_sector_focused_company_count
DataFieldDescription: Number of companies primarily focused in a given sector.
DataField: pv13_1l_scibr
DataFieldDescription: grouping fields
DataField: pv13_percentregion
DataFieldDescription: Exposure percentage
DataField: pv13_new_2l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_sector_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_com_rk_au
DataFieldDescription: the HITS authority score of competitors
DataField: pv13_r2_liquid_min2_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f4_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min2_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min25_513_sector
DataFieldDescription: grouping fields
DataField: pv13_4l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_top3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f4_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min100_2000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_liquid_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min22_1000_sector
DataFieldDescription: Grouping fields for top 1000
DataField: pv13_hierarchy_min22_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_page_rank
DataFieldDescription: the PageRank of customers
DataField: pv13_new_4l_scibr
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_sector
DataFieldDescription: grouping fields
DataField: pv13_di_6l
DataFieldDescription: grouping fields
DataField: rel_num_part
DataFieldDescription: number of the instrument's partners
DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min40_3000_513_sector
DataFieldDescription: grouping fields
DataField: implied_volatility_mean_10
DataFieldDescription: At-the-money option-implied volatility mean for 10 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_put_720
DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
DataField: implied_volatility_mean_skew_270
DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
DataField: implied_volatility_mean_180
DataFieldDescription: At-the-money option-implied volatility mean for 180 days
DataField: historical_volatility_20
DataFieldDescription: Close-to-close Historical volatility over 20 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: implied_volatility_call_20
DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
DataField: parkinson_volatility_90
DataFieldDescription: Parkinson model's historical volatility over 90 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_20
DataFieldDescription: At-the-money option-implied volatility mean for 20 days
DataField: implied_volatility_call_120
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
DataField: implied_volatility_call_1080
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
DataField: implied_volatility_mean_skew_60
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_put_180
DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
DataField: implied_volatility_mean_skew_20
DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: implied_volatility_put_10
DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
DataField: implied_volatility_mean_skew_10
DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
DataField: historical_volatility_30
DataFieldDescription: Close-to-close Historical volatility over 30 days
DataField: implied_volatility_call_60
DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
DataField: implied_volatility_mean_120
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: historical_volatility_180
DataFieldDescription: Close-to-close Historical volatility over 180 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: nws12_mainz_3l
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
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_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_prez_prevwap
DataFieldDescription: Pre-session volume weighted average price
DataField: news_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: nws12_prez_90_min
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
DataField: nws12_prez_30_min
DataFieldDescription: The percent change in price in the first 30 minutes following the news release
DataField: news_session_range
DataFieldDescription: Session High Price - Session Low Price
DataField: nws12_prez_peratio
DataFieldDescription: Reported price to earnings ratio for the calendar day of the session
DataField: nws12_afterhsz_mktcap
DataFieldDescription: Reported market capitalization for the calendar day of the session
DataField: news_ls
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_mainz_prevwap
DataFieldDescription: Pre session volume weighted average price
DataField: news_main_vwap
DataFieldDescription: Main session volume weighted average price
DataField: news_eod_close
DataFieldDescription: Close price of the session
DataField: nws12_afterhsz_30_min
DataFieldDescription: The percent change in price in the first 30 minutes following the news release
DataField: nws12_prez_tonhigh
DataFieldDescription: Highest price reached during the session before the time of the news
DataField: nws12_mainz_close_vol
DataFieldDescription: Main close volume
DataField: nws12_mainz_01l
DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
DataField: nws12_mainz_3s
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
DataField: nws12_mainz_10_min
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
DataField: nws12_prez_postvwap
DataFieldDescription: Post-session volume-weighted average price
DataField: nws12_mainz_90_min
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
DataField: news_session_range_pct
DataFieldDescription: (Session High Price - Session Low Price) / Session Low Price.
DataField: nws12_mainz_60_min
DataFieldDescription: The percent change in price in the first 60 minutes following the news release
DataField: nws12_mainz_volstddev
DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
DataField: news_pct_120min
DataFieldDescription: The percent change in price in the first 120 minutes following the news release
DataField: nws12_mainz_eodlow
DataFieldDescription: Lowest price reached between the time of news and the end of the session.
DataField: nws12_allz_provider
DataFieldDescription: index of name of the news provider
DataField: news_vol_stddev
DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
DataField: news_cap
DataFieldDescription: Reported market capitalization for the calendar day of the session
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: nws18_event_relevance
DataFieldDescription: Relevance of the event to the story
DataField: rp_css_product
DataFieldDescription: Composite sentiment score of product and service-related news
DataField: rp_ess_ptg
DataFieldDescription: Event sentiment score of price target news
DataField: rp_css_partner
DataFieldDescription: Composite sentiment score of partnership news
DataField: rp_ess_dividends
DataFieldDescription: Event sentiment score of dividends news
DataField: rp_ess_insider
DataFieldDescription: Event sentiment score of insider trading news
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: nws18_nip
DataFieldDescription: Degree of impact of the news
DataField: rp_ess_credit_ratings
DataFieldDescription: Event sentiment score of credit ratings news
DataField: rp_css_credit
DataFieldDescription: Composite sentiment score of credit news
DataField: rp_ess_earnings
DataFieldDescription: Event sentiment score of earnings news
DataField: rp_ess_revenue
DataFieldDescription: Event sentiment score of revenue news
DataField: rp_nip_technical
DataFieldDescription: News impact projection based on technical analysis
DataField: rp_ess_business
DataFieldDescription: Event sentiment score of business-related news
DataField: rp_nip_inverstor
DataFieldDescription: News impact projection of investor relations news
DataField: rp_css_price
DataFieldDescription: Composite sentiment score of stock price news
DataField: rp_nip_society
DataFieldDescription: News impact projection of society-related news
DataField: nws18_event_similarity_days
DataFieldDescription: Days since a similar event was detected
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: nws18_acb
DataFieldDescription: News sentiment specializing in corporate action announcements
DataField: rp_css_marketing
DataFieldDescription: Composite sentiment score of marketing news
DataField: rp_css_ratings
DataFieldDescription: Composite sentiment score of analyst ratings-related news
DataField: rp_css_inverstor
DataFieldDescription: Composite sentiment score of investor relations news
DataField: rp_css_revenue
DataFieldDescription: Composite sentiment score of revenue news
DataField: rp_nip_marketing
DataFieldDescription: News impact projection of marketing news
DataField: rp_ess_mna
DataFieldDescription: Event sentiment score of mergers and acquisitions-related news
DataField: nws18_bam
DataFieldDescription: News sentiment specializing in mergers and acquisitions
DataField: rp_nip_credit_ratings
DataFieldDescription: News impact projection of credit ratings news
DataField: rp_ess_technical
DataFieldDescription: Event sentiment score based on technical analysis
DataField: nws18_ssc
DataFieldDescription: Sentiment of the news calculated using multiple techniques
DataField: fnd2_dfdtxastxdfdexprssaccrs
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible temporary differences from reserves and accruals.
DataField: fn_proceeds_from_issuance_of_debt_q
DataFieldDescription: The cash inflow during the period from additional borrowings in aggregate debt. Includes proceeds from short-term and long-term debt.
DataField: fn_repurchased_shares_q
DataFieldDescription: Number of shares that have been repurchased during the period.
DataField: fn_liab_fair_val_l3_q
DataFieldDescription: Liabilities Fair Value, Recurring, Level 3
DataField: fnd2_unrgtxbnfdcfpprdtxpss
DataFieldDescription: Amount of decrease in unrecognized tax benefits resulting from tax positions that have been or will be taken in current period tax return.
DataField: fn_comp_non_opt_forfeited_q
DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that were forfeited during the reporting period.
DataField: fn_accrued_liab_q
DataFieldDescription: Carrying value as of the balance sheet date of obligations incurred and payable, pertaining to costs that are statutory in nature, are incurred on contractual obligations, or accumulate over time and for which invoices have not yet been received or will not be rendered.
DataField: fnd2_oprlsfmpdcurr
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 next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fnd2_dbplanartonplas
DataFieldDescription: Defined Benefit Plan, Benefits Paid, Plan Assets
DataField: fn_amortization_of_intangible_assets_a
DataFieldDescription: The aggregate expense charged against earnings to allocate the cost of intangible assets (nonphysical assets not used in production) in a systematic and rational manner to the periods expected to benefit from such assets. As a noncash expense, this element is added back to net income when calculating cash provided by or used in operations using the indirect method.
DataField: fnd2_a_restructuringcharges
DataFieldDescription: Amount of expenses associated with exit or disposal activities pursuant to an authorized plan. Excludes expenses related to a discontinued operation or an asset retirement obligation.
DataField: fnd2_dbplanfvalpnas
DataFieldDescription: Fair value of assets that have been segregated and restricted to provide pension or postretirement benefits. Assets include, but are not limited to, stocks, bonds, other investments, earnings from investments, and contributions by the employer and employees.
DataField: fn_new_shares_issued_a
DataFieldDescription: Number of new stock issued during the period.
DataField: fnd2_dfdlocalitxexp
DataFieldDescription: Income Tax Expense, Deferred
DataField: fnd2_dbplanamtsrginblsh
DataFieldDescription: The aggregate net amount recognized in the balance sheet associated with the defined benefit plan(s). Will normally be the same as the Defined Benefit Plan, Funded Status of Plan, Total.
DataField: fn_effect_of_exchange_rate_on_cash_and_equiv_q
DataFieldDescription: Amount of increase (decrease) from the effect of exchange rate changes on cash and cash equivalent balances held in foreign currencies.
DataField: fnd2_dbplanepdfbnfpyfour
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid 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_payments_to_acquire_businesses_net_of_cash_acquired_a
DataFieldDescription: The cash outflow associated with the acquisition of a business, net of the cash acquired from the purchase.
DataField: fnd2_sbcpnshardpreops
DataFieldDescription: Share-based compensation shares authorized under stock option plans exercise price range number of exercisable options
DataField: fnd2_a_inventoryrawmaterials
DataFieldDescription: Amount before valuation and LIFO reserves of raw materials expected to be sold, or consumed within 1 year or operating cycle, if longer.
DataField: fnd2_propplteqflublgland
DataFieldDescription: PPE, Buildings & Land, Useful Life, Maximum
DataField: fn_interest_payable_a
DataFieldDescription: Carrying value as of the balance sheet date of [accrued] interest payable on all forms of debt, including trade payables, that has been incurred and is unpaid. For classified balance sheets, used to reflect the current portion of the liabilities (due within 1 year or within the normal operating cycle if longer); for unclassified balance sheets, used to reflect the total liabilities (regardless of due date).
DataField: fnd2_q_bnsacqproformarvn
DataFieldDescription: The pro forma revenue for a period as if the business combination or combinations had been completed at the beginning of the period.
DataField: fnd2_itxreclchgdfdtxava
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 increase (decrease) in the valuation allowance for deferred tax assets.
DataField: fn_op_lease_min_pay_due_in_5y_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 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_incremental_shares_attributable_to_share_based_payment_a
DataFieldDescription: Additional shares included in the calculation of diluted EPS as a result of the potentially dilutive effect of share-based payment arrangements using the treasury stock method.
DataField: 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: fn_oth_income_loss_net_of_tax_q
DataFieldDescription: Amount after tax and reclassification adjustments of other comprehensive income (loss).
DataField: fnd2_currfedtxexp
DataFieldDescription: Income Tax Expense, Current - Federal
DataField: fn_business_combination_assets_aquired_goodwill_a
DataFieldDescription: Business Combination, Portion of Purchase Price Allocated to Goodwill
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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