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: 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: 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: 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_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_vol_60
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
DataField: pcr_oi_30
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 30 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: put_breakeven_60
DataFieldDescription: Price at which a stock's put options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: 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: 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_150
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 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: call_breakeven_720
DataFieldDescription: Price at which a stock's call options with expiration 720 days in the future break even based on its recent bid/ask mean.
DataField: pcr_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_270
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 270 days in the future.
DataField: call_breakeven_30
DataFieldDescription: Price at which a stock's call options with expiration 30 days in the future break even based on its recent bid/ask mean.
DataField: 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: forward_price_150
DataFieldDescription: Forward price at 150 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: forward_price_360
DataFieldDescription: Forward price at 360 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: 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_vol_20
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
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: 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: 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_60
DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
DataField: pcr_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: option_breakeven_1080
DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
DataField: 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: pcr_vol_150
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
DataField: fnd6_newa2v1300_xoptd
DataFieldDescription: Implied Option EPS Diluted
DataField: assets_curr
DataFieldDescription: Current Assets - Total
DataField: fnd6_newqeventv110_pncwiaq
DataFieldDescription: Core Pension w/o Interest Adjustment After-tax
DataField: fnd6_cptnewqeventv110_epsf12
DataFieldDescription: Earnings Per Share (Diluted) - Excluding Extraordinary Items - 12 Months Moving
DataField: fnd6_spis
DataFieldDescription: Special Items
DataField: cashflow
DataFieldDescription: Cashflow (Annual)
DataField: fnd6_newa2v1300_opeps
DataFieldDescription: Earnings Per Share from Operations
DataField: fnd6_eventv110_gdwlid12
DataFieldDescription: Impairments Diluted EPS - 12mm
DataField: retained_earnings
DataFieldDescription: Retained Earnings
DataField: fnd6_newqv1300_cshprq
DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - Basic
DataField: fnd6_txbco
DataFieldDescription: Excess Tax Benefit Stock Options - Cash Flow Operating
DataField: fnd6_fatl
DataFieldDescription: Property, Plant, and Equipment - Leases at Cost
DataField: fnd6_newqv1300_spcedq
DataFieldDescription: S&P Core Earnings EPS Diluted
DataField: fnd6_cptnewqeventv110_epsx12
DataFieldDescription: Earnings Per Share (Basic) - Excluding Extraordinary Items - 12 Months Moving
DataField: fnd6_zipcode
DataFieldDescription: ZIP code related to the company
DataField: fnd6_newqeventv110_drcq
DataFieldDescription: Deferred Revenue - Current
DataField: fnd6_newqeventv110_dcomq
DataFieldDescription: Deferred Compensation
DataField: fnd6_newqeventv110_gdwlipq
DataFieldDescription: Impairment of Goodwill Pretax
DataField: fnd6_mibt
DataFieldDescription: Noncontrolling Interests - Total - Balance Sheet
DataField: fnd6_newqeventv110_pncpeps12
DataFieldDescription: Core Pension Adjustment 12MM Basic EPS Effect Preliminary
DataField: fnd6_newqeventv110_acchgq
DataFieldDescription: Accounting Changes - Cumulative Effect
DataField: fnd6_esopnr
DataFieldDescription: Preferred ESOP Obligation - Non-Redeemable
DataField: fnd6_eventv110_spced12
DataFieldDescription: S&P Core Earnings EPS Diluted 12MM
DataField: fnd6_cptnewqv1300_atq
DataFieldDescription: Assets - Total
DataField: fnd6_prstkc
DataFieldDescription: Purchase of Common and Preferred Stock
DataField: fnd6_newa1v1300_dp
DataFieldDescription: Depreciation and Amortization
DataField: fnd6_eventv110_aqepsq
DataFieldDescription: Acquisition/Merger Basic EPS Effect
DataField: fnd6_ceql
DataFieldDescription: Common Equity - Liquidation Value
DataField: fnd6_cptmfmq_oibdpq
DataFieldDescription: Operating Income Before Depreciation - Quarterly
DataField: fnd6_newqv1300_ibq
DataFieldDescription: Income Before Extraordinary Items
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_cuo1conafv110_item
DataFieldDescription: Financial item
DataField: guidance_estimate_value
DataFieldDescription: Estimated value for basic annual financial guidance
DataField: anl4_afv4_median_eps
DataFieldDescription: Earnings per share - median of estimations
DataField: max_free_cashflow_per_share_guidance
DataFieldDescription: The maximum guidance value for free cash flow per share.
DataField: cashflow_per_share_min_guidance_quarterly
DataFieldDescription: Minimum guidance value for Cash Flow Per Share
DataField: cashflow_per_share_average
DataFieldDescription: Cash Flow Per Share - average of estimations with a delay of 1 quarter
DataField: anl4_epsr_value
DataFieldDescription: GAAP Earnings per share - announced financial value
DataField: anl4_ebit_high
DataFieldDescription: Earnings before interest and taxes - The highest estimation
DataField: anl4_basicconqfv110_numest
DataFieldDescription: The number of forecasts counted in aggregation
DataField: total_assets_reported_value
DataFieldDescription: Total Assets - actual value
DataField: est_ffo
DataFieldDescription: Funds From Operation - Summary on Estimations, Mean
DataField: anl4_bvps_high
DataFieldDescription: Book value - the highest estimation, per share
DataField: anl4_cuo1guidaf_item
DataFieldDescription: Financial item
DataField: free_cash_flow_per_share_max_guidance
DataFieldDescription: The maximum guidance value for Free Cash Flow Per Share on an annual basis.
DataField: earnings_per_share_median_value
DataFieldDescription: Earnings per share - median of estimations
DataField: max_net_profit_guidance
DataFieldDescription: The maximum guidance value for net profit on an annual basis.
DataField: anl4_afv4_eps_mean
DataFieldDescription: Earnings per share - mean of estimations for annual frequency
DataField: anl4_epsr_mean
DataFieldDescription: GAAP Earnings per share - mean of estimations
DataField: anl4_cuo1guidaf_minguidance
DataFieldDescription: Minimum guidance value
DataField: sales_guidance_value
DataFieldDescription: Sales - Guidance value for the annual period
DataField: anl4_qfd1_az_hgih_vid
DataFieldDescription: Dividend per share - The highest estimation
DataField: anl4_fsdtlestmtbscqv104_item
DataFieldDescription: Financial item
DataField: anl4_cfo_mean
DataFieldDescription: Cash Flow From Operations - mean of estimations
DataField: anl4_netdebt_flag
DataFieldDescription: Net debt - forecast type (revision/new/...)
DataField: anl4_qf_az_eps_number
DataFieldDescription: Earnings per share - number of estimations
DataField: anl4_dei2lqfv110_item
DataFieldDescription: Financial item
DataField: sales_estimate_count
DataFieldDescription: Sales - number of estimations
DataField: anl4_afv4_eps_high
DataFieldDescription: Earnings per share - The highest estimation
DataField: anl4_basicconafv110_low
DataFieldDescription: The lowest estimation
DataField: est_netprofit
DataFieldDescription: Net profit - mean of estimations
DataField: pv13_hierarchy_min10_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_company_total
DataFieldDescription: Total number of companies in the sector
DataField: pv13_rha2_min10_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_term_sector_total
DataFieldDescription: Number of terminal sectors for the company
DataField: pv13_hierarchy_f2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_min20_1000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_only_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_513_sector
DataFieldDescription: grouping fields
DataField: rel_ret_all
DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
DataField: pv13_hierarchy23_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_top3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_comproduct_company
DataFieldDescription: Company product
DataField: pv13_hierarchy_min2_pureplay_only_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_top3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_revere_parent
DataFieldDescription: Code of parent sector
DataField: pv13_hierarchy_min54_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_focused_pureplay_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_industry_3000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min30_3000_mapped_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min10_3000_513_sector
DataFieldDescription: grouping fields
DataField: rel_ret_part
DataFieldDescription: Averaged one-day return of the instrument's partners
DataField: pv13_h_min24_500_sector
DataFieldDescription: Grouping fields for top 500
DataField: pv13_hierarchy_min51_f4_513_sector
DataFieldDescription: grouping fields
DataField: pv13_ustomergraphrank_auth_rank
DataFieldDescription: the HITS authority score of customers
DataField: pv13_hierarchy_min20_3k_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min22_1000_sector
DataFieldDescription: Grouping fields for top 1000
DataField: implied_volatility_mean_180
DataFieldDescription: At-the-money option-implied volatility mean for 180 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: implied_volatility_mean_90
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
DataField: historical_volatility_10
DataFieldDescription: Close-to-close Historical volatility over 10 days
DataField: implied_volatility_mean_skew_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
DataField: implied_volatility_call_120
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
DataField: implied_volatility_call_360
DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
DataField: historical_volatility_60
DataFieldDescription: Close-to-close Historical volatility over 60 days
DataField: implied_volatility_put_360
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
DataField: implied_volatility_mean_skew_360
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
DataField: parkinson_volatility_120
DataFieldDescription: Parkinson model's historical volatility over 120 days
DataField: implied_volatility_mean_skew_30
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
DataField: implied_volatility_call_150
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
DataField: implied_volatility_mean_270
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
DataField: implied_volatility_put_10
DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
DataField: implied_volatility_mean_60
DataFieldDescription: At-the-money option-implied volatility mean for 60 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: implied_volatility_mean_skew_270
DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
DataField: implied_volatility_put_20
DataFieldDescription: At-the-money option-implied volatility for Put Option for 20 days
DataField: implied_volatility_mean_skew_150
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
DataField: implied_volatility_put_150
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
DataField: parkinson_volatility_90
DataFieldDescription: Parkinson model's historical volatility over 90 days
DataField: implied_volatility_put_30
DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
DataField: implied_volatility_mean_10
DataFieldDescription: At-the-money option-implied volatility mean for 10 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: parkinson_volatility_30
DataFieldDescription: Parkinson model's historical volatility over 30 days
DataField: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_put_60
DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
DataField: nws12_prez_4l
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
DataField: news_ton_high
DataFieldDescription: Highest price reached during the session before the time of news
DataField: nws12_prez_eodhigh
DataFieldDescription: Highest price reached between the time of news and the end of the session
DataField: nws12_afterhsz_div_y
DataFieldDescription: Annual yield
DataField: nws12_prez_3l
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
DataField: nws12_prez_1p
DataFieldDescription: The minimum of L or S above for 1-minute bucket
DataField: nws12_mainz_eodvwap
DataFieldDescription: Volume weighted average price between the time of news and the end of the session
DataField: news_mins_20_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
DataField: nws12_mainz_4l
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
DataField: nws12_afterhsz_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: nws12_afterhsz_provider
DataFieldDescription: index of name of the news provider
DataField: nws12_mainz_90_min
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
DataField: nws12_mainz_sl
DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS = 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
DataField: nws12_prez_tonlast
DataFieldDescription: Price at the time of news
DataField: news_mins_1_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_afterhsz_result_vs_index
DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
DataField: news_mins_20_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
DataField: nws12_allz_result2
DataFieldDescription: Percent change between the price at the time of the news release and the price at the close of the session
DataField: nws12_prez_provider
DataFieldDescription: index of name of the news provider
DataField: nws12_afterhsz_41rta
DataFieldDescription: 14-day Average True Range
DataField: nws12_prez_spylast
DataFieldDescription: Last Price of the SPY at the time of the news
DataField: nws12_afterhsz_02l
DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
DataField: news_mins_3_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
DataField: nws12_mainz_3l
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
DataField: nws12_afterhsz_2p
DataFieldDescription: The minimum of L or S above for 2-minute bucket
DataField: nws12_mainz_prev_vol
DataFieldDescription: Previous day's session volume
DataField: nws12_mainz_opengap
DataFieldDescription: (DayOpen - PrevClose) / PrevClose
DataField: nws12_prez_57s
DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
DataField: nws12_prez_01p
DataFieldDescription: The minimum of L or S above for 10-minute bucket
DataField: nws12_mainz_vol_ratio
DataFieldDescription: Curr_Vol / Mov_Vol
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: nws18_qep
DataFieldDescription: News sentiment based on positive and negative words on global equity
DataField: rp_css_ratings
DataFieldDescription: Composite sentiment score of analyst ratings-related news
DataField: rp_ess_ptg
DataFieldDescription: Event sentiment score of price target news
DataField: rp_nip_dividends
DataFieldDescription: News impact projection of dividends news
DataField: rp_css_revenue
DataFieldDescription: Composite sentiment score of revenue news
DataField: rp_ess_insider
DataFieldDescription: Event sentiment score of insider trading news
DataField: nws18_qcm
DataFieldDescription: News sentiment of relevant news with high confidence
DataField: rp_nip_partner
DataFieldDescription: News impact projection of partnership news
DataField: rp_nip_revenue
DataFieldDescription: News impact projection of revenue news
DataField: rp_css_price
DataFieldDescription: Composite sentiment score of stock price news
DataField: nws18_ber
DataFieldDescription: News sentiment specializing in earnings result
DataField: rp_nip_ratings
DataFieldDescription: News impact projection of analyst ratings-related news
DataField: rp_css_credit_ratings
DataFieldDescription: Composite sentiment score of credit ratings news
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: rp_ess_partner
DataFieldDescription: Event sentiment score of partnership news
DataField: rp_css_credit
DataFieldDescription: Composite sentiment score of credit news
DataField: rp_nip_credit
DataFieldDescription: News impact projection of credit news
DataField: rp_css_equity
DataFieldDescription: Composite sentiment score of equity action news
DataField: rp_nip_credit_ratings
DataFieldDescription: News impact projection of credit ratings news
DataField: rp_css_inverstor
DataFieldDescription: Composite sentiment score of investor relations news
DataField: rp_ess_society
DataFieldDescription: Event sentiment score of society-related news
DataField: rp_ess_price
DataFieldDescription: Event sentiment score of stock price news
DataField: rp_nip_inverstor
DataFieldDescription: News impact projection of investor relations news
DataField: nws18_event_similarity_days
DataFieldDescription: Days since a similar event was detected
DataField: rp_css_labor
DataFieldDescription: Composite sentiment score of labor issues news
DataField: nws18_nip
DataFieldDescription: Degree of impact of the news
DataField: rp_css_product
DataFieldDescription: Composite sentiment score of product and service-related news
DataField: rp_css_assets
DataFieldDescription: Composite sentiment score of assets news
DataField: rp_nip_product
DataFieldDescription: News impact projection of product and service-related news
DataField: rp_nip_technical
DataFieldDescription: News impact projection based on technical analysis
DataField: fnd2_a_sbcpnargmpmtwgtm
DataFieldDescription: The weighted average period between the balance sheet date and expiration for all awards outstanding under the plan, which may be expressed in a decimal value for number of years.
DataField: fnd2_a_dbplannpicbnfcst
DataFieldDescription: The total amount of net periodic benefit cost for defined benefit plans for the period. Periodic benefit costs include the following components: service cost, interest cost, expected return on plan assets, gain (loss), prior service cost or credit, transition asset or obligation, and gain (loss) due to settlements or curtailments.
DataField: fnd2_dbplanbnfpaid
DataFieldDescription: The amount of payments made for which participants are entitled under a pension plan, including pension benefits, death benefits, and benefits due on termination of employment. Also includes payments made under a postretirement benefit plan, including prescription drug benefits, health care benefits, life insurance benefits, and legal, educational and advisory services. This item represents a periodic decrease to the plan obligations and a decrease to plan assets.
DataField: fnd2_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_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: fn_comp_options_exercises_weighted_avg_a
DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
DataField: fn_comp_options_exercisable_weighted_avg_a
DataFieldDescription: The weighted-average price as of the balance sheet date at which grantees can acquire the shares reserved for issuance on vested portions of options outstanding and currently exercisable under the stock option plan.
DataField: fn_income_from_equity_investments_q
DataFieldDescription: Income From Equity Method Investments
DataField: fnd2_a_acmopclcchngcfectnt
DataFieldDescription: Accumulated change, net of tax, in accumulated gains and losses from derivative instruments designated and qualifying as the effective portion of cash flow hedges. Includes an entity's share of an equity investee's Increase or Decrease in deferred hedging gains or losses.
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: fn_effect_of_exchange_rate_on_cash_and_equiv_a
DataFieldDescription: Amount of increase (decrease) from the effect of exchange rate changes on cash and cash equivalent balances held in foreign currencies.
DataField: fn_comp_options_grants_weighted_avg_q
DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options that were terminated.
DataField: fn_finite_lived_intangible_assets_net_a
DataFieldDescription: Finite Lived Intangible Assets, Net
DataField: fn_taxes_payable_a
DataFieldDescription: Carrying value as of the balance sheet date of obligations incurred and payable for statutory income, sales, use, payroll, excise, real, property, and other taxes. For classified balance sheets, used to reflect the current portion of the liabilities (due within 1 year or within the normal operating cycle if longer); for unclassified balance sheets, used to reflect the total liabilities (regardless of due date).
DataField: fn_op_lease_min_pay_due_a
DataFieldDescription: Amount of required minimum rental payments for leases having an initial or remaining non-cancelable letter-terms in excess of 1 year.
DataField: fn_repurchased_shares_value_q
DataFieldDescription: Shares repurchased and either retired or put into treasury stock, likely as part of a share buyback plan.
DataField: fn_debt_instrument_carrying_amount_a
DataFieldDescription: Debt carrying amount
DataField: fnd2_a_flintasacmamtzcsrld
DataFieldDescription: Finite Lived Intangible Assets Accumulated Amortization, Customer Related
DataField: fnd2_a_dbplanintcst
DataFieldDescription: The increase in a defined benefit pension plan's projected benefit obligation or a defined benefit postretirement plan's accumulated postretirement benefit obligation due to the passage of time.
DataField: fn_op_lease_min_pay_due_in_2y_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 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fn_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_interest_paid_net_a
DataFieldDescription: Net interest
DataField: fn_comp_options_forfeitures_and_expirations_q
DataFieldDescription: For presentations that combine terminations, the number of shares under options that were cancelled during the reporting period as a result of occurrence of a terminating event specified in contractual agreements pertaining to the stock option plan or that expired.
DataField: fnd2_a_landlandiprts
DataFieldDescription: Amount before accumulated depreciation and depletion of real estate held for productive use and additions or improvements to real estate held for productive use, examples include, but are not limited to, walkways, driveways, fences, and parking lots. Excludes land held for sale
DataField: fn_interest_payable_q
DataFieldDescription: Carrying value as of the balance sheet date of accrued interest payable on all forms of debt, including trade payables, that has been incurred and is unpaid. For classified balance sheets, used to reflect the current portion of the liabilities (due within 1 year or within the normal operating cycle if longer); for unclassified balance sheets, used to reflect the total liabilities (regardless of due date).
DataField: fn_comp_options_exercisable_weighted_avg_q
DataFieldDescription: The weighted-average price as of the balance sheet date at which grantees can acquire the shares reserved for issuance on vested portions of options outstanding and currently exercisable under the stock option plan.
DataField: fn_def_tax_assets_liab_net_a
DataFieldDescription: Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.
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_propplteqflublgland
DataFieldDescription: PPE, Buildings & Land, Useful Life, Maximum
DataField: fnd2_a_provisionfordbflact
DataFieldDescription: Provision For Doubtful Accounts In Period
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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