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_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: 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_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: 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: pcr_vol_90
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 90 days in the future.
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: call_breakeven_120
DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
DataField: 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_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_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: 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: put_breakeven_180
DataFieldDescription: Price at which a stock's put options with expiration 180 days in the future break even based on its recent bid/ask mean.
DataField: put_breakeven_720
DataFieldDescription: Price at which a stock's put options with expiration 720 days in the future break even based on its recent bid/ask mean.
DataField: pcr_oi_90
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
DataField: put_breakeven_1080
DataFieldDescription: Price at which a stock's put options with expiration 1080 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_60
DataFieldDescription: Price at which a stock's options with expiration 60 days in the future break even based on its recent bid/ask mean.
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: pcr_vol_10
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
DataField: option_breakeven_20
DataFieldDescription: Price at which a stock's options with expiration 20 days in the future break even based on its recent bid/ask mean.
DataField: 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_vol_150
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
DataField: call_breakeven_60
DataFieldDescription: Price at which a stock's call options with expiration 60 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_720
DataFieldDescription: Price at which a stock's options with expiration 720 days in the future break even based on its recent bid/ask mean.
DataField: option_breakeven_90
DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
DataField: pcr_vol_20
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
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_120
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 120 days in the future.
DataField: pcr_vol_180
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
DataField: pcr_oi_720
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.
DataField: fnd6_ibmii
DataFieldDescription: Income before Extraordinary Items and Noncontrolling Interests
DataField: fnd6_eventv110_txdbcaq
DataFieldDescription: Current Deferred Tax Asset
DataField: fnd6_emps
DataFieldDescription: Employees
DataField: fnd6_newqv1300_ancq
DataFieldDescription: Non-Current Assets - Total
DataField: fnd6_newqeventv110_tfvlq
DataFieldDescription: Total Fair Value Liabilities
DataField: fnd6_newa2v1300_xsga
DataFieldDescription: Selling, General and Administrative Expense
DataField: fnd6_optex
DataFieldDescription: Options Exercisable (000)
DataField: assets_curr
DataFieldDescription: Current Assets - Total
DataField: fnd6_newqv1300_ivstq
DataFieldDescription: Short-Term Investments - Total
DataField: fnd6_eventv110_gdwlieps12
DataFieldDescription: Impairment of Goodwill Basic EPS Effect 12MM
DataField: fnd6_sics
DataFieldDescription: SIC Code
DataField: fnd6_dd3
DataFieldDescription: Debt Due in 3rd Year
DataField: fnd6_cptnewqv1300_nopiq
DataFieldDescription: Non-Operating Income (Expense) - Total
DataField: fnd6_newa2v1300_txdb
DataFieldDescription: Deferred Taxes - Balance Sheet
DataField: liabilities_curr
DataFieldDescription: Current Liabilities - Total
DataField: fnd6_dd1q
DataFieldDescription: Long-Term Debt Due in 1 Year
DataField: fnd6_newqeventv110_ibadjq
DataFieldDescription: Income Before Extraordinary Items - Adjusted for Common Stock Equivalents
DataField: fnd6_newqv1300_optfvgrq
DataFieldDescription: Options - Fair Value of Options Granted
DataField: fnd6_currencyqv1300_curcd
DataFieldDescription: ISO Currency Code - Company Annual Market
DataField: fnd6_mfma2_oancf
DataFieldDescription: Operating Activities - Net Cash Flow
DataField: fnd6_siv
DataFieldDescription: Sale of Investments
DataField: fnd6_mfma2_txach
DataFieldDescription: Income Taxes - Accrued - Increase/(Decrease)
DataField: fnd6_newqeventv110_glaq
DataFieldDescription: Gain/Loss After-Tax
DataField: fnd6_newa2v1300_prsho
DataFieldDescription: Redeem Pfd Shares Outs (000)
DataField: fnd6_newqv1300_txdbq
DataFieldDescription: Deferred Taxes - Balance Sheet
DataField: fnd6_newqeventv110_ibmiiq
DataFieldDescription: Income before Extraordinary Items and Noncontrolling Interests
DataField: fnd6_newqeventv110_prcpeps12
DataFieldDescription: Core Post-Retirement Adjustment 12MM Basic EPS Effect Preliminary
DataField: fnd6_cicurr
DataFieldDescription: Comp Inc - Currency Trans Adj
DataField: fnd6_newqv1300_oepf12
DataFieldDescription: Earnings Per Share - Diluted - from Operations - 12MM
DataField: fnd6_eventv110_gdwliepsq
DataFieldDescription: Impairment of Goodwill Basic EPS Effect
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: earnings_per_share_reported_value
DataFieldDescription: Reported Earnings Per Share - Actual Value
DataField: est_tot_goodwill
DataFieldDescription: Total Goodwill - mean of estimations
DataField: anl4_basicconqfv110_high
DataFieldDescription: The highest estimation
DataField: anl4_adxqfv110_numest
DataFieldDescription: The number of forecasts counted in aggregation
DataField: anl4_ads1detailqfv110_bk
DataFieldDescription: Broker name (int)
DataField: anl4_ebitda_low
DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - The lowest estimation
DataField: anl4_fsdtlestmtqfv4_item
DataFieldDescription: Financial item
DataField: anl4_totgw_median
DataFieldDescription: Total Goodwill - median of estimations
DataField: anl4_cfi_low
DataFieldDescription: Cash Flow From Investing - The lowest estimation
DataField: reporting_currency_code_9
DataFieldDescription: Home currency of instrument
DataField: shares_outstanding_max_guidance
DataFieldDescription: Maximum guidance value for Shares
DataField: anl4_detailltv4v104_preest
DataFieldDescription: The previous estimation of financial item
DataField: anl4_fcfps_median
DataFieldDescription: Free cash flow - summary on estimations, 50th-percentile, per share
DataField: anl4_qfv4_eps_high
DataFieldDescription: Earnings per share - The highest estimation
DataField: cashflow_per_share_estimate_count
DataFieldDescription: Cash Flow Per Share - number of estimations - delay1
DataField: max_ebit_guidance
DataFieldDescription: The maximum guidance value for Earnings Before Interest and Taxes (EBIT) on an annual basis.
DataField: est_cashflow_ps
DataFieldDescription: Cash Flow Per Share - average of estimations
DataField: anl4_netprofita_low
DataFieldDescription: Adjusted net income - the lowest estimation
DataField: net_profit_reported_value
DataFieldDescription: Net profit- announced financial value
DataField: anl4_ptpr_flag
DataFieldDescription: Reported Pretax income - forecast type (revision/new/...)
DataField: actual_dividend_value_quarterly
DataFieldDescription: Dividend - Actual value for the quarter
DataField: total_assets_amount
DataFieldDescription: Total Assets - actual value
DataField: anl4_qfv4_cfps_mean
DataFieldDescription: Cash Flow Per Share - average of estimations
DataField: anl4_basicconltv110_low
DataFieldDescription: The lowest estimation
DataField: pretax_income_reported_min_guidance
DataFieldDescription: Reported Pretax income - minimum guidance value
DataField: anl4_cuo1guidaf_item
DataFieldDescription: Financial item
DataField: anl4_capex_mean
DataFieldDescription: Capital Expenditures - mean of estimations
DataField: median_sales_estimate
DataFieldDescription: Sales - median of estimations
DataField: anl4_af_div_value
DataFieldDescription: Dividend - Actual value
DataField: min_shares_outstanding_guidance
DataFieldDescription: Minimum guidance value for Shares
DataField: pv13_h_min30_3000_mapped_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_1k_513_sector
DataFieldDescription: grouping fields
DataField: rel_ret_part
DataFieldDescription: Averaged one-day return of the instrument's partners
DataField: pv13_revere_company_total
DataFieldDescription: Total number of companies in the sector
DataField: pv13_region
DataFieldDescription: Unique code of the region
DataField: pv13_di_5l
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min20_3k_sector
DataFieldDescription: grouping fields
DataField: pv13_h_min5_500_sector
DataFieldDescription: Grouping fields
DataField: pv13_hierarchy_min2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_com_rk_au
DataFieldDescription: the HITS authority score of competitors
DataField: pv13_hierarchy_min2_focused_only_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_f2_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min52_sector
DataFieldDescription: grouping fields
DataField: pv13_rcsed_6l
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min2_pureplay_only_sector
DataFieldDescription: grouping fields
DataField: rel_num_all
DataFieldDescription: number of the companies whose product overlapped with the instrument
DataField: pv13_h_min10_all_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min5_corr21_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_h_f3_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min100_2000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_custretsig_retsig
DataFieldDescription: Sign of customer return
DataField: pv13_hierarchy_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min2_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_rha2_min5_1000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_513_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_min5_1000_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f1_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min10_sector_3000_513_sector
DataFieldDescription: grouping fields
DataField: pv13_hierarchy_min51_f3_513_sector
DataFieldDescription: grouping fields
DataField: pv13_r2_min2_1000_sector
DataFieldDescription: grouping fields
DataField: implied_volatility_put_60
DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
DataField: implied_volatility_mean_skew_1080
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
DataField: implied_volatility_mean_skew_90
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
DataField: implied_volatility_put_10
DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
DataField: implied_volatility_mean_skew_120
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
DataField: implied_volatility_put_720
DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
DataField: parkinson_volatility_30
DataFieldDescription: Parkinson model's historical volatility over 30 days
DataField: implied_volatility_call_270
DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
DataField: implied_volatility_mean_skew_720
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
DataField: parkinson_volatility_120
DataFieldDescription: Parkinson model's historical volatility over 120 days
DataField: implied_volatility_call_10
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
DataField: implied_volatility_mean_skew_60
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
DataField: implied_volatility_mean_720
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
DataField: implied_volatility_call_60
DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
DataField: parkinson_volatility_20
DataFieldDescription: Parkinson model's historical volatility over 20 days
DataField: implied_volatility_call_360
DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
DataField: implied_volatility_mean_20
DataFieldDescription: At-the-money option-implied volatility mean for 20 days
DataField: implied_volatility_put_180
DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
DataField: parkinson_volatility_180
DataFieldDescription: Parkinson model's historical volatility over 180 days
DataField: implied_volatility_mean_360
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
DataField: implied_volatility_call_1080
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
DataField: implied_volatility_mean_skew_10
DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
DataField: implied_volatility_put_1080
DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
DataField: implied_volatility_mean_skew_30
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
DataField: implied_volatility_mean_150
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
DataField: parkinson_volatility_60
DataFieldDescription: Parkinson model's historical volatility over 60 days
DataField: implied_volatility_call_30
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
DataField: historical_volatility_60
DataFieldDescription: Close-to-close Historical volatility over 60 days
DataField: historical_volatility_150
DataFieldDescription: Close-to-close Historical volatility over 150 days
DataField: implied_volatility_call_120
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
DataField: nws12_mainz_mainvwap
DataFieldDescription: Main session volume weighted average price
DataField: nws12_prez_2s
DataFieldDescription: Number of minutes that elapsed before price went down 2 percentage points
DataField: news_max_dn_ret
DataFieldDescription: Percent change from the price at the time of the news to the after the news low
DataField: news_mins_3_pct_dn
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
DataField: nws12_afterhsz_short_interest
DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
DataField: nws12_mainz_result2
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
DataField: nws12_mainz_01l
DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
DataField: nws12_mainz_tonhigh
DataFieldDescription: Highest price reached during the session before the time of news
DataField: nws12_afterhsz_2p
DataFieldDescription: The minimum of L or S above for 2-minute bucket
DataField: nws12_prez_1l
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: nws12_mainz_01p
DataFieldDescription: The minimum of L or S above for 10-minute bucket
DataField: news_prev_vol
DataFieldDescription: Previous day's session volume
DataField: news_eod_vwap
DataFieldDescription: Volume weighted average price between the time of news and the end of the session
DataField: nws12_mainz_90_min
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
DataField: nws12_afterhsz_maxdown
DataFieldDescription: Percent change from the price at the time of the news to the after the news low
DataField: news_atr14
DataFieldDescription: 14-day Average True Range
DataField: news_ton_last
DataFieldDescription: Price at the time of news
DataField: nws12_afterhsz_newssess
DataFieldDescription: Index of the session in which the news was reported
DataField: nws12_afterhsz_10_min
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
DataField: news_spy_last
DataFieldDescription: Last Price of the SPY at the time of the news
DataField: news_mins_1_pct_up
DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
DataField: news_max_dn_amt
DataFieldDescription: The price at the time of the news minus the after the news low
DataField: nws12_prez_01s
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
DataField: nws12_afterhsz_mktcap
DataFieldDescription: Reported market capitalization for the calendar day of the session
DataField: nws12_prez_prevwap
DataFieldDescription: Pre-session volume weighted average price
DataField: nws12_afterhsz_peratio
DataFieldDescription: Reported price to earnings ratio for the calendar day of the session
DataField: news_tot_ticks
DataFieldDescription: Total number of ticks for the trading day
DataField: nws12_prez_3l
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
DataField: nws12_mainz_newssess
DataFieldDescription: Index of session in which the news was reported
DataField: news_range_stddev
DataFieldDescription: (RangeAmt - AvgRange) / RangeStdDev, where AvgRange is the average of the daily range, and RangeStdDev is one standard deviation for the daily range, both for 30 calendar days
DataField: top1000
DataFieldDescription: 20140630
DataField: top200
DataFieldDescription: 20140630
DataField: top3000
DataFieldDescription: 20140630
DataField: top500
DataFieldDescription: 20140630
DataField: topsp500
DataFieldDescription: 20140630
DataField: rp_nip_assets
DataFieldDescription: News impact projection of assets news
DataField: rp_ess_insider
DataFieldDescription: Event sentiment score of insider trading news
DataField: rp_nip_society
DataFieldDescription: News impact projection of society-related 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_nip_legal
DataFieldDescription: News impact projection of legal news
DataField: rp_nip_dividends
DataFieldDescription: News impact projection of dividends news
DataField: rp_ess_ptg
DataFieldDescription: Event sentiment score of price target news
DataField: rp_css_revenue
DataFieldDescription: Composite sentiment score of revenue news
DataField: rp_nip_revenue
DataFieldDescription: News impact projection of revenue news
DataField: nws18_sse
DataFieldDescription: Sentiment of phrases impacting the company
DataField: rp_nip_credit_ratings
DataFieldDescription: News impact projection of credit ratings news
DataField: rp_css_legal
DataFieldDescription: Composite sentiment score of legal news
DataField: rp_nip_equity
DataFieldDescription: News impact projection of equity action news
DataField: rp_nip_labor
DataFieldDescription: News impact projection of labor issues news
DataField: nws18_bee
DataFieldDescription: News sentiment specializing in growth of earnings
DataField: rp_ess_mna
DataFieldDescription: Event sentiment score of mergers and acquisitions-related news
DataField: rp_css_inverstor
DataFieldDescription: Composite sentiment score of investor relations news
DataField: rp_css_price
DataFieldDescription: Composite sentiment score of stock price news
DataField: nws18_event_similarity_days
DataFieldDescription: Days since a similar event was detected
DataField: rp_css_equity
DataFieldDescription: Composite sentiment score of equity action news
DataField: rp_css_mna
DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
DataField: rp_nip_credit
DataFieldDescription: News impact projection of credit news
DataField: nws18_ghc_lna
DataFieldDescription: Change in analyst recommendation
DataField: rp_css_partner
DataFieldDescription: Composite sentiment score of partnership news
DataField: nws18_ber
DataFieldDescription: News sentiment specializing in earnings result
DataField: nws18_ssc
DataFieldDescription: Sentiment of the news calculated using multiple techniques
DataField: rp_ess_assets
DataFieldDescription: Event sentiment score of assets news
DataField: rp_css_earnings
DataFieldDescription: Composite sentiment score of earnings news
DataField: rp_css_ratings
DataFieldDescription: Composite sentiment score of analyst ratings-related news
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_comp_options_out_number_q
DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
DataField: fn_comp_options_grants_weighted_avg_a
DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options that were terminated.
DataField: fnd2_dbplanartonplas
DataFieldDescription: Defined Benefit Plan, Benefits Paid, Plan Assets
DataField: fn_proceeds_from_issuance_of_common_stock_q
DataFieldDescription: The cash inflow from the additional capital contribution to the entity.
DataField: fn_oth_income_loss_net_of_tax_a
DataFieldDescription: Amount after tax and reclassification adjustments of other comprehensive income (loss).
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_dbplanepdrtnplas
DataFieldDescription: An amount calculated as a basis for determining the extent of delayed recognition of the effects of changes in the fair value of assets. The expected return on plan assets is determined based on the expected long-term rate of return on plan assets and the market-related value of plan assets.
DataField: fnd2_a_dbplanservicecst
DataFieldDescription: The actuarial present value of benefits attributed by the pension benefit formula to services rendered by employees during the period. The portion of the expected postretirement benefit obligation attributed to employee service during the period. The service cost component is a portion of the benefit obligation and is unaffected by the funded status of the plan.
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: fn_excess_tax_benefit_from_share_based_comp_fin_activities_q
DataFieldDescription: Amount of cash inflow from realized tax benefit related to deductible compensation cost reported on the entity's tax return for equity instruments in excess of the compensation cost for those instruments recognized for financial reporting purposes.
DataField: fnd2_dbplanepdfbnfpytwo
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
DataField: fnd2_a_sbcpnargtbysbpmtwpwrr
DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options of the plan that expired.
DataField: fn_debt_instrument_carrying_amount_a
DataFieldDescription: Debt carrying amount
DataField: fn_liab_fair_val_l1_q
DataFieldDescription: Liabilities Fair Value, Recurring, Level 1
DataField: fn_liab_fair_val_l3_q
DataFieldDescription: Liabilities Fair Value, Recurring, Level 3
DataField: fnd2_a_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: fn_line_of_credit_facility_amount_out_a
DataFieldDescription: Amount borrowed under the credit facility as of the balance sheet date.
DataField: fn_finite_lived_intangible_assets_gross_q
DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
DataField: fn_accum_oth_income_loss_fx_adj_net_of_tax_q
DataFieldDescription: Accumulated adjustment, net of tax, that results from the process of translating subsidiary financial statements and foreign equity investments into the reporting currency from the functional currency of the reporting entity, net of reclassification of realized foreign currency translation gains or losses.
DataField: fn_proceeds_from_issuance_of_debt_a
DataFieldDescription: The cash inflow during the period from additional borrowings in aggregate debt. Includes proceeds from short-term and long-term debt.
DataField: fn_oth_comp_grants_weighted_avg_grant_date_fair_value_q
DataFieldDescription: Quarterly Share-Based Compensation Equity Instruments Other Than Options Nonvested Weighted Average Grant Date Fair Value
DataField: fn_debt_issuance_costs_a
DataFieldDescription: Amount of debt issuance costs (for example, but not limited to, legal, accounting, broker, and regulatory fees).
DataField: fn_repayments_of_lines_of_credit_q
DataFieldDescription: Amount of cash outflow for payment of an obligation from a lender, including but not limited to, letter of credit, standby letter of credit and revolving credit arrangements.
DataField: fn_unrecognized_tax_benefits_a
DataFieldDescription: Amount of unrecognized tax benefits.
DataField: fn_payments_for_repurchase_of_common_stock_q
DataFieldDescription: Value reported on Cash Flow Statement. May include shares repurchased as part of a buyback plan, as well as shares purchased for employee compensation, etc.
DataField: fnd2_dfdtxasoprlcarryfwd
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible operating loss carryforwards.
DataField: fnd2_unrgtxbnfinregfprtxps
DataFieldDescription: Amount of increase in unrecognized tax benefits resulting from tax positions taken in prior period tax returns.
DataField: fn_proceeds_from_lt_debt_q
DataFieldDescription: Proceeds From Issuance Of Debt, Long Term
DataField: fn_oth_income_loss_derivatives_qualifying_as_hedges_of_tax_q
DataFieldDescription: Amount after tax and reclassification adjustments, of increase (decrease) in accumulated gain (loss) from derivative instruments designated and qualifying as the effective portion of cash flow hedges and an entity's share of an equity investee's increase (decrease) in deferred hedging gain (loss).
DataField: 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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