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896 lines
54 KiB
896 lines
54 KiB
任务指令
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你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
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核心规则
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设计维度框架
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维度1：时间序列动量（TM）
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核心概念：捕捉行业价格的趋势、动量和形态变化
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设计思路：
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动量的变化率、加速度或平滑度构建
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动量衰减或增强模式识别
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价格与成交量关系的时序分析
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维度2：横截面领导力（CL）
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核心概念：识别行业内部的分化、龙头效应和相对强度
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bucket（用于龙头股筛选）
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设计思路：
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行业内部龙头股与平均表现的差异
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行业成分股的离散度分析
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相对排名的变化和稳定性
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维度3：市场状态适应性（MS）
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核心概念：根据市场环境动态调整因子逻辑
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设计思路：
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波动率调整的动量指标
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不同市场状态（高/低波动）使用不同的回顾期
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条件逻辑下的参数动态调整
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维度4：行业间联动（IS）
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多序列相关性分析
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设计思路：
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领先-滞后行业的相关性分析
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行业间动量传导效应
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板块轮动的早期信号识别
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维度5：交易行为情绪（TS）
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核心概念：基于交易行为和情绪指标的反转信号
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设计思路：
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超买超卖状态识别
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交易拥挤度指标
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情绪极端值后的均值回归
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复合因子设计原则
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强制要求：
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每个表达式必须融合至少两个设计维度
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必须使用提供的操作符列表中的函数
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因子应具有经济逻辑解释性
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推荐组合模式：
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TM + CL：时序动量 + 横截面领导力
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示例：行业动量加速度 × 龙头股相对强度
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TM + MS：时序动量 + 状态适应性
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示例：波动率调整后的动量指标
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CL + IS：横截面 + 行业间联动
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示例：龙头股表现与相关行业的领先滞后关系
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MS + TS：状态适应 + 交易情绪
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示例：不同市场状态下的反转信号
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IS + TS：行业联动 + 交易情绪
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示例：行业间相关性变化与交易拥挤度
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参数化建议：
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使用不同的时间窗口组合（短/中/长周期）
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尝试不同的权重分配方式
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考虑非线性变换（log, power, sqrt）
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使用条件逻辑增强鲁棒性
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表达式构建指南
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基本结构：
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text
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复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
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操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
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操作符使用策略：
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算术运算：abs(x), add(x, y, filter = false), densify(x), divide(x, y), inverse(x), max(x, y, ..), min(x, y ..), multiply(x ,y, ... , filter=false), power(x, y), reverse(x), sign(x), signed_power(x, y), sqrt(x), subtract(x, y, filter=false)
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条件逻辑：and(input1, input2), if_else(input1, input2, input 3), input1 < input2, input1 <= input2, input1 == input2, input1 > input2, input1 >= input2, input1!= input2, is_nan(input), not(x), or(input1, input2)
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时间序列操作：days_from_last_change(x), hump(x, hump = 0.01), kth_element(x, d, k), last_diff_value(x, d), ts_arg_max(x, d), ts_arg_min(x, d), ts_av_diff(x, d), ts_backfill(x,lookback = d, k=1, ignore="NAN"), ts_corr(x, y, d), ts_count_nans(x ,d), ts_covariance(y, x, d), ts_decay_linear(x, d, dense = false), ts_delay(x, d), ts_delta(x, d), ts_mean(x, d), ts_product(x, d), "ts_quantile(x,d, driver=""gaussian"" )", ts_rank(x, d, constant = 0), ts_regression(y, x, d, lag = 0, rettype = 0), ts_scale(x, d, constant = 0), ts_std_dev(x, d), ts_step(1), ts_sum(x, d), ts_zscore(x, d)
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横截面操作: normalize(x, useStd = false, limit = 0.0), quantile(x, driver = gaussian, sigma = 1.0), rank(x, rate=2), scale(x, scale=1, longscale=1, shortscale=1), winsorize(x, std=4), zscore(x)
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向量操作符：vec_avg(x), vec_sum(x)
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转换操作符: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10"), trade_when(x, y, z)
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聚合操作符: group_backfill(x, group, d, std = 4.0), group_mean(x, weight, group), group_neutralize(x, group), group_rank(x, group), group_scale(x, group), group_zscore(x, group), subtract(x, y, filter=false), multiply(x ,y, ... , filter=false), divide(x, y), add(x, y, filter = false)
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*=====*
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注意事项:
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避免过度复杂的嵌套
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使用经济直觉验证逻辑合理性
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考虑实际交易可行性
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包含风险控制元素（如波动率调整）
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*=====*
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参数逻辑：参数d（回顾期）应在[5, 10, 20, 30, 60, 120]等具有市场意义（周、月、季度、半年）的数值中合理选择并差异化。
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行业隐含：通过group_mean、group_rank等函数或假设表达式在行业指数上运行来体现“行业”逻辑。
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输出格式：
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输出必须是且仅是 100行纯文本。
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每一行是一个完整、独立、语法正确的WebSim表达式。
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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。
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示例思维（仅供理解，不输出）
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一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
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multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
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这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
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现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
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**输出格式**(一行一个表达式, 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)：
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表达式
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表达式
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表达式
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...
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表达式
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请提供具体的WQ表达式。
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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。
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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
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========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
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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
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特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
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Description: Absolute value of x
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Operator: add(x, y, filter = false)
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Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
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Operator: densify(x)
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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
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Operator: divide(x, y)
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Description: x / y
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Operator: inverse(x)
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Description: 1 / x
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Operator: log(x)
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Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
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Operator: max(x, y, ..)
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Description: Maximum value of all inputs. At least 2 inputs are required
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Operator: min(x, y ..)
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Description: Minimum value of all inputs. At least 2 inputs are required
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Operator: multiply(x ,y, ... , filter=false)
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Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
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Operator: power(x, y)
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Description: x ^ y
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Operator: reverse(x)
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Description: - x
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Operator: sign(x)
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Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
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Operator: signed_power(x, y)
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Description: x raised to the power of y such that final result preserves sign of x
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Operator: sqrt(x)
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Description: Square root of x
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Operator: subtract(x, y, filter=false)
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Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
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Operator: and(input1, input2)
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Description: Logical AND operator, returns true if both operands are true and returns false otherwise
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Operator: if_else(input1, input2, input 3)
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Description: If input1 is true then return input2 else return input3.
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Operator: input1 < input2
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Description: If input1 < input2 return true, else return false
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Operator: input1 <= input2
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Description: Returns true if input1 <= input2, return false otherwise
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Operator: input1 == input2
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Description: Returns true if both inputs are same and returns false otherwise
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Operator: input1 > input2
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Description: Logic comparison operators to compares two inputs
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Operator: input1 >= input2
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Description: Returns true if input1 >= input2, return false otherwise
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Operator: input1!= input2
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Description: Returns true if both inputs are NOT the same and returns false otherwise
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Operator: is_nan(input)
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Description: If (input == NaN) return 1 else return 0
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Operator: not(x)
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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).
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Operator: or(input1, input2)
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Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
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Operator: days_from_last_change(x)
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Description: Amount of days since last change of x
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Operator: hump(x, hump = 0.01)
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Description: Limits amount and magnitude of changes in input (thus reducing turnover)
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Operator: kth_element(x, d, k)
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Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
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Operator: last_diff_value(x, d)
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Description: Returns last x value not equal to current x value from last d days
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Operator: ts_arg_max(x, d)
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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
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Operator: ts_arg_min(x, d)
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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.
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Operator: ts_av_diff(x, d)
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Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
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Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
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Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
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Operator: ts_corr(x, y, d)
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Description: Returns correlation of x and y for the past d days
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Operator: ts_count_nans(x ,d)
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Description: Returns the number of NaN values in x for the past d days
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Operator: ts_covariance(y, x, d)
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Description: Returns covariance of y and x for the past d days
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Operator: ts_decay_linear(x, d, dense = false)
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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.
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Operator: ts_delay(x, d)
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Description: Returns x value d days ago
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Operator: ts_delta(x, d)
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Description: Returns x - ts_delay(x, d)
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Operator: ts_mean(x, d)
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Description: Returns average value of x for the past d days.
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Operator: ts_product(x, d)
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Description: Returns product of x for the past d days
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Operator: ts_quantile(x,d, driver="gaussian" )
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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.
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Operator: ts_rank(x, d, constant = 0)
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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.
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Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
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Description: Returns various parameters related to regression function
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Operator: ts_scale(x, d, constant = 0)
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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
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Operator: ts_std_dev(x, d)
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Description: Returns standard deviation of x for the past d days
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Operator: ts_step(1)
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Description: Returns days' counter
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Operator: ts_sum(x, d)
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Description: Sum values of x for the past d days.
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Operator: ts_zscore(x, d)
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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.
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Operator: normalize(x, useStd = false, limit = 0.0)
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Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
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Operator: quantile(x, driver = gaussian, sigma = 1.0)
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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
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Operator: rank(x, rate=2)
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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
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Operator: scale(x, scale=1, longscale=1, shortscale=1)
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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
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Operator: winsorize(x, std=4)
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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.
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Operator: zscore(x)
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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
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Operator: vec_avg(x)
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Description: Taking mean of the vector field x
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Operator: vec_sum(x)
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Description: Sum of vector field x
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Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
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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
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Operator: trade_when(x, y, z)
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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
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Operator: group_backfill(x, group, d, std = 4.0)
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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
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Operator: group_mean(x, weight, group)
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Description: All elements in group equals to the mean
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Operator: group_neutralize(x, group)
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Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
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Operator: group_rank(x, group)
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Description: Each elements in a group is assigned the corresponding rank in this group
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Operator: group_scale(x, group)
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Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
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Operator: group_zscore(x, group)
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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 =======================================
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========================= 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
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DataField: forward_price_120
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DataFieldDescription: Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
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DataField: put_breakeven_60
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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.
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DataField: forward_price_150
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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.
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DataField: option_breakeven_60
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DataFieldDescription: Price at which a stock's options with expiration 60 days in the future break even based on its recent bid/ask mean.
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DataField: put_breakeven_90
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DataFieldDescription: Price at which a stock's put options with expiration 90 days in the future break even based on its recent bid/ask mean.
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DataField: call_breakeven_60
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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.
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DataField: pcr_oi_1080
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DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
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DataField: put_breakeven_30
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DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
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DataField: forward_price_1080
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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.
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DataField: forward_price_180
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DataFieldDescription: Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.
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DataField: pcr_vol_30
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DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.
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DataField: put_breakeven_180
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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.
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DataField: option_breakeven_90
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DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
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DataField: pcr_vol_60
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DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
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DataField: option_breakeven_30
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DataFieldDescription: Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.
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DataField: option_breakeven_1080
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DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
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DataField: option_breakeven_270
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DataFieldDescription: Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.
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DataField: call_breakeven_20
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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.
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DataField: put_breakeven_720
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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.
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DataField: forward_price_30
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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.
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DataField: forward_price_360
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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.
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DataField: option_breakeven_360
|
|
DataFieldDescription: Price at which a stock's options with expiration 360 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_20
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
|
|
DataField: call_breakeven_1080
|
|
DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
|
|
DataField: pcr_vol_120
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
|
|
DataField: forward_price_720
|
|
DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: put_breakeven_360
|
|
DataFieldDescription: Price at which a stock's put options with expiration 360 days in the future break even based on its recent bid/ask mean.
|
|
DataField: 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: 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: cashflow
|
|
DataFieldDescription: Cashflow (Annual)
|
|
DataField: fnd6_newqv1300_cstkq
|
|
DataFieldDescription: Common/Ordinary Stock (Capital)
|
|
DataField: fnd6_newqeventv110_setpq
|
|
DataFieldDescription: Settlement (Litigation/Insurance) Pretax
|
|
DataField: cash
|
|
DataFieldDescription: Cash
|
|
DataField: fnd6_niadj
|
|
DataFieldDescription: Net Income Adjusted for Common/Ordinary Stock (Capital) Equivalents
|
|
DataField: fnd6_eventv110_nrtxtepsq
|
|
DataFieldDescription: Nonrecurring Income Taxes Basic EPS Effect
|
|
DataField: cash_st
|
|
DataFieldDescription: Cash and Short-Term Investments
|
|
DataField: fnd6_pstkc
|
|
DataFieldDescription: Preferred Stock - Convertible
|
|
DataField: fnd6_nopio
|
|
DataFieldDescription: Nonoperating Income (Expense) - Other
|
|
DataField: fnd6_newa2v1300_tstkn
|
|
DataFieldDescription: Treasury Stock - Number of Common Shares
|
|
DataField: equity
|
|
DataFieldDescription: Common/Ordinary Equity - Total
|
|
DataField: fnd6_newa2v1300_rect
|
|
DataFieldDescription: Receivables - Total
|
|
DataField: fnd6_newa1v1300_ao
|
|
DataFieldDescription: Assets - Other
|
|
DataField: fnd6_cptnewqv1300_saleq
|
|
DataFieldDescription: Sales/Turnover (Net)
|
|
DataField: fnd6_eventv110_pncd12
|
|
DataFieldDescription: Core Pension Adjustment Diluted EPS Effect 12MM
|
|
DataField: fnd6_newa2v1300_re
|
|
DataFieldDescription: Retained Earnings
|
|
DataField: fnd6_newa2v1300_spi
|
|
DataFieldDescription: Special Items
|
|
DataField: fnd6_cptnewqv1300_ltq
|
|
DataFieldDescription: Liabilities - Total
|
|
DataField: fnd6_drlt
|
|
DataFieldDescription: Deferred Revenue - Long-term
|
|
DataField: fnd6_mfma1_csho
|
|
DataFieldDescription: Common Shares Outstanding
|
|
DataField: fnd6_newqv1300_cogsq
|
|
DataFieldDescription: Cost of Goods Sold
|
|
DataField: fnd6_xintopt
|
|
DataFieldDescription: Implied Option Expense
|
|
DataField: fnd6_newqv1300_acomincq
|
|
DataFieldDescription: Accumulated Other Comprehensive Income (Loss)
|
|
DataField: fnd6_newqeventv110_txditcq
|
|
DataFieldDescription: Deferred Taxes and Investment Tax Credit
|
|
DataField: fnd6_newqeventv110_cshprq
|
|
DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - Basic
|
|
DataField: fnd6_newa1v1300_invt
|
|
DataFieldDescription: Inventories - Total
|
|
DataField: sales_growth
|
|
DataFieldDescription: Growth in Sales (Quarterly)
|
|
DataField: fnd6_newqeventv110_pncwiapq
|
|
DataFieldDescription: Core Pension w/o Interest Adjustment After-tax Preliminary
|
|
DataField: fnd6_newqeventv110_spceepspq
|
|
DataFieldDescription: S&P Core Earnings EPS Basic - Preliminary
|
|
DataField: fnd6_newa2v1300_pi
|
|
DataFieldDescription: Pretax Income
|
|
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_cuo1actualqfv110_actual
|
|
DataFieldDescription: Announced financial data
|
|
DataField: anl4_af_cfps_value
|
|
DataFieldDescription: Cash Flow Per Share - Actual Value
|
|
DataField: anl4_eaz1laf_person
|
|
DataFieldDescription: Broker Id
|
|
DataField: funds_from_operations_max_guidance
|
|
DataFieldDescription: The maximum guidance value for Funds from operation - annual
|
|
DataField: est_ebitda
|
|
DataFieldDescription: Earnings before interest, taxes, depreciation, and amortization - mean of estimations
|
|
DataField: anl4_ptp_high
|
|
DataFieldDescription: Pretax income - the highest estimation
|
|
DataField: anl4_dez1basicqfv4_preest
|
|
DataFieldDescription: The previous estimation of finanicial item
|
|
DataField: anl4_basicconafv110_pu
|
|
DataFieldDescription: The number of upper estimations
|
|
DataField: anl4_adxqfv110_down
|
|
DataFieldDescription: Number of lower estimations
|
|
DataField: anl4_bac1actualqfv110_item
|
|
DataFieldDescription: Financial item
|
|
DataField: anl4_fcf_low
|
|
DataFieldDescription: Free Cash Flow - The lowest estimation
|
|
DataField: min_free_cash_flow_per_share_guidance
|
|
DataFieldDescription: Free cash flow per share - minimum guidance value for the annual period
|
|
DataField: anl4_af_div_value
|
|
DataFieldDescription: Dividend - Actual value
|
|
DataField: min_net_income_guidance
|
|
DataFieldDescription: Net profit - minimum guidance value
|
|
DataField: anl4_cuo1detailqfv110_item
|
|
DataFieldDescription: Financial item
|
|
DataField: anl4_basicdetailqfv110_prevval
|
|
DataFieldDescription: The previous estimation of financial item
|
|
DataField: min_total_goodwill_guidance
|
|
DataFieldDescription: Total Goodwill - The lowest guidance value
|
|
DataField: sales_estimate_median_quarterly
|
|
DataFieldDescription: Sales - median of estimations
|
|
DataField: anl4_ads1detailqfv110_prevval
|
|
DataFieldDescription: The previous estimation of financial item
|
|
DataField: min_gross_income_guidance
|
|
DataFieldDescription: The minimum guidance value for Gross Income.
|
|
DataField: anl4_cuo1guidaf_item
|
|
DataFieldDescription: Financial item
|
|
DataField: anl4_bvps_value
|
|
DataFieldDescription: Book value per share - announced financial value
|
|
DataField: anl4_qfv4_cfps_high
|
|
DataFieldDescription: Cash Flow Per Share - The highest estimation for the quarter
|
|
DataField: maximum_guidance_value
|
|
DataFieldDescription: Maximum guidance value for basic annual financials
|
|
DataField: max_reported_eps_guidance_2
|
|
DataFieldDescription: Reported Earnings Per Share - Maximum guidance value for the annual period
|
|
DataField: anl4_dez1safv4_preest
|
|
DataFieldDescription: The previous estimation of finanicial item
|
|
DataField: anl4_cfo_median
|
|
DataFieldDescription: Cash Flow From Operations - median of estimations
|
|
DataField: anl4_netprofit_std
|
|
DataFieldDescription: Net profit - standard deviation of estimations
|
|
DataField: anl4_qf_az_eps_number
|
|
DataFieldDescription: Earnings per share - number of estimations
|
|
DataField: anl4_basicconafv110_high
|
|
DataFieldDescription: The highest estimation
|
|
DataField: pv13_revere_parent
|
|
DataFieldDescription: Code of parent sector
|
|
DataField: pv13_hierarchy_min5_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min51_f3_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_ustomergraphrank_hub_rank
|
|
DataFieldDescription: the HITS hub score of customers
|
|
DataField: pv13_h_min20_top3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_ustomergraphrank_auth_rank
|
|
DataFieldDescription: the HITS authority score of customers
|
|
DataField: rel_ret_cust
|
|
DataFieldDescription: averaged one-day-return of the instrument's customers
|
|
DataField: pv13_h_min10_top3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f3_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rcsed_6l
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_only_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy23_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h2_min2_1k_sector
|
|
DataFieldDescription: Grouping fields for top 1000
|
|
DataField: pv13_new_5l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_1k_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_key_sector_total
|
|
DataFieldDescription: Number of key focus sectors for the company
|
|
DataField: pv13_hierarchy_min10_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min2_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min2_focused_pureplay_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_region
|
|
DataFieldDescription: Unique code of the region
|
|
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: rel_ret_all
|
|
DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
|
|
DataField: pv13_rha2_min30_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min20_f3_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy2_min2_1k_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f1_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: parkinson_volatility_30
|
|
DataFieldDescription: Parkinson model's historical volatility over 30 days
|
|
DataField: implied_volatility_call_120
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
|
|
DataField: implied_volatility_call_1080
|
|
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
|
|
DataField: implied_volatility_put_360
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
|
|
DataField: implied_volatility_call_30
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
|
|
DataField: implied_volatility_put_90
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
|
|
DataField: implied_volatility_put_1080
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
|
|
DataField: historical_volatility_20
|
|
DataFieldDescription: Close-to-close Historical volatility over 20 days
|
|
DataField: implied_volatility_call_60
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
|
|
DataField: implied_volatility_mean_skew_90
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
|
|
DataField: historical_volatility_30
|
|
DataFieldDescription: Close-to-close Historical volatility over 30 days
|
|
DataField: historical_volatility_120
|
|
DataFieldDescription: Close-to-close Historical volatility over 120 days
|
|
DataField: parkinson_volatility_60
|
|
DataFieldDescription: Parkinson model's historical volatility over 60 days
|
|
DataField: parkinson_volatility_180
|
|
DataFieldDescription: Parkinson model's historical volatility over 180 days
|
|
DataField: implied_volatility_mean_150
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
|
|
DataField: implied_volatility_mean_skew_30
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
|
|
DataField: implied_volatility_mean_270
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
|
|
DataField: implied_volatility_put_150
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
|
|
DataField: implied_volatility_mean_skew_60
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
|
|
DataField: implied_volatility_mean_skew_10
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
|
|
DataField: implied_volatility_mean_skew_150
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
|
|
DataField: implied_volatility_call_90
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 90 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_30
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 30 days
|
|
DataField: implied_volatility_mean_120
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
|
|
DataField: implied_volatility_mean_90
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
|
|
DataField: implied_volatility_mean_skew_180
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
|
|
DataField: implied_volatility_mean_skew_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
|
|
DataField: parkinson_volatility_150
|
|
DataFieldDescription: Parkinson model's historical volatility over 150 days
|
|
DataField: nws12_afterhsz_60_min
|
|
DataFieldDescription: The percent change in price in the first 60 minutes following the news release
|
|
DataField: nws12_afterhsz_57l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
|
|
DataField: nws12_prez_1s
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
|
|
DataField: nws12_afterhsz_41rta
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DataFieldDescription: 14-day Average True Range
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DataField: nws12_prez_prevclose
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DataFieldDescription: Previous trading day's close price
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DataField: nws12_mainz_2l
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DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
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DataField: nws12_prez_short_interest
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DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
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DataField: nws12_prez_range
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DataFieldDescription: Session High Price - Session Low Price) / Session Low Price.
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DataField: nws12_afterhsz_result_vs_index
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DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
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DataField: nws12_mainz_newrecord
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DataFieldDescription: Tracks whether the news is first instance or a duplicate
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DataField: nws12_afterhsz_02l
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DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
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DataField: news_mins_2_pct_up
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DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
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DataField: news_pe_ratio
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DataFieldDescription: Reported price-to-earnings ratio for the calendar day of the session
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DataField: news_max_dn_ret
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DataFieldDescription: Percent change from the price at the time of the news to the after the news low
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DataField: nws12_prez_curr_vol
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DataFieldDescription: Current day's session volume
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DataField: nws12_afterhsz_120_min
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DataFieldDescription: The percent change in price in the first 120 minutes following the news release
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DataField: nws12_prez_57s
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DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
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DataField: news_mins_1_pct_up
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DataFieldDescription: Number of minutes that elapsed before price went up 1 percentage point
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DataField: nws12_mainz_prevclose
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DataFieldDescription: Previous trading day's close price
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DataField: news_dividend_yield
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DataFieldDescription: Annual yield
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DataField: news_prev_vol
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DataFieldDescription: Previous day's session volume
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DataField: nws12_prez_vol_ratio
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DataFieldDescription: Curr_Vol / Mov_Vol
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DataField: nws12_mainz_tonhigh
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DataFieldDescription: Highest price reached during the session before the time of news
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DataField: nws12_prez_1_minute
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DataFieldDescription: The percent change in price in the first minute following the news release
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DataField: nws12_afterhsz_tonhigh
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DataFieldDescription: Highest price reached during the session before the time of news
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DataField: nws12_afterhsz_result2
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DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
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DataField: news_session_range_pct
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DataFieldDescription: (Session High Price - Session Low Price) / Session Low Price.
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DataField: news_pct_5_min
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DataFieldDescription: The percent change in price in the first 5 minutes following the news release
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DataField: nws12_mainz_mktcap
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|
DataFieldDescription: Reported market capitalization for the calendar day of the session
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DataField: nws12_afterhsz_eodhigh
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DataFieldDescription: Highest price reached between the time of news and the end of the session
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DataField: top1000
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|
DataFieldDescription: 20140630
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DataField: top200
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|
DataFieldDescription: 20140630
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|
DataField: top3000
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|
DataFieldDescription: 20140630
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DataField: top500
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DataFieldDescription: 20140630
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|
DataField: topsp500
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DataFieldDescription: 20140630
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DataField: nws18_event_relevance
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DataFieldDescription: Relevance of the event to the story
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DataField: rp_css_society
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|
DataFieldDescription: Composite sentiment score of society-related news
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|
DataField: nws18_qep
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|
DataFieldDescription: News sentiment based on positive and negative words on global equity
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DataField: rp_css_labor
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|
DataFieldDescription: Composite sentiment score of labor issues news
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|
DataField: rp_css_partner
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|
DataFieldDescription: Composite sentiment score of partnership news
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|
DataField: rp_css_product
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|
DataFieldDescription: Composite sentiment score of product and service-related news
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|
DataField: rp_css_credit_ratings
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|
DataFieldDescription: Composite sentiment score of credit ratings news
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|
DataField: rp_css_marketing
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|
DataFieldDescription: Composite sentiment score of marketing news
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|
DataField: rp_ess_ptg
|
|
DataFieldDescription: Event sentiment score of price target news
|
|
DataField: rp_ess_business
|
|
DataFieldDescription: Event sentiment score of business-related news
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|
DataField: rp_nip_mna
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|
DataFieldDescription: News impact projection of mergers and acquisitions-related news
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|
DataField: rp_css_insider
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|
DataFieldDescription: Composite sentiment score of insider trading news
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|
DataField: rp_nip_ptg
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|
DataFieldDescription: News impact projection of price target news
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|
DataField: nws18_bam
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|
DataFieldDescription: News sentiment specializing in mergers and acquisitions
|
|
DataField: nws18_qcm
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|
DataFieldDescription: News sentiment of relevant news with high confidence
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|
DataField: nws18_event_similarity_days
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|
DataFieldDescription: Days since a similar event was detected
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|
DataField: rp_css_revenue
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|
DataFieldDescription: Composite sentiment score of revenue news
|
|
DataField: rp_ess_credit_ratings
|
|
DataFieldDescription: Event sentiment score of credit ratings news
|
|
DataField: rp_ess_legal
|
|
DataFieldDescription: Event sentiment score of legal news
|
|
DataField: rp_nip_equity
|
|
DataFieldDescription: News impact projection of equity action news
|
|
DataField: rp_css_assets
|
|
DataFieldDescription: Composite sentiment score of assets news
|
|
DataField: rp_css_ratings
|
|
DataFieldDescription: Composite sentiment score of analyst ratings-related 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_ess_revenue
|
|
DataFieldDescription: Event sentiment score of revenue news
|
|
DataField: rp_ess_technical
|
|
DataFieldDescription: Event sentiment score based on technical analysis
|
|
DataField: rp_nip_business
|
|
DataFieldDescription: News impact projection of business-related news
|
|
DataField: rp_css_credit
|
|
DataFieldDescription: Composite sentiment score of credit news
|
|
DataField: rp_css_inverstor
|
|
DataFieldDescription: Composite sentiment score of investor relations news
|
|
DataField: rp_nip_inverstor
|
|
DataFieldDescription: News impact projection of investor relations news
|
|
DataField: fnd2_a_frtandfixturesg
|
|
DataFieldDescription: Amount before accumulated depreciation of equipment commonly used in offices and stores that have no permanent connection to the structure of a building or utilities. Examples include, but are not limited to, desks, chairs, tables, and bookcases.
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|
DataField: fnd2_a_sbcpnargmpmtwstgm
|
|
DataFieldDescription: As of the balance sheet date, the number of shares into which fully vested and expected to vest stock options outstanding can be converted under the option plan.
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|
DataField: fn_business_combination_assets_aquired_goodwill_a
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|
DataFieldDescription: Business Combination, Portion of Purchase Price Allocated to Goodwill
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|
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.
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|
DataField: fnd2_dbplanepdfbnfpy5
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|
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
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|
DataField: fn_comp_options_grants_weighted_avg_a
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|
DataFieldDescription: Weighted average price at which grantees could have acquired the underlying shares with respect to stock options that were terminated.
|
|
DataField: fnd2_a_stkrpeprogramardamt
|
|
DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
|
|
DataField: fn_interest_paid_net_q
|
|
DataFieldDescription: Net interest
|
|
DataField: fn_comp_options_exercisable_number_q
|
|
DataFieldDescription: The number of shares into which fully or partially vested stock options outstanding as of the balance sheet date can be currently converted under the option plan.
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|
DataField: fn_def_tax_assets_liab_net_q
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|
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_ebitdm
|
|
DataFieldDescription: EBIT, Domestic
|
|
DataField: fnd2_dbplanepdfbnfpythree
|
|
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 3rd 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.
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|
DataField: fnd2_a_flintasamt1expyfour
|
|
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 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
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|
DataField: fn_liab_fair_val_q
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|
DataFieldDescription: Liabilities Fair Value, Recurring, Total
|
|
DataField: fnd2_q_flintasamt1expyfour
|
|
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 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fnd2_sbcpnshardpreops
|
|
DataFieldDescription: Share-based compensation shares authorized under stock option plans exercise price range number of exercisable options
|
|
DataField: fnd2_a_inventoryfinishedgoods
|
|
DataFieldDescription: Amount before valuation and LIFO reserves of completed merchandise or goods expected to be sold within 1 year or operating cycle, if longer.
|
|
DataField: fnd2_dbplanbnfol
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|
DataFieldDescription: 1) For defined benefit pension plans, the benefit obligation is the projected benefit obligation, which is the actuarial present value as of a date of all benefits attributed by the pension benefit formula to employee service rendered prior to that date. 2) For other postretirement defined benefit plans, the benefit obligation is the accumulated postretirement benefit obligation, which is the actuarial present value of benefits attributed to employee service rendered to a particular date.
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|
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: 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_flintasamt1expytwo
|
|
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 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_debt_instrument_interest_rate_stated_percentage_a
|
|
DataFieldDescription: Stated percentage of interest rate on debt
|
|
DataField: fnd2_propplteqmuflmblgland
|
|
DataFieldDescription: PPE, Buildings & land, Useful Life, Minimum
|
|
DataField: fnd2_a_sbcpnargmsawpfipwerpr
|
|
DataFieldDescription: Weighted average price of options that were either forfeited or expired.
|
|
DataField: fn_liab_fair_val_l2_a
|
|
DataFieldDescription: Liabilities Fair Value, Recurring, Level 2
|
|
DataField: fn_payments_for_repurchase_of_common_stock_a
|
|
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: fn_avg_diluted_sharesout_adj_a
|
|
DataFieldDescription: The sum of dilutive potential common shares or units used in the calculation of the diluted per-share or per-unit computation.
|
|
DataField: fn_employee_related_liab_q
|
|
DataFieldDescription: Total of the carrying values as of the balance sheet date of obligations incurred through that date and payable for obligations related to services received from employees, such as accrued salaries and bonuses, payroll taxes and fringe benefits. 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_debt_instrument_interest_rate_stated_percentage_q
|
|
DataFieldDescription: Stated percentage of interest rate on debt
|
|
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: 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
|
|
========================= 数据字段结束 =======================================
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