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898 lines
54 KiB
898 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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目标：识别价格趋势的强度、速度和持续性
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可用的具体构建方法：
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1. 简单动量：ts_delta(close, d) [d=5,10,20,30,60]
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2. 趋势斜率：ts_regression(close, ts_step(1), d, 0, 1) [rettype=1获取斜率]
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3. 动量加速度：ts_delta(ts_delta(close, d1), d2) [避免嵌套ts_regression]
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4. 平滑动量：ts_mean(returns, d) [returns=ts_delta(close,1)]
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5. 动量衰减：ts_decay_linear(returns, d)
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6. 价量关系：ts_corr(ts_delta(close,5), ts_delta(volume,5), d)
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建议组合：使用不同d参数创建短期/中期/长期动量
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维度2：横截面领导力（CL）
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目标：识别行业内的龙头股和相对强度
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具体构建方法：
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1. 龙头股筛选：if_else(rank(volume) > 0.7, 龙头值, 其他值) [使用volume代替market_cap]
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2. 龙头组合：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4")) [使用volume排序]
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3. 行业内离散度：ts_std_dev(group_rank(returns, industry), 20)
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4. 相对排名稳定性：ts_mean(rank(returns), d)
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维度3：市场状态适应性（MS）
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目标：根据波动率、趋势状态调整参数
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具体构建方法：
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1. 波动率调整：ts_delta(close,5) / ts_std_dev(returns,20)
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2. 状态条件选择：if_else(ts_rank(volatility,30) > 0.7, 短期动量, 长期动量)
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3. 参数动态化：if_else(ts_std_dev(returns,20) > 阈值, 5, 20) [作为d参数]
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4. 趋势状态识别：ts_rank(ts_mean(returns,20), 60) > 0.5
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基本结构：
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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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=== 关键语法规则（必须遵守） ===
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1. 数据字段规范：
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- 可使用字段：close, volume, returns
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- ❌ 错误：market_cap, marketcap, mkt_cap [这些字段不存在]
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- ✅ 正确：使用volume作为规模代理，close作为价格
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- returns通常定义为：ts_delta(close, 1) 或 close/ts_delay(close,1)-1
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2. ts_regression使用规范：
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- 避免深度嵌套ts_regression，特别是作为其他函数的参数
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- ✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
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- ❌ 错误：ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
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- 替代方案：用ts_delta组合计算动量变化
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3. if_else使用规范：
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- 条件必须是简单布尔表达式
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- 避免序列比较：❌ ts_std_dev(returns,60) > ts_mean(ts_std_dev(returns,60),120)
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- 正确使用：✅ if_else(ts_rank(ts_std_dev(returns,60), 120) > 0.7, 短期动量, 长期动量)
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4. bucket函数使用规范：
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- bucket()返回分组ID，可用于条件判断
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- ✅ 正确：bucket(rank(volume), range="0,3,0.4") == 0 [第一组为大成交量]
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- ✅ 正确：group_mean(x, 1, bucket(rank(volume), range="0,3,0.4"))
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- 注意字符串格式：range="起始值,组数,步长" 或 buckets="分割点列表"
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=== 关键语法规则结束 ===
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*=====*
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注意事项:
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1. 避免过度复杂的嵌套（建议不超过3层）
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2. 每个表达式应有明确的经济逻辑
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3. 考虑实际交易可行性（避免未来函数）
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4. 包含风险控制元素（如波动率调整）
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5. 只能使用可用的数据字段：close, volume, returns等
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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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构建框架指导（请按此逻辑创造新因子）：
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维度融合模板（选择至少2个）：
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A. 领导力动量 = 时序动量 × 横截面调整
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逻辑：大成交量股票的动量更强
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结构：group_mean(ts_delta(close, d1), 1, bucket(rank(volume), range="0,3,0.4"))
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B. 状态自适应动量 = 条件选择动量
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逻辑：高波动用短期动量，低波动用长期动量
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结构：if_else(ts_std_dev(returns,20) > 0.02, ts_delta(close,5), ts_delta(close,20))
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C. 行业传导因子 = 领先行业动量 × 相关性强度
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逻辑：与强势行业相关性高的行业未来表现好
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结构：multiply(ts_corr(group_mean(returns,1,industry), group_mean(returns,1,sector), d1), ts_delta(close,d2))
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D. 情绪反转 = 过度交易信号 × 基础趋势
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逻辑：过度交易时反转，趋势延续时跟随
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结构：multiply(reverse(ts_rank(volume/ts_mean(volume,20), 10)), ts_delta(close,20))
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关键组件库（可自由组合）：
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1. 动量类：ts_delta(close,{d}), ts_regression(close,ts_step(1),{d},0,1)
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2. 波动类：ts_std_dev(returns,{d}), ts_mean(abs(returns),{d})
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3. 成交量类：volume/ts_mean(volume,{d}), ts_zscore(volume,{d})
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4. 横截面类：if_else(rank(volume) > 阈值, 值1, 值2), bucket(rank(volume), range="0,3,0.4")
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5. 相关性类：ts_corr({x},{y},{d})
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6. 条件逻辑：if_else({condition}, {true_value}, {false_value})
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参数池：d ∈ [5,10,20,30,60,120], 阈值 ∈ [0.5,0.7,0.8]
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*=====*
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输出格式：
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输出必须是且仅是纯文本。
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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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===================== !!! 重点(输出方式) !!! =====================
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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。
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**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)：
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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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=================================================================
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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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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.
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========================= 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: call_breakeven_180
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DataFieldDescription: Price at which a stock's call options with expiration 180 days in the future break even based on its recent bid/ask mean.
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DataField: pcr_oi_150
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DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.
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DataField: option_breakeven_180
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DataFieldDescription: Price at which a stock's options with expiration 180 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: put_breakeven_360
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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.
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DataField: put_breakeven_1080
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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.
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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_oi_all
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DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
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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_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: option_breakeven_10
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DataFieldDescription: Price at which a stock's options with expiration 10 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: call_breakeven_360
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DataFieldDescription: Price at which a stock's call options with expiration 360 days in the future break even based on its recent bid/ask mean.
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DataField: pcr_oi_10
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DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
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DataField: pcr_vol_1080
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DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.
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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: call_breakeven_30
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DataFieldDescription: Price at which a stock's call options with expiration 30 days in the future break even based on its recent bid/ask mean.
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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: 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: 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: pcr_vol_150
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DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
|
|
DataField: pcr_oi_30
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 30 days in the future.
|
|
DataField: forward_price_10
|
|
DataFieldDescription: Forward price at 10 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_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: forward_price_60
|
|
DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: forward_price_270
|
|
DataFieldDescription: Forward price at 270 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_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: 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_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: fnd6_newa2v1300_opeps
|
|
DataFieldDescription: Earnings Per Share from Operations
|
|
DataField: fnd6_newqv1300_dilavq
|
|
DataFieldDescription: Dilution Available - Excluding Extraordinary Items
|
|
DataField: fnd6_newqeventv110_wdaq
|
|
DataFieldDescription: Writedowns After-tax
|
|
DataField: fnd6_mkvaltq
|
|
DataFieldDescription: Market Value - Total
|
|
DataField: fnd6_newqv1300_chq
|
|
DataFieldDescription: Cash
|
|
DataField: fnd6_esubc
|
|
DataFieldDescription: Equity in Net Loss - Earnings
|
|
DataField: fnd6_cptnewqeventv110_saleq
|
|
DataFieldDescription: Sales/Turnover (Net)
|
|
DataField: fnd6_newqeventv110_glaq
|
|
DataFieldDescription: Gain/Loss After-Tax
|
|
DataField: fnd6_cimii
|
|
DataFieldDescription: Comprehensive Income - Noncontrolling Interest
|
|
DataField: working_capital
|
|
DataFieldDescription: Working Capital (Balance Sheet)
|
|
DataField: fnd6_newa1v1300_ceq
|
|
DataFieldDescription: Common/Ordinary Equity - Total
|
|
DataField: fnd6_newqeventv110_dcomq
|
|
DataFieldDescription: Deferred Compensation
|
|
DataField: fnd6_cptnewqeventv110_epsfxq
|
|
DataFieldDescription: Earnings Per Share (Diluted) - Excluding Extraordinary items
|
|
DataField: fnd6_newqeventv110_tfvlq
|
|
DataFieldDescription: Total Fair Value Liabilities
|
|
DataField: fnd6_txds
|
|
DataFieldDescription: Deferred Taxes - State
|
|
DataField: fnd6_newqeventv110_esoprq
|
|
DataFieldDescription: Preferred ESOP Obligation - Redeemable
|
|
DataField: fnd6_newqv1300_drcq
|
|
DataFieldDescription: Deferred Revenue - Current
|
|
DataField: fnd6_newa1v1300_aociother
|
|
DataFieldDescription: Accum Other Comp Inc - Other Adjustments
|
|
DataField: fnd6_fatp
|
|
DataFieldDescription: Plant, Property and Equipment at Cost - Land & Improvements
|
|
DataField: fnd6_newqv1300_rcpq
|
|
DataFieldDescription: Restructuring Cost Pretax
|
|
DataField: fnd6_intan
|
|
DataFieldDescription: Intangible Assets - Total
|
|
DataField: cashflow_fin
|
|
DataFieldDescription: Financing Activities - Net Cash Flow
|
|
DataField: fnd6_newa1v1300_apalch
|
|
DataFieldDescription: Accounts Payable and Accrued Liabilities - Increase/(Decrease)
|
|
DataField: fnd6_prcl
|
|
DataFieldDescription: Price Low - Annual
|
|
DataField: fnd6_newqv1300_altoq
|
|
DataFieldDescription: Other Long-term Assets
|
|
DataField: fnd6_newqeventv110_ibq
|
|
DataFieldDescription: Income Before Extraordinary Items
|
|
DataField: cogs
|
|
DataFieldDescription: Cost of Goods Sold
|
|
DataField: fnd6_newa1v1300_gdwl
|
|
DataFieldDescription: Goodwill
|
|
DataField: fnd6_newa1v1300_capx
|
|
DataFieldDescription: Capital Expenditures
|
|
DataField: fnd6_newqeventv110_pncwidpq
|
|
DataFieldDescription: Core Pension w/o Interest Adjustment Diluted EPS Effect Preliminary
|
|
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: dividend_estimate_value
|
|
DataFieldDescription: Dividend per share - estimated value
|
|
DataField: anl4_fcfps_low
|
|
DataFieldDescription: Free Cash Flow Per Share - the lowest estimation
|
|
DataField: anl4_ady_down
|
|
DataFieldDescription: Number of lower estimations
|
|
DataField: anl4_eaz1lqfv110_person
|
|
DataFieldDescription: Broker Id
|
|
DataField: anl4_bvps_value
|
|
DataFieldDescription: Book value per share - announced financial value
|
|
DataField: anl4_dez1basicqfv4_preest
|
|
DataFieldDescription: The previous estimation of finanicial item
|
|
DataField: actual_cashflow_per_share_value_quarterly
|
|
DataFieldDescription: Cash Flow Per Share - actual value for the quarter
|
|
DataField: sales_estimate_median_value
|
|
DataFieldDescription: Sales - Median value among forecasts
|
|
DataField: anl4_ads1detailafv110_estvalue
|
|
DataFieldDescription: Estimation value
|
|
DataField: sales_guidance_value_quarterly
|
|
DataFieldDescription: Sales - guidance value
|
|
DataField: anl4_adjusted_netincome_ft
|
|
DataFieldDescription: Adjusted net income - forecast type (revision/new/...)
|
|
DataField: anl4_ebit_std
|
|
DataFieldDescription: Earnings before interest and taxes - standard deviation of estimations
|
|
DataField: anl4_qfv4_div_median
|
|
DataFieldDescription: Dividend per share - median of estimations
|
|
DataField: sales_estimate_minimum_quarterly
|
|
DataFieldDescription: Sales - The lowest estimation
|
|
DataField: max_tangible_book_value_per_share_guidance
|
|
DataFieldDescription: Tangible Book Value per Share - maximum guidance value
|
|
DataField: anl4_epsr_mean
|
|
DataFieldDescription: GAAP Earnings per share - mean of estimations
|
|
DataField: anl4_qfv4_median_eps
|
|
DataFieldDescription: Earnings per share - median of estimations
|
|
DataField: max_adjusted_eps_guidance_2
|
|
DataFieldDescription: The maximum guidance value for adjusted earnings per share on an annual basis.
|
|
DataField: earnings_per_share_median_value
|
|
DataFieldDescription: Earnings per share - median of estimations
|
|
DataField: min_net_debt_guidance
|
|
DataFieldDescription: The minimum guidance value for Net Debt on an annual basis.
|
|
DataField: anl4_qf_az_wol_spe
|
|
DataFieldDescription: Earnings per share - The lowest estimation
|
|
DataField: max_adjusted_net_profit_guidance
|
|
DataFieldDescription: The maximum guidance value for adjusted net profit on an annual basis.
|
|
DataField: anl4_netprofit_high
|
|
DataFieldDescription: Net Profit - The highest estimation
|
|
DataField: anl4_totgw_high
|
|
DataFieldDescription: Total Goodwill - The highest estimation
|
|
DataField: minimum_guidance_value
|
|
DataFieldDescription: Minimum guidance value for basic annual financials
|
|
DataField: anl4_medianepsbfam
|
|
DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - median of estimations
|
|
DataField: net_debt_min_guidance_qtr
|
|
DataFieldDescription: Minimum guidance value for Net Debt
|
|
DataField: shareholders_equity_actual_value
|
|
DataFieldDescription: Shareholders' Equity - Total Value
|
|
DataField: eps_adjusted_min_guidance_value
|
|
DataFieldDescription: The minimum guidance value for adjusted earnings per share excluding extraordinary items and stock option expenses on an annual basis.
|
|
DataField: shareholders_equity_min_guidance
|
|
DataFieldDescription: Minimum guidance value for Share Equity
|
|
DataField: pv13_r2_liquid_min10_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_pureplay_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min20_3k_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f4_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min30_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_pureplay_only_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_6l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min51_f3_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min5_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min50_f3_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f4_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_f1_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_r2_liquid_min2_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_1l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min5_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_city
|
|
DataFieldDescription: City code
|
|
DataField: pv13_hierarchy_min51_f1_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min5_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_f3_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_new_5l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_r2_min5_1000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min5_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: rel_num_cust
|
|
DataFieldDescription: number of the instrument's customers
|
|
DataField: pv13_new_4l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min10_2k_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min40_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min5_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min10_all_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: historical_volatility_60
|
|
DataFieldDescription: Close-to-close Historical volatility over 60 days
|
|
DataField: implied_volatility_mean_skew_270
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
|
|
DataField: implied_volatility_call_720
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
|
|
DataField: implied_volatility_put_60
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
|
|
DataField: implied_volatility_put_90
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
|
|
DataField: implied_volatility_mean_skew_30
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
|
|
DataField: implied_volatility_mean_skew_120
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
|
|
DataField: implied_volatility_put_180
|
|
DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
|
|
DataField: implied_volatility_call_30
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
|
|
DataField: implied_volatility_mean_skew_90
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
|
|
DataField: implied_volatility_mean_20
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 20 days
|
|
DataField: historical_volatility_180
|
|
DataFieldDescription: Close-to-close Historical volatility over 180 days
|
|
DataField: implied_volatility_call_10
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
|
|
DataField: implied_volatility_mean_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
|
|
DataField: historical_volatility_20
|
|
DataFieldDescription: Close-to-close Historical volatility over 20 days
|
|
DataField: implied_volatility_call_90
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option 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_1080
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
|
|
DataField: historical_volatility_120
|
|
DataFieldDescription: Close-to-close Historical volatility over 120 days
|
|
DataField: implied_volatility_mean_720
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
|
|
DataField: implied_volatility_call_180
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
|
|
DataField: implied_volatility_put_150
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
|
|
DataField: parkinson_volatility_150
|
|
DataFieldDescription: Parkinson model's historical volatility over 150 days
|
|
DataField: implied_volatility_call_360
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
|
|
DataField: historical_volatility_90
|
|
DataFieldDescription: Close-to-close Historical volatility over 90 days
|
|
DataField: implied_volatility_mean_skew_720
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
|
|
DataField: parkinson_volatility_90
|
|
DataFieldDescription: Parkinson model's historical volatility over 90 days
|
|
DataField: parkinson_volatility_20
|
|
DataFieldDescription: Parkinson model's historical volatility over 20 days
|
|
DataField: implied_volatility_call_150
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
|
|
DataField: parkinson_volatility_30
|
|
DataFieldDescription: Parkinson model's historical volatility over 30 days
|
|
DataField: news_ratio_vol
|
|
DataFieldDescription: Curr_Vol / Mov_Vol
|
|
DataField: news_session_range
|
|
DataFieldDescription: Session High Price - Session Low Price
|
|
DataField: nws12_mainz_mainvwap
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DataFieldDescription: Main session volume weighted average price
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DataField: nws12_afterhsz_tonlow
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DataFieldDescription: Lowest price reached during the session before the time of the news
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DataField: nws12_prez_opengap
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DataFieldDescription: (DayOpen - PrevClose) / PrevClose.
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DataField: nws12_afterhsz_curr_vol
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DataFieldDescription: Current day's session volume
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DataField: nws12_prez_01p
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DataFieldDescription: The minimum of L or S above for 10-minute bucket
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DataField: nws12_prez_tonlow
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DataFieldDescription: Lowest price reached during the session before the time of the news
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DataField: nws12_afterhsz_90_min
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DataFieldDescription: The percent change in price in the first 90 minutes following the news release
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DataField: nws12_afterhsz_1p
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DataFieldDescription: The minimum of L or S above for 1-minute bucket
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DataField: nws12_afterhsz_maxdown
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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: news_ls
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DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS= 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
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DataField: nws12_mainz_curr_vol
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DataFieldDescription: Current day's session volume
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DataField: news_pct_30sec
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DataFieldDescription: The percent change in price in the 30 seconds following the news release
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DataField: nws12_afterhsz_4s
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DataFieldDescription: Number of minutes that elapsed before price went down 4 percentage points
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DataField: nws12_mainz_prevwap
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DataFieldDescription: Pre session volume weighted average price
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DataField: nws12_prez_eodvwap
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DataFieldDescription: Volume-weighted average price between the time of news and the end of the session
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DataField: news_mins_20_pct_up
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DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
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DataField: nws12_afterhsz_volstddev
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DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
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DataField: nws12_afterhsz_5p
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DataFieldDescription: The minimum of L or S above for 5-minute bucket
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DataField: nws12_prez_prevclose
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DataFieldDescription: Previous trading day's close price
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DataField: news_mins_10_pct_dn
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DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
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DataField: nws12_mainz_5s
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|
DataFieldDescription: Number of minutes that elapsed before price went down 5 percentage points
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DataField: news_spy_close
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|
DataFieldDescription: Price of SPY at close of session
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DataField: news_eod_vwap
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|
DataFieldDescription: Volume weighted average price between the time of news and the end of the session
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|
DataField: nws12_afterhsz_3s
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|
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
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|
DataField: nws12_afterhsz_01s
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|
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
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|
DataField: nws12_prez_volstddev
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|
DataFieldDescription: (CurrentVolume - AvgVol)/VolStDev, where AvgVol is the average of the daily volume, and VolStdDev is one standard deviation for the daily volume, both for 30 calendar days
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|
DataField: nws12_prez_57p
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|
DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
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DataField: nws12_mainz_02s
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|
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
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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: rp_ess_credit
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|
DataFieldDescription: Event sentiment score of credit news
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|
DataField: rp_ess_dividends
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|
DataFieldDescription: Event sentiment score of dividends news
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|
DataField: rp_css_revenue
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|
DataFieldDescription: Composite sentiment score of revenue news
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|
DataField: rp_nip_product
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|
DataFieldDescription: News impact projection of product and service-related news
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|
DataField: rp_css_assets
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|
DataFieldDescription: Composite sentiment score of assets news
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|
DataField: nws18_relevance
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|
DataFieldDescription: Relevance of news to the company
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|
DataField: rp_nip_insider
|
|
DataFieldDescription: News impact projection of insider trading news
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|
DataField: nws18_ghc_lna
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|
DataFieldDescription: Change in analyst recommendation
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|
DataField: rp_css_mna
|
|
DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
|
|
DataField: rp_ess_price
|
|
DataFieldDescription: Event sentiment score of stock price news
|
|
DataField: rp_css_technical
|
|
DataFieldDescription: Composite sentiment score based on technical analysis
|
|
DataField: rp_nip_credit
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|
DataFieldDescription: News impact projection of credit 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_nip_dividends
|
|
DataFieldDescription: News impact projection of dividends news
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|
DataField: rp_nip_technical
|
|
DataFieldDescription: News impact projection based on technical analysis
|
|
DataField: rp_css_price
|
|
DataFieldDescription: Composite sentiment score of stock price news
|
|
DataField: rp_css_credit_ratings
|
|
DataFieldDescription: Composite sentiment score of credit ratings news
|
|
DataField: rp_nip_mna
|
|
DataFieldDescription: News impact projection of mergers and acquisitions-related news
|
|
DataField: rp_css_inverstor
|
|
DataFieldDescription: Composite sentiment score of investor relations news
|
|
DataField: rp_css_ptg
|
|
DataFieldDescription: Composite sentiment score of price target news
|
|
DataField: rp_css_marketing
|
|
DataFieldDescription: Composite sentiment score of marketing news
|
|
DataField: nws18_sse
|
|
DataFieldDescription: Sentiment of phrases impacting the company
|
|
DataField: rp_nip_equity
|
|
DataFieldDescription: News impact projection of equity action news
|
|
DataField: rp_ess_society
|
|
DataFieldDescription: Event sentiment score of society-related news
|
|
DataField: rp_nip_credit_ratings
|
|
DataFieldDescription: News impact projection of credit ratings news
|
|
DataField: rp_ess_earnings
|
|
DataFieldDescription: Event sentiment score of earnings news
|
|
DataField: rp_ess_business
|
|
DataFieldDescription: Event sentiment score of business-related news
|
|
DataField: rp_css_society
|
|
DataFieldDescription: Composite sentiment score of society-related news
|
|
DataField: rp_nip_labor
|
|
DataFieldDescription: News impact projection of labor issues news
|
|
DataField: rp_css_legal
|
|
DataFieldDescription: Composite sentiment score of legal news
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|
DataField: fn_line_of_credit_facility_amount_out_q
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|
DataFieldDescription: Amount borrowed under the credit facility as of the balance sheet date.
|
|
DataField: fn_comp_fair_value_assumptions_weighted_avg_vol_rate_a
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|
DataFieldDescription: Weighted average expected volatility rate of share-based compensation awards.
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|
DataField: fn_accum_oth_income_loss_net_of_tax_q
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|
DataFieldDescription: Accumulated change in equity from transactions and other events and circumstances from non-owner sources, net of tax effect, at period end. Excludes Net Income (Loss), and accumulated changes in equity from transactions resulting from investments by owners and distributions to owners. Includes foreign currency translation items, certain pension adjustments, unrealized gains and losses on certain investments in debt and equity securities, other than temporary impairment (OTTI) losses related to factors other than credit losses on available-for-sale and held-to-maturity debt securities that an entity does not intend to sell and it is not more likely than not that the entity will be required to sell before recovery of the amortized cost basis, as well as changes in the fair value of derivatives related to the effective portion of a designated cash flow hedge.
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|
DataField: fn_business_combination_purchase_price_q
|
|
DataFieldDescription: Business Combination, Purchase Price
|
|
DataField: fn_oth_comp_forfeitures_fair_value_a
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|
DataFieldDescription: Annual Share Based Compensation Equity Instruments Other Than Options Forfeitures Weighted Average Grant Date Fair Value
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|
DataField: fn_repayments_of_lt_debt_a
|
|
DataFieldDescription: The cash outflow for debt initially having maturity due after 1 year or beyond the normal operating cycle, if longer.
|
|
DataField: fn_business_combination_purchase_price_a
|
|
DataFieldDescription: Business Combination, Purchase Price
|
|
DataField: fn_unrecognized_tax_benefits_a
|
|
DataFieldDescription: Amount of unrecognized tax benefits.
|
|
DataField: fn_employee_related_liab_a
|
|
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).
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DataField: fn_finite_lived_intangible_assets_net_a
|
|
DataFieldDescription: Finite Lived Intangible Assets, Net
|
|
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_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: fn_accum_depr_depletion_and_amortization_ppne_q
|
|
DataFieldDescription: Amount of accumulated depreciation, depletion and amortization for physical assets used in the normal conduct of business to produce goods and services.
|
|
DataField: fnd2_a_acmopclcchngcfectnt
|
|
DataFieldDescription: Accumulated change, net of tax, in accumulated gains and losses from derivative instruments designated and qualifying as the effective portion of cash flow hedges. Includes an entity's share of an equity investee's Increase or Decrease in deferred hedging gains or losses.
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|
DataField: fn_allocated_share_based_compensation_expense_q
|
|
DataFieldDescription: Represents the expense recognized during the period arising from equity-based compensation arrangements (for example, shares of stock, units, stock options, or other equity instruments) with employees, directors, and certain consultants qualifying for treatment as employees.
|
|
DataField: fn_incremental_shares_attributable_to_share_based_payment_q
|
|
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: fn_income_from_equity_investments_q
|
|
DataFieldDescription: Income From Equity Method Investments
|
|
DataField: fn_income_taxes_paid_a
|
|
DataFieldDescription: The amount of cash paid during the current period to foreign, federal, state, and local authorities as taxes on income.
|
|
DataField: fnd2_oprlsfmpdcurr
|
|
DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
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|
DataField: fn_allocated_share_based_compensation_expense_a
|
|
DataFieldDescription: Represents the expense recognized during the period arising from equity-based compensation arrangements (for example, shares of stock, unit, stock options or other equity instruments) with employees, directors and certain consultants qualifying for treatment as employees.
|
|
DataField: fn_repurchased_shares_value_a
|
|
DataFieldDescription: Shares repurchased and either retired or put into treasury stock, likely as part of a share buyback plan.
|
|
DataField: fn_line_of_credit_facility_amount_out_a
|
|
DataFieldDescription: Amount borrowed under the credit facility as of the balance sheet date.
|
|
DataField: fn_liab_fair_val_l3_a
|
|
DataFieldDescription: Liabilities Fair Value, Recurring, Level 3
|
|
DataField: fn_comp_options_grants_fair_value_a
|
|
DataFieldDescription: Annual Share-Based Compensation Arrangement by Share-Based Payment Award Options Grants in Period Weighted Average Grant Date Fair Value
|
|
DataField: fnd2_dfdtxasoprlcarryfwd
|
|
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible operating loss carryforwards.
|
|
DataField: fnd2_q_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: fn_comp_options_exercises_weighted_avg_a
|
|
DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
|
|
DataField: fnd2_a_inventoryrawmaterials
|
|
DataFieldDescription: Amount before valuation and LIFO reserves of raw materials expected to be sold, or consumed within 1 year or operating cycle, if longer.
|
|
DataField: fn_debt_instrument_interest_rate_stated_percentage_q
|
|
DataFieldDescription: Stated percentage of interest rate on debt
|
|
DataField: fn_eff_income_tax_rate_continuing_operations_q
|
|
DataFieldDescription: Percentage of current income tax expense (benefit) and deferred income tax expense (benefit) pertaining to continuing operations.
|
|
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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