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898 lines
52 KiB
898 lines
52 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: forward_price_10
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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.
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DataField: option_breakeven_360
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DataFieldDescription: Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.
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DataField: call_breakeven_150
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DataFieldDescription: Price at which a stock's call options with expiration 150 days in the future break even based on its recent bid/ask mean.
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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: option_breakeven_150
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DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
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DataField: pcr_oi_60
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DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 60 days in the future.
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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: 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: pcr_oi_720
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DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 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.
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DataField: pcr_vol_10
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DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
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DataField: option_breakeven_120
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DataFieldDescription: Price at which a stock's options with expiration 120 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: 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: 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: 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_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: pcr_vol_720
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DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 720 days in the future.
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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: put_breakeven_20
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DataFieldDescription: Price at which a stock's put options with expiration 20 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: pcr_vol_180
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
|
|
DataField: forward_price_1080
|
|
DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: pcr_oi_360
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 360 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: pcr_oi_10
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
|
|
DataField: forward_price_20
|
|
DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: pcr_vol_all
|
|
DataFieldDescription: Ratio of put volume to call volume for all maturities on stock's options.
|
|
DataField: fnd6_rea
|
|
DataFieldDescription: Retained Earnings - Restatement
|
|
DataField: fnd6_newa2v1300_ppegt
|
|
DataFieldDescription: Property, Plant and Equipment - Total (Gross)
|
|
DataField: fnd6_newqv1300_mibtq
|
|
DataFieldDescription: Noncontrolling Interests - Total - Balance Sheet - Quarterly
|
|
DataField: ebit
|
|
DataFieldDescription: Earnings Before Interest and Taxes
|
|
DataField: fnd6_newqeventv110_ppegtq
|
|
DataFieldDescription: Property, Plant and Equipment - Total (Gross) - Quarterly
|
|
DataField: fnd6_newqeventv110_gdwlipq
|
|
DataFieldDescription: Impairment of Goodwill Pretax
|
|
DataField: fnd6_newqeventv110_spceepsq
|
|
DataFieldDescription: S&P Core Earnings EPS Basic
|
|
DataField: fnd6_newqeventv110_glcedq
|
|
DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS
|
|
DataField: fnd6_newqeventv110_spceeps12
|
|
DataFieldDescription: S&P Core Earnings EPS Basic 12MM
|
|
DataField: fnd6_txdfed
|
|
DataFieldDescription: Deferred Taxes - Federal
|
|
DataField: fnd6_fatb
|
|
DataFieldDescription: Plant, Property and Equipment at Cost - Buildings
|
|
DataField: fnd6_newa1v1300_dp
|
|
DataFieldDescription: Depreciation and Amortization
|
|
DataField: fnd6_newa2v1300_prsho
|
|
DataFieldDescription: Redeem Pfd Shares Outs (000)
|
|
DataField: fnd6_newqv1300_aol2q
|
|
DataFieldDescription: Assets Level 2 (Observable)
|
|
DataField: fnd6_mfma1_dpc
|
|
DataFieldDescription: Depreciation and Amortization (Cash Flow)
|
|
DataField: fnd6_ptis
|
|
DataFieldDescription: Pretax Income
|
|
DataField: fnd6_cptnewqv1300_ceqq
|
|
DataFieldDescription: Common/Ordinary Equity - Total
|
|
DataField: fnd6_newqv1300_cogsq
|
|
DataFieldDescription: Cost of Goods Sold
|
|
DataField: fnd6_newa1v1300_dltt
|
|
DataFieldDescription: Long-Term Debt - Total
|
|
DataField: fnd6_newqv1300_invrmq
|
|
DataFieldDescription: Inventory - Raw Materials
|
|
DataField: fnd6_newqeventv110_pncq
|
|
DataFieldDescription: Core Pension Adjustment
|
|
DataField: fnd6_txtubtxtr
|
|
DataFieldDescription: Impact on Effective Tax Rate
|
|
DataField: fnd6_newa1v1300_dcom
|
|
DataFieldDescription: Deferred Compensation
|
|
DataField: fnd6_newa1v1300_ebit
|
|
DataFieldDescription: Earnings Before Interest and Taxes
|
|
DataField: fnd6_dd5
|
|
DataFieldDescription: Debt Due in 5th Year
|
|
DataField: fnd6_newqv1300_cshfdq
|
|
DataFieldDescription: Common Shares for Diluted EPS
|
|
DataField: fnd6_newa1v1300_dv
|
|
DataFieldDescription: Cash Dividends (Cash Flow)
|
|
DataField: cash
|
|
DataFieldDescription: Cash
|
|
DataField: fnd6_newqeventv110_seteps12
|
|
DataFieldDescription: Settlement (Litigation/Insurance) Basic EPS Effect 12MM
|
|
DataField: fnd6_mfma2_opeps
|
|
DataFieldDescription: Earnings Per Share from Operations
|
|
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: 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: anl4_fsguidanceafv4_minguidance
|
|
DataFieldDescription: Min guidance value
|
|
DataField: anl4_afv4_eps_high
|
|
DataFieldDescription: Earnings per share - The highest estimation
|
|
DataField: anl4_basicdetailqfv110_prevval
|
|
DataFieldDescription: The previous estimation of financial item
|
|
DataField: anl4_basicconltv110_high
|
|
DataFieldDescription: The highest estimation
|
|
DataField: dividend_previous_estimate_value
|
|
DataFieldDescription: The previous estimation of dividend
|
|
DataField: anl4_bac1conafv110_item
|
|
DataFieldDescription: Financial item
|
|
DataField: anl4_fsguidanceafv4_maxguidance
|
|
DataFieldDescription: Maximum guidance value
|
|
DataField: anl4_eaz2lafv110_prevval
|
|
DataFieldDescription: The previous estimation of financial item
|
|
DataField: anl4_fsdetailltv4v104_item
|
|
DataFieldDescription: Financial item
|
|
DataField: selling_general_admin_expense_reported_value
|
|
DataFieldDescription: Selling, General & Administrative Expense value
|
|
DataField: max_free_cashflow_guidance
|
|
DataFieldDescription: The maximum guidance value for Free Cash Flow.
|
|
DataField: anl4_cfo_value
|
|
DataFieldDescription: Cash Flow From Operations - announced financial value
|
|
DataField: anl4_fsdtlestmtbscv104_item
|
|
DataFieldDescription: Financial item
|
|
DataField: min_total_goodwill_guidance
|
|
DataFieldDescription: Total Goodwill - The lowest guidance value
|
|
DataField: anl4_qfd1_az_wol_spfc
|
|
DataFieldDescription: Cash Flow Per Share - The lowest estimation
|
|
DataField: eps_reported_min_guidance_qtr
|
|
DataFieldDescription: Reported Earnings Per Share - Minimum guidance value
|
|
DataField: anl4_gric_value
|
|
DataFieldDescription: Gross income- announced financial value
|
|
DataField: anl4_detailltv4_est
|
|
DataFieldDescription: Long term estimation value
|
|
DataField: max_pretax_profit_guidance
|
|
DataFieldDescription: The maximum guidance value for Pretax income on an annual basis.
|
|
DataField: anl4_fsguidancebasicqfv4_item
|
|
DataFieldDescription: Financial item
|
|
DataField: anl4_afv4_div_std
|
|
DataFieldDescription: Dividend per share - standard deviation of estimations
|
|
DataField: cashflow_per_share_median_value
|
|
DataFieldDescription: Cash Flow Per Share - Median value among forecasts
|
|
DataField: anl4_dei3lafv110_item
|
|
DataFieldDescription: Financial item
|
|
DataField: anl4_ady_high
|
|
DataFieldDescription: The highest estimation
|
|
DataField: anl4_epsa_flag
|
|
DataFieldDescription: Earnings per share adjusted by excluding extraordinary items and stock option expenses - forecast type (revision/new/...)
|
|
DataField: max_share_buyback_guidance
|
|
DataFieldDescription: Maximum guidance value for Shares Basic - Annual
|
|
DataField: anl4_netdebt_flag
|
|
DataFieldDescription: Net debt - forecast type (revision/new/...)
|
|
DataField: anl4_qfd1_az_cfps_median
|
|
DataFieldDescription: Cash Flow Per Share - Median value among forecasts
|
|
DataField: anl4_qfv4_minguidance
|
|
DataFieldDescription: Min guidance value
|
|
DataField: rel_ret_comp
|
|
DataFieldDescription: Averaged one-day return of the competing companies
|
|
DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min40_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min54_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_pureplay_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchys32_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f1_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_3l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_2k_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min10_top3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_2l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min25_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min5_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_company_total
|
|
DataFieldDescription: Total number of companies in the sector
|
|
DataField: pv13_rha2_min2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_term_sector_total
|
|
DataFieldDescription: Number of terminal sectors for the company
|
|
DataField: pv13_hierarchy23_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_custretsig_retsig
|
|
DataFieldDescription: Sign of customer return
|
|
DataField: pv13_hierarchy_min20_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_term
|
|
DataFieldDescription: Indicates when a sector is the terminal sector (i.e., no sub-sectors)
|
|
DataField: pv13_hierarchy_min100_corr21_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: rel_ret_all
|
|
DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
|
|
DataField: pv13_h_min2_focused_pureplay_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_f4_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_pureplay_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min30_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min5_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min2_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min51_f4_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: implied_volatility_mean_skew_720
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
|
|
DataField: implied_volatility_mean_skew_20
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
|
|
DataField: implied_volatility_mean_720
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
|
|
DataField: implied_volatility_call_1080
|
|
DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
|
|
DataField: implied_volatility_call_30
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
|
|
DataField: implied_volatility_call_10
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
|
|
DataField: implied_volatility_call_720
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
|
|
DataField: parkinson_volatility_120
|
|
DataFieldDescription: Parkinson model's historical volatility over 120 days
|
|
DataField: implied_volatility_put_120
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
|
|
DataField: historical_volatility_20
|
|
DataFieldDescription: Close-to-close Historical volatility over 20 days
|
|
DataField: parkinson_volatility_60
|
|
DataFieldDescription: Parkinson model's historical volatility over 60 days
|
|
DataField: implied_volatility_call_150
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
|
|
DataField: implied_volatility_put_270
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
|
|
DataField: implied_volatility_mean_120
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
|
|
DataField: implied_volatility_mean_10
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 10 days
|
|
DataField: implied_volatility_put_90
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
|
|
DataField: implied_volatility_mean_270
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
|
|
DataField: implied_volatility_mean_skew_1080
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 3 years
|
|
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: historical_volatility_150
|
|
DataFieldDescription: Close-to-close Historical volatility over 150 days
|
|
DataField: implied_volatility_mean_skew_120
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
|
|
DataField: parkinson_volatility_10
|
|
DataFieldDescription: Parkinson model's historical volatility over 2 weeks
|
|
DataField: implied_volatility_mean_skew_150
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
|
|
DataField: parkinson_volatility_90
|
|
DataFieldDescription: Parkinson model's historical volatility over 90 days
|
|
DataField: implied_volatility_mean_skew_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
|
|
DataField: historical_volatility_180
|
|
DataFieldDescription: Close-to-close Historical volatility over 180 days
|
|
DataField: implied_volatility_call_180
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
|
|
DataField: implied_volatility_put_360
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
|
|
DataField: implied_volatility_mean_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
|
|
DataField: news_indx_perf
|
|
DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
|
|
DataField: nws12_afterhsz_3p
|
|
DataFieldDescription: The minimum of L or S above for 3-minute bucket
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DataField: news_prev_day_ret
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DataFieldDescription: Percent change between the previous day's open and close
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DataField: nws12_mainz_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_57s
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DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
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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_afterhsz_41rta
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DataFieldDescription: 14-day Average True Range
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DataField: nws12_afterhsz_mov_vol
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DataFieldDescription: 30-day moving average session volume
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DataField: nws12_allz_newssess
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DataFieldDescription: Index of session in which the news was reported
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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: news_eps_actual
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DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
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DataField: news_mins_3_chg
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DataFieldDescription: The minimum of L or S above for 3-minute bucket
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DataField: news_mins_7_5_pct_up
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DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
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DataField: nws12_prez_maxdnamt
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DataFieldDescription: The price at the time of the news minus the after the news low
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DataField: nws12_afterhsz_maxdnamt
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DataFieldDescription: The price at the time of the news minus the after the news low
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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: nws12_prez_dayopen
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DataFieldDescription: Price at the session open
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DataField: news_mins_5_chg
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DataFieldDescription: The minimum of L or S above for 5-minute bucket
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DataField: nws12_mainz_30_min
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DataFieldDescription: The percent change in price in the first 30 minutes following the news release
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DataField: nws12_mainz_peratio
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|
DataFieldDescription: Reported price-to-earnings ratio for the calendar day of the session
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DataField: news_pct_10min
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|
DataFieldDescription: The percent change in price in the first 10 minutes following the news release
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DataField: news_mins_5_pct_up
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|
DataFieldDescription: Number of minutes that elapsed before price went up 5 percentage points
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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_prez_eodlow
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|
DataFieldDescription: Lowest price reached between the time of news and the end of the session.
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DataField: nws12_mainz_prevday
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|
DataFieldDescription: Percent change between the previous day's open and close
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|
DataField: nws12_prez_02p
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|
DataFieldDescription: The minimum of L or S above for 20-minute bucket
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|
DataField: nws12_afterhsz_div_y
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|
DataFieldDescription: Annual yield
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|
DataField: nws12_afterhsz_lowexcstddev
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|
DataFieldDescription: (TONLast - EODLow) / StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
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|
DataField: nws12_afterhsz_1s
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|
DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
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|
DataField: news_mins_4_chg
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|
DataFieldDescription: The minimum of L or S above for 4-minute bucket
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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
|
|
DataFieldDescription: 20140630
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|
DataField: topsp500
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|
DataFieldDescription: 20140630
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|
DataField: rp_nip_assets
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|
DataFieldDescription: News impact projection of assets news
|
|
DataField: rp_ess_technical
|
|
DataFieldDescription: Event sentiment score based on technical analysis
|
|
DataField: nws18_event_relevance
|
|
DataFieldDescription: Relevance of the event to the story
|
|
DataField: rp_ess_insider
|
|
DataFieldDescription: Event sentiment score of insider trading news
|
|
DataField: rp_nip_society
|
|
DataFieldDescription: News impact projection of society-related news
|
|
DataField: nws18_bam
|
|
DataFieldDescription: News sentiment specializing in mergers and acquisitions
|
|
DataField: rp_nip_marketing
|
|
DataFieldDescription: News impact projection of marketing news
|
|
DataField: nws18_sse
|
|
DataFieldDescription: Sentiment of phrases impacting the company
|
|
DataField: rp_nip_product
|
|
DataFieldDescription: News impact projection of product and service-related news
|
|
DataField: nws18_event_similarity_days
|
|
DataFieldDescription: Days since a similar event was detected
|
|
DataField: nws18_relevance
|
|
DataFieldDescription: Relevance of news to the company
|
|
DataField: rp_ess_credit
|
|
DataFieldDescription: Event sentiment score of credit news
|
|
DataField: nws18_qep
|
|
DataFieldDescription: News sentiment based on positive and negative words on global equity
|
|
DataField: nws18_ssc
|
|
DataFieldDescription: Sentiment of the news calculated using multiple techniques
|
|
DataField: rp_ess_earnings
|
|
DataFieldDescription: Event sentiment score of earnings news
|
|
DataField: rp_ess_equity
|
|
DataFieldDescription: Event sentiment score of equity action news
|
|
DataField: rp_ess_society
|
|
DataFieldDescription: Event sentiment score of society-related news
|
|
DataField: rp_nip_inverstor
|
|
DataFieldDescription: News impact projection of investor relations news
|
|
DataField: rp_ess_price
|
|
DataFieldDescription: Event sentiment score of stock price news
|
|
DataField: rp_ess_ptg
|
|
DataFieldDescription: Event sentiment score of price target news
|
|
DataField: rp_css_partner
|
|
DataFieldDescription: Composite sentiment score of partnership news
|
|
DataField: rp_nip_partner
|
|
DataFieldDescription: News impact projection of partnership news
|
|
DataField: rp_nip_credit
|
|
DataFieldDescription: News impact projection of credit news
|
|
DataField: rp_css_earnings
|
|
DataFieldDescription: Composite sentiment score of earnings news
|
|
DataField: rp_ess_dividends
|
|
DataFieldDescription: Event sentiment score of dividends news
|
|
DataField: nws18_acb
|
|
DataFieldDescription: News sentiment specializing in corporate action announcements
|
|
DataField: rp_nip_equity
|
|
DataFieldDescription: News impact projection of equity action news
|
|
DataField: nws18_nip
|
|
DataFieldDescription: Degree of impact of the news
|
|
DataField: rp_nip_labor
|
|
DataFieldDescription: News impact projection of labor issues news
|
|
DataField: rp_css_business
|
|
DataFieldDescription: Composite sentiment score of business-related news
|
|
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_comp_non_opt_forfeited_q
|
|
DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that were forfeited during the reporting period.
|
|
DataField: fn_proceeds_from_stock_options_exercised_q
|
|
DataFieldDescription: The cash inflow associated with the amount received from holders exercising their stock options. This item inherently excludes any excess tax benefit, which the entity may have realized and reported separately.
|
|
DataField: fn_profit_loss_q
|
|
DataFieldDescription: The consolidated profit or loss for the period, net of income taxes, including the portion attributable to the noncontrolling interest.
|
|
DataField: fnd2_a_sbcpnargmsawpfipwerpr
|
|
DataFieldDescription: Weighted average price of options that were either forfeited or expired.
|
|
DataField: fnd2_a_sbcpnargmpmwggil
|
|
DataFieldDescription: Amount by which the current fair value of the underlying stock exceeds the exercise price of fully vested and expected to vest options outstanding.
|
|
DataField: fn_finite_lived_intangible_assets_net_q
|
|
DataFieldDescription: Finite Lived Intangible Assets, Net
|
|
DataField: fnd2_dbplanepdfbnfpnext12m
|
|
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fnd2_a_sbcpnargmsptawervl
|
|
DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
|
|
DataField: fn_finite_lived_intangible_assets_gross_q
|
|
DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
|
|
DataField: fn_comp_non_opt_vested_q
|
|
DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that vested during the reporting period.
|
|
DataField: fnd2_dfdtxastxdfdexprssaccrs
|
|
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible temporary differences from reserves and accruals.
|
|
DataField: fnd2_a_stkrpeprogramardamt
|
|
DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
|
|
DataField: fnd2_a_curritxexp
|
|
DataFieldDescription: Income Tax Expense, Current
|
|
DataField: fn_comp_non_opt_nonvested_number_a
|
|
DataFieldDescription: The number of non-vested equity-based payment instruments, excluding stock (or unit) options, that validly exist and are outstanding as of the balance sheet date.
|
|
DataField: fn_comp_options_out_number_q
|
|
DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
|
|
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.
|
|
DataField: fnd2_q_atdlsecexfcepsastkos
|
|
DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
|
|
DataField: fn_accum_depr_depletion_and_amortization_ppne_a
|
|
DataFieldDescription: Amount of accumulated depreciation, depletion and amortization for physical assets used in the normal conduct of business to produce goods and services.
|
|
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: fn_finite_lived_intangible_assets_gross_a
|
|
DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
|
|
DataField: fnd2_a_gwllimrml
|
|
DataFieldDescription: Amount of loss from the write-down of an asset representing the future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized.
|
|
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_comp_not_rec_a
|
|
DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
|
|
DataField: fn_income_taxes_paid_q
|
|
DataFieldDescription: The amount of cash paid during the current period to foreign, federal, state, and local authorities as taxes on income.
|
|
DataField: fn_comp_options_out_intrinsic_value_a
|
|
DataFieldDescription: The intrinsic value of a stock option is the amount by which the market value of the underlying stock exceeds the exercise price of the option.
|
|
DataField: fn_income_tax_expense_q
|
|
DataFieldDescription: Income Tax Expense (Benefit)
|
|
DataField: fnd2_a_atdlsecexfcepsastkos
|
|
DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
|
|
DataField: fnd2_a_flintasacmamtzcsrld
|
|
DataFieldDescription: Finite Lived Intangible Assets Accumulated Amortization, Customer Related
|
|
DataField: fnd2_a_ltrmdmrepoplinnext12m
|
|
DataFieldDescription: Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: 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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|
|
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