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891 lines
58 KiB
891 lines
58 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 20 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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视角一：市场摩擦的横截面成像 (Cross-sectional Imaging of Market Frictions)
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核心洞见：传统因子假设市场无摩擦。真正的Alpha可能藏于“摩擦本身”——即指令流（order flow）转化为价格运动时，在不同股票上表现出的效率差异图谱。我们不为消除摩擦建模，而是主动测绘它。
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研究提示词 (用于指导因子构建)：
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指令流冲击的“消化速率”：测量单位异常交易量（可定义为其z-score）所引发的瞬时价格冲击（如未来1-5分钟收益率），以及该冲击在随后较短时间（如30分钟、1小时）的衰减半衰期。寻找“冲击大但衰减慢”（高摩擦、低流动性）与“冲击小但衰减快”（低摩擦、高流动性）的股票，并研究其横截面收益预测能力。
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买卖失衡的“路径依赖”：考察买单流与卖单流（可用正负交易额近似）的净额在时间序列上的自相关性模式。是呈现“均值回归”（失衡迅速被套利消化）还是“趋势持续”（失衡自我强化）？这种模式在不同波动率、不同市值股票中如何变化？能否构建一个度量“订单流趋势持续性”的指标？
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价格发现的“领地性”：将价格变化分解为“由自身交易驱动”的部分和“由同行业龙头/指数驱动”的部分（可通过日内收益率与龙头/指数收益率的滚动协方差分解）。计算“自身解释比例”。该比例高的股票，其价格发现是“内生性”的；比例低的则是“外生性”或“跟随性”的。这两类股票在不同市场环境（牛市/熊市/震荡市）下的表现有何系统性差异？
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视角二：投资者注意力的生态学建模 (Ecology of Investor Attention)
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核心洞见：注意力是金融市场最稀缺的资源。市场不是信息的聚合器，而是注意力资源的分配系统。因子应捕捉注意力的“聚集-分散”、“转移-停滞”动态，而非信息本身。
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研究提示词 (用于指导因子构建)：
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注意力“聚焦度”与“涣散度”：用交易量在时间序列上的分布熵来衡量。例如，过去20个交易日内，交易量分布的基尼系数或赫芬达尔指数。高集中度（某几天放巨量）意味着注意力短期爆发；低集中度（量能均匀）意味着持续平淡的关注。研究这两种状态转换前后，股票的收益特征。
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行业内注意力的“捕食-被捕食”关系：定义“注意力领导者”（如当日涨幅前3的股票）和“注意力追随者”（同行业其他股票）。计算领导股出现后，追随者的成交量与价格在随后N个时间段内的响应速度和强度。这种响应的不对称性（有些股票迅速响应，有些滞后）能否预测未来的相对强弱？
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“注意力惯性”与“注意力反转”：度量一个股票在失去短期催化剂（如公告、事件）后，其异常成交量（或搜索量、社交媒体活跃度）回落至基线水平的速度。回落慢的股票具有“注意力惯性”，可能存在定价偏差的持续；回落快的股票则“注意力反转”迅速。构建一个“注意力衰减时间”因子。
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视角三：价格运动的“形态语法”与“叙事连贯性” (Morphological Syntax & Narrative Coherence of Price Movements)
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核心洞见：价格运动不仅是有方向的，更是有“形状”和“语法”的。如同语言，一段价格走势是否存在合“语法”的叙事结构（如“筑底-突破-回踩-主升”），还是杂乱无章的“噪音词汇”？市场参与者会潜意识地识别并交易这些“连贯叙事”。
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研究提示词 (用于指导因子构建)：
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K线序列的“可压缩性”：使用一种简化的算法（如趋势线拟合后的残差大小，或一定容忍度下的分段线性近似所需节点数），来衡量过去一段价格序列的信息复杂度。低复杂度（高可压缩性）意味着价格运动清晰、有规律；高复杂度则意味着混乱。研究“从混乱转向清晰”或“从清晰转向混乱”的拐点。
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关键价位互动的“叙事逻辑”：识别近期的显著高点、低点、缺口、密集成交区作为“关键叙事节点”。分析当前价格在接近这些节点时的行为：是“尊重”（反弹/受阻）还是“无视”（直接穿越）？一系列对历史节点的“尊重”行为构成了一个连贯的“支撑阻力叙事”。能否量化这种叙事逻辑的强度？
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多尺度运动的“相位同步”：选取短期（如5日）、中期（20日）、长期（60日）的价格滤波序列（如移动平均线）。计算它们之间方向变化的领先滞后关系与同步性（可类似相位角分析）。例如，短期均线是否总是率先转向并引导中长期？还是三者经常背离？“多周期共振向上/向下”这种高度同步的状态，其形成过程和瓦解过程有何预测意义？
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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
|
|
Operator: group_rank(x, group)
|
|
Description: Each elements in a group is assigned the corresponding rank in this group
|
|
Operator: group_scale(x, group)
|
|
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
|
|
Operator: group_zscore(x, group)
|
|
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
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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_720
|
|
DataFieldDescription: Price at which a stock's call options with expiration 720 days in the future break even based on its recent bid/ask mean.
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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.
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|
DataField: forward_price_120
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|
DataFieldDescription: Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
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|
DataField: option_breakeven_20
|
|
DataFieldDescription: Price at which a stock's options with expiration 20 days in the future break even based on its recent bid/ask mean.
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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.
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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: 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: pcr_vol_1080
|
|
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: 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.
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|
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.
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|
DataField: put_breakeven_10
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|
DataFieldDescription: Price at which a stock's put options with expiration 10 days in the future break even based on its recent bid/ask mean.
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|
DataField: option_breakeven_270
|
|
DataFieldDescription: Price at which a stock's options with expiration 270 days in the future break even based on its recent bid/ask mean.
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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_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.
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|
DataField: pcr_vol_30
|
|
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: option_breakeven_90
|
|
DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
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DataField: call_breakeven_30
|
|
DataFieldDescription: Price at which a stock's call options with expiration 30 days in the future break even based on its recent bid/ask mean.
|
|
DataField: pcr_oi_270
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 270 days in the future.
|
|
DataField: pcr_vol_120
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 120 days in the future.
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|
DataField: put_breakeven_720
|
|
DataFieldDescription: Price at which a stock's put options with expiration 720 days in the future break even based on its recent bid/ask mean.
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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.
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|
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.
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DataField: pcr_oi_60
|
|
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: 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.
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|
DataField: call_breakeven_10
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|
DataFieldDescription: Price at which a stock's call options with expiration 10 days in the future break even based on its recent bid/ask mean.
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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_oi_720
|
|
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: put_breakeven_120
|
|
DataFieldDescription: Price at which a stock's put options with expiration 120 days in the future break even based on its recent bid/ask mean.
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DataField: forward_price_360
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|
DataFieldDescription: Forward price at 360 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
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DataField: fnd6_newqeventv110_ppentq
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|
DataFieldDescription: Property Plant and Equipment - Total (Net)
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DataField: fnd6_newqeventv110_dpactq
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|
DataFieldDescription: Depreciation, Depletion and Amortization (Accumulated)
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|
DataField: fnd6_xrent
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|
DataFieldDescription: Rental Expense
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|
DataField: fnd6_eventv110_spidq
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|
DataFieldDescription: Other Special Items Diluted EPS Effect
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|
DataField: fnd6_invwip
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|
DataFieldDescription: Inventories - Work In Process
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|
DataField: fnd6_newa1v1300_dcom
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DataFieldDescription: Deferred Compensation
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DataField: fnd6_cimii
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|
DataFieldDescription: Comprehensive Income - Noncontrolling Interest
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|
DataField: fnd6_pncepsq
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|
DataFieldDescription: Core Pension Adjustment Basic EPS Effect
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DataField: fnd6_txs
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|
DataFieldDescription: Income Taxes - State
|
|
DataField: fnd6_newqeventv110_ivltq
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|
DataFieldDescription: Total Long-term Investments
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|
DataField: fnd6_newqv1300_rdipdq
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|
DataFieldDescription: In Process R&D Expense Diluted EPS Effect
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|
DataField: fnd6_newqv1300_spceepspq
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|
DataFieldDescription: S&P Core Earnings EPS Basic - Preliminary
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DataField: fnd6_eventv110_nrtxtdq
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|
DataFieldDescription: Nonrecurring Income Taxes Diluted EPS Effect
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|
DataField: fnd6_newqv1300_lseq
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|
DataFieldDescription: Liabilities and Stockholders' Equity - Total
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|
DataField: fnd6_newqeventv110_pnc12
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|
DataFieldDescription: Pension Core Adjustment - 12mm
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|
DataField: fnd6_newqeventv110_cicurrq
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|
DataFieldDescription: Comp Inc - Currency Trans Adj
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|
DataField: fnd6_newqeventv110_cshiq
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|
DataFieldDescription: Common Shares Issued
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|
DataField: fnd6_newa1v1300_dlc
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|
DataFieldDescription: Debt in Current Liabilities - Total
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|
DataField: fnd6_newa2v1300_xopteps
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|
DataFieldDescription: Implied Option EPS Basic
|
|
DataField: fnd6_eventv110_gdwlidq
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|
DataFieldDescription: Impairment of Goodwill Diluted EPS Effect
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|
DataField: fnd6_tfvl
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|
DataFieldDescription: Total Fair Value Liabilities
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|
DataField: fnd6_newqv1300_epsfiq
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|
DataFieldDescription: Earnings Per Share (Diluted) - Including Extraordinary Items
|
|
DataField: fnd6_ivaeqs
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|
DataFieldDescription: Investments at Equity
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|
DataField: fnd6_oiadps
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|
DataFieldDescription: Operating Income after Depreciation
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|
DataField: fnd6_cptnewqeventv110_opepsq
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|
DataFieldDescription: Earnings Per Share from Operations
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|
DataField: fnd6_newa2v1300_oiadp
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|
DataFieldDescription: Operating Income After Depreciation
|
|
DataField: fnd6_nopio
|
|
DataFieldDescription: Nonoperating Income (Expense) - Other
|
|
DataField: fnd6_cld2
|
|
DataFieldDescription: Capitalized Leases - Due in 2nd Year
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|
DataField: fnd6_cptnewqeventv110_ltq
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|
DataFieldDescription: Liabilities - Total
|
|
DataField: fnd6_newqeventv110_prceps12
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|
DataFieldDescription: Core Post Retirement Adjustment Basic EPS Effect 12MM
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|
DataField: scl12_alltype_buzzvec
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|
DataFieldDescription: sentiment volume
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|
DataField: scl12_alltype_sentvec
|
|
DataFieldDescription: sentiment
|
|
DataField: scl12_alltype_typevec
|
|
DataFieldDescription: instrument type index
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|
DataField: scl12_buzz
|
|
DataFieldDescription: relative sentiment volume
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|
DataField: scl12_buzz_fast_d1
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|
DataFieldDescription: relative sentiment volume
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|
DataField: scl12_buzzvec
|
|
DataFieldDescription: sentiment volume
|
|
DataField: scl12_sentiment
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|
DataFieldDescription: sentiment
|
|
DataField: scl12_sentiment_fast_d1
|
|
DataFieldDescription: sentiment
|
|
DataField: scl12_sentvec
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|
DataFieldDescription: sentiment
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|
DataField: scl12_typevec
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|
DataFieldDescription: instrument type index
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|
DataField: snt_buzz
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|
DataFieldDescription: negative relative sentiment volume, fill nan with 0
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|
DataField: snt_buzz_bfl
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|
DataFieldDescription: negative relative sentiment volume, fill nan with 1
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|
DataField: snt_buzz_bfl_fast_d1
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|
DataFieldDescription: negative relative sentiment volume, fill nan with 1
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|
DataField: snt_buzz_fast_d1
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|
DataFieldDescription: negative relative sentiment volume, fill nan with 0
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|
DataField: snt_buzz_ret
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|
DataFieldDescription: negative return of relative sentiment volume
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|
DataField: snt_buzz_ret_fast_d1
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|
DataFieldDescription: negative return of relative sentiment volume
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|
DataField: snt_value
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|
DataFieldDescription: negative sentiment, fill nan with 0
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|
DataField: snt_value_fast_d1
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|
DataFieldDescription: negative sentiment, fill nan with 0
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|
DataField: analyst_revision_rank_derivative
|
|
DataFieldDescription: Change in ranking for analyst revisions and momentum compared to previous period.
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|
DataField: cashflow_efficiency_rank_derivative
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|
DataFieldDescription: Change in ranking for cash flow generation and profitability compared to previous period.
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|
DataField: composite_factor_score_derivative
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|
DataFieldDescription: Change in overall composite factor score from the prior period.
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|
DataField: earnings_certainty_rank_derivative
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|
DataFieldDescription: Change in ranking for earnings sustainability and certainty compared to previous period.
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|
DataField: fscore_bfl_growth
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|
DataFieldDescription: The purpose of this metric is to qualify the expected MT growth potential of the stock.
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DataField: fscore_bfl_momentum
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DataFieldDescription: The purpose of this metric is to identify stocks which are currently undergoing either up or downward analyst revisions.
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|
DataField: fscore_bfl_profitability
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|
DataFieldDescription: The purpose of this metric is to rank stock based on their ability to generate cash flows.
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DataField: fscore_bfl_quality
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|
DataFieldDescription: The purpose of this metric is to measure both the sustainability and certainty of earnings.
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|
DataField: fscore_bfl_surface
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|
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.
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DataField: fscore_bfl_surface_accel
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|
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?).
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|
DataField: fscore_bfl_total
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|
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
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|
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
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|
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
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|
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
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DataField: beta_last_360_days_spy
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|
DataFieldDescription: Beta to SPY in 360 Days
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|
DataField: beta_last_60_days_spy
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|
DataFieldDescription: Beta to SPY in 60 Days
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|
DataField: beta_last_90_days_spy
|
|
DataFieldDescription: Beta to SPY in 90 Days
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|
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
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|
DataField: systematic_risk_last_90_days
|
|
DataFieldDescription: Systematic Risk Last 90 Days
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|
DataField: unsystematic_risk_last_30_days
|
|
DataFieldDescription: Unsystematic Risk Last 30 Days - Relative to SPY
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|
DataField: unsystematic_risk_last_360_days
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|
DataFieldDescription: Unsystematic Risk Last 360 Days - Relative to SPY
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|
DataField: unsystematic_risk_last_60_days
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|
DataFieldDescription: Unsystematic Risk Last 60 Days - Relative to SPY
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|
DataField: unsystematic_risk_last_90_days
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|
DataFieldDescription: Unsystematic Risk Last 90 Days - Relative to SPY
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|
DataField: anl4_fcf_median
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|
DataFieldDescription: Free cash flow - aggregation on estimations, 50th percentile
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|
DataField: max_selling_general_admin_guidance
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|
DataFieldDescription: The maximum guidance value for Selling, General & Administrative Expense
|
|
DataField: anl4_detailltv4v104_preest
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|
DataFieldDescription: The previous estimation of financial item
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|
DataField: anl4_ads1detailqfv110_prevval
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|
DataFieldDescription: The previous estimation of financial item
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|
DataField: anl4_capex_flag
|
|
DataFieldDescription: Capital Expenditures - forecast type (revision/new/...)
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|
DataField: anl4_qfd1_az_wol_spe
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|
DataFieldDescription: Earnings per share - The lowest estimation
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|
DataField: anl4_totgw_low
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|
DataFieldDescription: Total Goodwill - The lowest estimation
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|
DataField: anl4_bac1conafv110_item
|
|
DataFieldDescription: Financial item
|
|
DataField: sales_min_guidance_value
|
|
DataFieldDescription: Minimum sales guidance for the annual period.
|
|
DataField: est_sales
|
|
DataFieldDescription: Sales - mean of estimations
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|
DataField: min_shareholders_equity_guidance
|
|
DataFieldDescription: Minimum guidance value for Shareholders' Equity
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|
DataField: guidance_value_currency_code_qtr
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|
DataFieldDescription: Home currency of instrument
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|
DataField: est_cashflow_ps
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|
DataFieldDescription: Cash Flow Per Share - average of estimations
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|
DataField: min_tangible_book_value_per_share_guidance
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|
DataFieldDescription: Tangible Book Value per Share - minimum guidance value
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|
DataField: anl4_gric_std
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|
DataFieldDescription: Gross income - std of estimations
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|
DataField: research_development_expense_reported_value
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|
DataFieldDescription: Research & Development (Income Statement) Value in Millions
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|
DataField: anl4_basicafv4_actual
|
|
DataFieldDescription: Announced financial data for the annual period.
|
|
DataField: max_stock_option_expense_guidance
|
|
DataFieldDescription: Stock option expense - Maximum guidance value for the annual period
|
|
DataField: cashflow_per_share_maximum
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|
DataFieldDescription: Cash Flow - The highest estimation, per share, with a delay of 1 quarter
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|
DataField: anl4_gric_number
|
|
DataFieldDescription: Gross income - number of estimations
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|
DataField: anl4_capex_low
|
|
DataFieldDescription: Capital Expenditures - The lowest estimation
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|
DataField: anl4_qf_az_cfps_number
|
|
DataFieldDescription: Cash Flow Per Share - number of estimations
|
|
DataField: pretax_income_total
|
|
DataFieldDescription: Pretax Profit - Value for the annual period
|
|
DataField: max_investing_cashflow_guidance_2
|
|
DataFieldDescription: The maximum guidance value for Cash Flow from Investing activities on an annual basis.
|
|
DataField: shares_outstanding_max_guidance
|
|
DataFieldDescription: Maximum guidance value for Shares
|
|
DataField: anl4_cfo_mean
|
|
DataFieldDescription: Cash Flow From Operations - mean of estimations
|
|
DataField: min_free_cash_flow_per_share_guidance
|
|
DataFieldDescription: Free cash flow per share - minimum guidance value for the annual period
|
|
DataField: total_goodwill_reported_value
|
|
DataFieldDescription: Total Goodwill - Actual Value in Millions
|
|
DataField: anl4_netprofit_number
|
|
DataFieldDescription: Net profit - number of estimations
|
|
DataField: anl4_qf_az_hgih_vid
|
|
DataFieldDescription: Dividend per share - The highest estimation
|
|
DataField: pv13_revere_comproduct_company
|
|
DataFieldDescription: Company product
|
|
DataField: pv13_new_1l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_r2_liquid_min10_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min5_corr21_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min52_1k_sector
|
|
DataFieldDescription: Grouping fields for top 1000
|
|
DataField: pv13_hierarchy_min52_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min10_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_2k_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min20_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h2_min2_1k_sector
|
|
DataFieldDescription: Grouping fields for top 1000
|
|
DataField: pv13_reportperiodend
|
|
DataFieldDescription: Stated end date for the report
|
|
DataField: pv13_hierarchy_min2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: rel_num_cust
|
|
DataFieldDescription: number of the instrument's customers
|
|
DataField: pv13_hierarchy_min2_focused_only_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_index_value
|
|
DataFieldDescription: Value of specified index for the date
|
|
DataField: pv13_h_min5_500_sector
|
|
DataFieldDescription: Grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_only_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min20_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min5_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min10_industry_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_key_sector_total
|
|
DataFieldDescription: Number of key focus sectors for the company
|
|
DataField: pv13_hierarchy_min50_f3_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min2_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_h_min51_f3_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_pureplay_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_3l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_region
|
|
DataFieldDescription: Unique code of the region
|
|
DataField: pv13_rcsed_6l
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|
DataFieldDescription: grouping fields
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|
DataField: pv13_r2_liquid_min5_sector
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|
DataFieldDescription: grouping fields
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|
DataField: implied_volatility_mean_180
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 180 days
|
|
DataField: implied_volatility_mean_720
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 720 days
|
|
DataField: implied_volatility_mean_150
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
|
|
DataField: implied_volatility_mean_skew_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
|
|
DataField: parkinson_volatility_180
|
|
DataFieldDescription: Parkinson model's historical volatility over 180 days
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|
DataField: implied_volatility_mean_skew_150
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 150 days
|
|
DataField: implied_volatility_put_180
|
|
DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
|
|
DataField: implied_volatility_put_150
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
|
|
DataField: implied_volatility_put_90
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
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|
DataField: implied_volatility_put_720
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
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|
DataField: implied_volatility_mean_skew_30
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
|
|
DataField: implied_volatility_mean_skew_120
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|
DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
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|
DataField: implied_volatility_put_120
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
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|
DataField: implied_volatility_call_30
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
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|
DataField: implied_volatility_mean_120
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 120 days
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|
DataField: implied_volatility_call_120
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
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|
DataField: implied_volatility_call_60
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|
DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
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|
DataField: parkinson_volatility_30
|
|
DataFieldDescription: Parkinson model's historical volatility over 30 days
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|
DataField: historical_volatility_30
|
|
DataFieldDescription: Close-to-close Historical volatility over 30 days
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|
DataField: historical_volatility_10
|
|
DataFieldDescription: Close-to-close Historical volatility over 10 days
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|
DataField: implied_volatility_mean_skew_270
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
|
|
DataField: implied_volatility_mean_skew_180
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
|
|
DataField: historical_volatility_150
|
|
DataFieldDescription: Close-to-close Historical volatility over 150 days
|
|
DataField: implied_volatility_mean_30
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 30 days
|
|
DataField: implied_volatility_call_20
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
|
|
DataField: historical_volatility_120
|
|
DataFieldDescription: Close-to-close Historical volatility over 120 days
|
|
DataField: implied_volatility_mean_1080
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 3 years
|
|
DataField: parkinson_volatility_150
|
|
DataFieldDescription: Parkinson model's historical volatility over 150 days
|
|
DataField: implied_volatility_put_60
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
|
|
DataField: implied_volatility_call_150
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
|
|
DataField: nws12_afterhsz_2s
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 2 percentage points
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|
DataField: nws12_mainz_01s
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
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|
DataField: nws12_afterhsz_3s
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
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|
DataField: nws12_prez_volstddev
|
|
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
|
|
DataField: nws12_prez_41rta
|
|
DataFieldDescription: Fourteen-day Average True Range
|
|
DataField: nws12_prez_4l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
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|
DataField: news_session_range
|
|
DataFieldDescription: Session High Price - Session Low Price
|
|
DataField: nws12_mainz_57p
|
|
DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
|
|
DataField: nws12_mainz_tonlow
|
|
DataFieldDescription: Lowest price reached during the session before the time of the news
|
|
DataField: nws12_prez_mainvwap
|
|
DataFieldDescription: Main session volume-weighted average price
|
|
DataField: nws12_mainz_reportsess
|
|
DataFieldDescription: Index of Session on which the spreadsheet is reporting
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|
DataField: news_high_exc_stddev
|
|
DataFieldDescription: (EODHigh - TONLast)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
|
|
DataField: news_prev_close
|
|
DataFieldDescription: Previous trading day's close price
|
|
DataField: nws12_afterhsz_120_min
|
|
DataFieldDescription: The percent change in price in the first 120 minutes following the news release
|
|
DataField: nws12_prez_dayopen
|
|
DataFieldDescription: Price at the session open
|
|
DataField: nws12_afterhsz_60_min
|
|
DataFieldDescription: The percent change in price in the first 60 minutes following the news release
|
|
DataField: nws12_prez_maxdnamt
|
|
DataFieldDescription: The price at the time of the news minus the after the news low
|
|
DataField: nws12_prez_2l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
|
|
DataField: news_ton_low
|
|
DataFieldDescription: Lowest price reached during the session before the time of the news
|
|
DataField: nws12_prez_result1
|
|
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
|
|
DataField: nws12_afterhsz_3l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
|
|
DataField: nws12_afterhsz_maxup
|
|
DataFieldDescription: Percent change from the price at the time of the news to the after the news high
|
|
DataField: news_ton_high
|
|
DataFieldDescription: Highest price reached during the session before the time of news
|
|
DataField: nws12_prez_newssess
|
|
DataFieldDescription: Index of session in which the news was reported
|
|
DataField: nws12_prez_3l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
|
|
DataField: nws12_afterhsz_01p
|
|
DataFieldDescription: The minimum of L or S above for 10 minute bucket
|
|
DataField: news_mins_5_pct_up
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 5 percentage points
|
|
DataField: news_mins_3_pct_up
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
|
|
DataField: nws12_afterhsz_provider
|
|
DataFieldDescription: index of name of the news provider
|
|
DataField: nws12_afterhsz_57p
|
|
DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
|
|
DataField: top1000
|
|
DataFieldDescription: 20140630
|
|
DataField: top200
|
|
DataFieldDescription: 20140630
|
|
DataField: top3000
|
|
DataFieldDescription: 20140630
|
|
DataField: top500
|
|
DataFieldDescription: 20140630
|
|
DataField: topsp500
|
|
DataFieldDescription: 20140630
|
|
DataField: rp_css_technical
|
|
DataFieldDescription: Composite sentiment score based on technical analysis
|
|
DataField: rp_nip_labor
|
|
DataFieldDescription: News impact projection of labor issues news
|
|
DataField: nws18_ssc
|
|
DataFieldDescription: Sentiment of the news calculated using multiple techniques
|
|
DataField: rp_ess_assets
|
|
DataFieldDescription: Event sentiment score of assets news
|
|
DataField: rp_nip_ratings
|
|
DataFieldDescription: News impact projection of analyst ratings-related news
|
|
DataField: nws18_event_similarity_days
|
|
DataFieldDescription: Days since a similar event was detected
|
|
DataField: rp_ess_partner
|
|
DataFieldDescription: Event sentiment score of partnership news
|
|
DataField: rp_css_legal
|
|
DataFieldDescription: Composite sentiment score of legal news
|
|
DataField: rp_ess_mna
|
|
DataFieldDescription: Event sentiment score of mergers and acquisitions-related news
|
|
DataField: nws18_relevance
|
|
DataFieldDescription: Relevance of news to the company
|
|
DataField: rp_css_labor
|
|
DataFieldDescription: Composite sentiment score of labor issues news
|
|
DataField: rp_css_credit_ratings
|
|
DataFieldDescription: Composite sentiment score of credit ratings news
|
|
DataField: rp_nip_ptg
|
|
DataFieldDescription: News impact projection of price target news
|
|
DataField: rp_nip_partner
|
|
DataFieldDescription: News impact projection of partnership news
|
|
DataField: rp_nip_credit_ratings
|
|
DataFieldDescription: News impact projection of credit ratings news
|
|
DataField: rp_css_insider
|
|
DataFieldDescription: Composite sentiment score of insider trading news
|
|
DataField: rp_css_mna
|
|
DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
|
|
DataField: rp_css_credit
|
|
DataFieldDescription: Composite sentiment score of credit news
|
|
DataField: rp_nip_equity
|
|
DataFieldDescription: News impact projection of equity action news
|
|
DataField: rp_ess_ratings
|
|
DataFieldDescription: Event sentiment score of analyst ratings-related news
|
|
DataField: rp_nip_product
|
|
DataFieldDescription: News impact projection of product and service-related news
|
|
DataField: rp_nip_inverstor
|
|
DataFieldDescription: News impact projection of investor relations news
|
|
DataField: rp_css_inverstor
|
|
DataFieldDescription: Composite sentiment score of investor relations news
|
|
DataField: rp_ess_credit
|
|
DataFieldDescription: Event sentiment score of credit news
|
|
DataField: rp_css_dividends
|
|
DataFieldDescription: Composite sentiment score of dividends news
|
|
DataField: rp_ess_legal
|
|
DataFieldDescription: Event sentiment score of legal news
|
|
DataField: nws18_ghc_lna
|
|
DataFieldDescription: Change in analyst recommendation
|
|
DataField: rp_css_assets
|
|
DataFieldDescription: Composite sentiment score of assets news
|
|
DataField: rp_css_business
|
|
DataFieldDescription: Composite sentiment score of business-related news
|
|
DataField: rp_css_revenue
|
|
DataFieldDescription: Composite sentiment score of revenue news
|
|
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: fnd2_a_sbcpnargmpmtwopsffesip
|
|
DataFieldDescription: The number of shares under options that were cancelled during the reporting period as a result of occurrence of a terminating event specified in contractual agreements pertaining to the stock option plan.
|
|
DataField: fn_amortization_of_intangible_assets_q
|
|
DataFieldDescription: The aggregate expense charged against earnings to allocate the cost of intangible assets (nonphysical assets not used in production) in a systematic and rational manner to the periods expected to benefit from such assets. As a noncash expense, this element is added back to net income when calculating cash provided by or used in operations using the indirect method.
|
|
DataField: fn_op_lease_min_pay_due_in_5y_a
|
|
DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fn_goodwill_acquired_during_period_a
|
|
DataFieldDescription: Amount of increase in asset representing future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized resulting from a business combination.
|
|
DataField: fnd2_a_flintasgcsrld
|
|
DataFieldDescription: Finite Lived Intangible Assets Gross, Customer Related
|
|
DataField: fn_comp_number_of_shares_authorized_a
|
|
DataFieldDescription: Count of unique IDs of industry participants. Industry stands for an aggregate view of all equity clearance activity for the date, symbol, and transaction type in question.
|
|
DataField: fnd2_a_flintasamt1expnext12m
|
|
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 next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fn_treasury_stock_shares_a
|
|
DataFieldDescription: Number of common and preferred shares that were previously issued and that were repurchased by the issuing entity and held in treasury on the financial statement date. This stock has no voting rights and receives no dividends.
|
|
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_stkrpeprogramardamt
|
|
DataFieldDescription: Amount of a stock repurchase plan authorized by an entity's Board of Directors.
|
|
DataField: fn_oth_income_loss_available_for_sale_securities_adj_of_tax_q
|
|
DataFieldDescription: Amount after tax and reclassification adjustments, of appreciation (loss) in value of unsold available-for-sale securities. Excludes amounts related to other than temporary impairment (OTTI) loss.
|
|
DataField: fn_assets_fair_val_l2_q
|
|
DataFieldDescription: Asset Fair Value, Recurring, Level 2
|
|
DataField: fnd2_propplteqflublgland
|
|
DataFieldDescription: PPE, Buildings & Land, Useful Life, Maximum
|
|
DataField: fn_assets_fair_val_l3_q
|
|
DataFieldDescription: Asset Fair Value, Recurring, Level 3
|
|
DataField: fnd2_dfdfritxexp
|
|
DataFieldDescription: Income Tax Expense, Deferred - Foreign
|
|
DataField: fnd2_a_flintasamt1expythree
|
|
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 3rd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fn_finite_lived_intangible_assets_net_a
|
|
DataFieldDescription: Finite Lived Intangible Assets, Net
|
|
DataField: fn_prepaid_expense_a
|
|
DataFieldDescription: Carrying amount for an unclassified balance sheet date of expenditures made in advance of when the economic benefit of the cost will be realized, and which will be expensed in future periods with the passage of time or when a triggering event occurs. For a classified balance sheet, represents the noncurrent portion of prepaid expenses (the current portion has a separate concept).
|
|
DataField: fnd2_dfctrbplancstrg
|
|
DataFieldDescription: The amount of the cost recognized during the period for defined contribution plans.
|
|
DataField: fn_employee_related_liab_q
|
|
DataFieldDescription: Total of the carrying values as of the balance sheet date of obligations incurred through that date and payable for obligations related to services received from employees, such as accrued salaries and bonuses, payroll taxes and fringe benefits. For classified balance sheets, used to reflect the current portion of the liabilities (due within 1 year or within the normal operating cycle if longer); for unclassified balance sheets, used to reflect the total liabilities (regardless of due date).
|
|
DataField: fn_entity_common_stock_shares_out_a
|
|
DataFieldDescription: Indicate number of shares or other units outstanding of each of registrant's classes of capital or common stock or other ownership interests, if and as stated on cover of related periodic report. Where multiple classes or units exist define each class/interest by adding class of stock items such as Common Class A [Member], Common Class B [Member] or Partnership Interest [Member] onto the Instrument [Domain] of the Entity Listings, Instrument.
|
|
DataField: fnd2_a_sbcpnargmsawpfipwerpr
|
|
DataFieldDescription: Weighted average price of options that were either forfeited or expired.
|
|
DataField: fnd2_a_atdlsecexfcepsastkos
|
|
DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
|
|
DataField: fnd2_dbplanepdfbnfpyfour
|
|
DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in 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: fn_avg_diluted_sharesout_adj_q
|
|
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_antidilutive_securities_excl_from_eps_q
|
|
DataFieldDescription: Securities (including those issuable pursuant to contingent stock agreements) that could potentially dilute basic earnings per share (EPS) or earnings per unit (EPU) in the future that were not included in the computation of diluted EPS or EPU because to do so would increase EPS or EPU amounts or decrease loss per share or unit amounts for the period presented.
|
|
DataField: fn_op_lease_min_pay_due_a
|
|
DataFieldDescription: Amount of required minimum rental payments for leases having an initial or remaining non-cancelable letter-terms in excess of 1 year.
|
|
DataField: fn_def_tax_assets_liab_net_a
|
|
DataFieldDescription: Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.
|
|
DataField: fn_line_of_credit_facility_amount_out_q
|
|
DataFieldDescription: Amount borrowed under the credit facility as of the 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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|
|
|
|