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961 lines
58 KiB
961 lines
58 KiB
任务指令
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一、核心设计理念
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你是一名WorldQuant WebSim因子工程师，需要设计用于行业轮动策略的复合型Alpha因子。所有因子必须基于以下三个创新视角构建，每个视角提供独特的研究框架：
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视角一：市场摩擦的横截面测绘 (Cross-sectional Imaging of Market Frictions)
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核心思想：市场摩擦（流动性差异、交易冲击、价格发现延迟）不是需要消除的噪音，而是Alpha的直接来源。主动测绘不同股票对相同指令流冲击的差异化反应模式。
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关键研究维度：
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指令流冲击的"消化速率"图谱：测量单位异常交易量引发的价格冲击及其衰减速度。构建"冲击-衰减"二维坐标系，识别高摩擦（冲击大、衰减慢）与低摩擦（冲击小、衰减快）的股票集群。
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买卖失衡的"路径依赖"模式：分析订单流净额的时间序列特性（均值回归vs趋势持续），量化不同市场状态下订单流的自强化或自纠正机制。
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价格发现的"领地性"划分：分解价格变动的驱动来源（自身交易驱动vs行业/指数驱动），计算"价格发现自主权"指标，研究内生性与外生性股票在不同市场环境中的轮动规律。
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视角二：投资者注意力的生态学系统 (Ecology of Investor Attention)
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核心思想：金融市场是注意力资源的分配系统而非信息聚合器。Alpha来源于对注意力"聚集-分散-转移"动态的精准捕捉。
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关键研究维度：
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注意力分布的"聚焦度"谱系：量化交易量/活跃度在时间维度上的集中程度（基尼系数、赫芬达尔指数），识别注意力爆发期、持续关注期和注意力真空期。
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行业内注意力的"级联传导"网络：建立领导者-追随者注意力传导模型，测量强势股票出现后，同行业其他股票的响应速度、响应强度和响应延迟。
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注意力惯性的"衰减曲线"：度量催化事件结束后，异常关注度回归基线的速度，构建"注意力记忆时长"因子，捕捉定价偏差的持续性。
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视角三：价格运动的"形态语法"解析 (Morphological Syntax of Price Movements)
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核心思想：价格运动具有类似语言的"语法结构"和"叙事连贯性"。市场参与者潜意识地识别并交易这些形态模式，为系统性形态识别提供Alpha机会。
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关键研究维度：
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价格序列的"可压缩性"度量：使用简化算法（分段线性近似、趋势线拟合残差）量化价格运动的规律性程度，识别从混沌转向有序（或相反）的临界状态。
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关键价位的"叙事逻辑"强度：分析价格在历史关键节点（前高、前低、缺口、密集区）的行为一致性，量化"支撑阻力叙事"的连贯性得分。
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多时间尺度的"相位同步"分析：研究不同周期滤波序列（如5日、20日、60日均线）之间的领先滞后关系和同步程度，识别多周期共振的形成与瓦解过程。
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二、因子构建方法论
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2.1 数据字段使用规范
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可用字段：
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close: 收盘价（唯一价格字段）
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volume: 成交量（用于规模代理、活跃度度量）
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returns: 收益率序列，定义为 ts_delta(close, 1) 或 divide(close, ts_delay(close, 1)) - 1
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禁止字段：
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❌ market_cap, marketcap, mkt_cap（不存在）
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✅ 使用volume作为规模代理，必要时进行横截面排序和分组
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2.2 复合因子构建框架
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维度融合模板（至少选择2个维度组合）：
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A. 领导力动量 = 时序动量 × 横截面领导力调整
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text
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逻辑：大成交量股票的动量信号更强、更持续
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结构示例：group_mean(ts_delta(close, 20), 1, bucket(rank(volume), range="0,3,0.4"))
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经济解释：测量不同成交量分组内价格变化的均值，捕捉大成交量群体的主导方向
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B. 状态自适应动量 = 市场状态 × 动量周期选择
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text
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逻辑：高波动环境使用短期动量，低波动环境使用长期动量
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结构示例：if_else(ts_std_dev(returns, 20) > 0.02, ts_delta(close, 5), ts_delta(close, 20))
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经济解释：根据波动率状态动态调整动量计算窗口，适应不同市场环境
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C. 行业传导因子 = 行业间相关性 × 领先滞后关系
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text
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逻辑：与强势行业保持高相关性且略有滞后的行业可能迎来轮动机会
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结构示例：multiply(ts_corr(group_mean(returns, 1, industry_A), group_mean(returns, 1, industry_B), 30), ts_delta(close, 10))
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经济解释：测量行业间联动强度与自身动量的协同效应
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D. 情绪反转因子 = 过度交易信号 × 趋势强度
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text
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逻辑：在过度交易区域，强势趋势可能面临反转；在交易清淡区域，趋势可能延续
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结构示例：multiply(reverse(ts_rank(divide(volume, ts_mean(volume, 20)), 10)), ts_delta(close, 20))
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经济解释：交易活跃度异常高时反转动量信号，异常低时增强动量信号
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2.3 关键操作符使用规范
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1. ts_regression使用规范：
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✅ 正确：reg_slope = ts_regression(close, ts_step(1), 30, 0, 1)
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❌ 错误：避免深度嵌套，如ts_delta(ts_regression(close, ts_step(1), 30, 0, 1), 5)
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✅ 替代方案：先计算回归斜率，再对其应用ts_delta
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2. if_else条件表达式规范：
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✅ 正确：if_else(ts_rank(ts_std_dev(returns, 60), 120) > 0.7, 短期动量, 长期动量)
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❌ 错误：避免复杂序列比较，如ts_std_dev(returns, 60) > ts_mean(ts_std_dev(returns, 60), 120)
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3. bucket分组函数规范：
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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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4. 行业处理函数：
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group_mean(x, weight, group): 计算组内加权平均
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group_neutralize(x, group): 对组内进行中性化处理
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group_rank(x, group): 计算组内排序
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group_scale(x, group): 组内标准化到[0,1]
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group_zscore(x, group): 计算组内z-score
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2.4 参数选择逻辑
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回顾期d应从以下具有市场意义的数值中选择：[5, 10, 20, 30, 60, 120]
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5: 周度（5个交易日）
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10: 双周
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20: 月度（约20个交易日）
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30: 月半
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60: 季度
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120: 半年
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阈值参数从[0.5, 0.7, 0.8]中选择
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同一因子内不同组件的参数应差异化，体现多时间尺度融合
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三、因子组件库（可自由组合）
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3.1 动量类组件
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简单动量：ts_delta(close, {d})
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回归动量：ts_regression(close, ts_step(1), {d}, 0, 1)（返回斜率）
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加速动量：ts_delta(ts_delta(close, 5), 5)
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排名动量：ts_rank(ts_delta(close, 20), 60)
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3.2 波动性与风险调整组件
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波动率：ts_std_dev(returns, {d})
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平均绝对收益：ts_mean(abs(returns), {d})
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波动率调整：divide(ts_delta(close, 20), ts_std_dev(returns, 20))
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波动率状态：ts_rank(ts_std_dev(returns, 20), 60)
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3.3 成交量与活跃度组件
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成交量异常：divide(volume, ts_mean(volume, {d}))
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成交量z-score：ts_zscore(volume, {d})
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成交量排名：rank(volume)
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成交量分布：bucket(rank(volume), range="0,3,0.4")
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3.4 横截面调整组件
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规模分组：if_else(rank(volume) > 0.7, 大市值组信号, 小市值组信号)
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相对强弱：divide(ts_delta(close, 10), group_mean(ts_delta(close, 10), 1, industry))
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行业中性化：group_neutralize(原始信号, industry)
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3.5 相关性与时序关系组件
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时间序列相关性：ts_corr({x}, {y}, {d})
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协方差：ts_covariance({y}, {x}, {d})
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领先滞后关系：ts_corr(ts_delay(x, 1), y, d)
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四、因子构建原则
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4.1 复杂度控制原则
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嵌套层数建议不超过3层
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每个表达式应有清晰的经济逻辑解释
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避免过度优化和数据挖掘偏差
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4.2 交易可行性原则
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严格避免未来函数（只能使用历史信息）
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考虑实际交易成本（避免高换手率因子）
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使用hump(x, hump=0.01)平滑信号变化，降低换手
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4.3 风险控制原则
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包含波动率调整元素
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考虑极端值处理（使用winsorize(x, std=4)）
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进行适当的标准化（normalize()或zscore()）
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4.4 行业轮动特异性
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必须包含行业维度处理（group_*函数）
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体现行业间传导、轮动、分化逻辑
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考虑行业相对强弱与绝对动量的结合
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五、表达式构建示例框架
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示例1：行业注意力传导因子
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text
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经济逻辑：捕捉强势行业对弱势行业的注意力传导效应，测量追随行业对领导行业信号的响应速度和强度。
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组件分解：
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1. 识别领导行业：过去5日行业动量排名前30%
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2. 测量响应强度：自身收益率与领导行业收益率的滞后相关性
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3. 调整响应延迟：根据成交量调整，大成交量股票响应更快
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4. 行业相对位置：在自身行业内的动量排名
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示例2：摩擦差异化的动量因子
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text
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经济逻辑：在高摩擦（低流动性）股票中寻找未被充分消化的动量，在低摩擦股票中寻找快速衰减的反转机会。
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组件分解：
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1. 摩擦测量：成交量冲击的价格影响半衰期
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2. 动量计算：不同摩擦环境下的最优动量窗口
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3. 横截面调整：同摩擦水平股票间的相对强弱
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4. 行业中性化：控制行业风格暴露
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示例3：多周期形态共振因子
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text
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经济逻辑：识别短期、中期、长期价格趋势进入同步状态（共振）的股票，这些股票往往有更强的趋势持续性。
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组件分解：
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1. 多周期滤波：5日、20日、60日价格序列
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2. 相位同步测量：不同周期序列方向一致性的时间比例
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3. 共振强度：同步期的动量加速度
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4. 行业调整：与行业共振状态的相对差异
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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)
|
|
Description: Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
|
|
Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
|
|
Description: Returns various parameters related to regression function
|
|
Operator: ts_scale(x, d, constant = 0)
|
|
Description: Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
|
|
Operator: ts_std_dev(x, d)
|
|
Description: Returns standard deviation of x for the past d days
|
|
Operator: ts_step(1)
|
|
Description: Returns days' counter
|
|
Operator: ts_sum(x, d)
|
|
Description: Sum values of x for the past d days.
|
|
Operator: ts_zscore(x, d)
|
|
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
|
|
Operator: normalize(x, useStd = false, limit = 0.0)
|
|
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
|
|
Operator: quantile(x, driver = gaussian, sigma = 1.0)
|
|
Description: Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector
|
|
Operator: rank(x, rate=2)
|
|
Description: Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0
|
|
Operator: scale(x, scale=1, longscale=1, shortscale=1)
|
|
Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
|
|
Operator: winsorize(x, std=4)
|
|
Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
|
|
Operator: zscore(x)
|
|
Description: Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
|
|
Operator: vec_avg(x)
|
|
Description: Taking mean of the vector field x
|
|
Operator: vec_sum(x)
|
|
Description: Sum of vector field x
|
|
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
|
|
Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
|
|
Operator: trade_when(x, y, z)
|
|
Description: Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition
|
|
Operator: group_backfill(x, group, d, std = 4.0)
|
|
Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
|
|
Operator: group_mean(x, weight, group)
|
|
Description: All elements in group equals to the mean
|
|
Operator: group_neutralize(x, group)
|
|
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
|
|
Operator: group_rank(x, group)
|
|
Description: Each elements in a group is assigned the corresponding rank in this group
|
|
Operator: group_scale(x, group)
|
|
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
|
|
Operator: group_zscore(x, group)
|
|
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
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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: pcr_oi_all
|
|
DataFieldDescription: Ratio of put open interest to call open interest for all maturities on stock's options.
|
|
DataField: pcr_vol_270
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 270 days in the future.
|
|
DataField: put_breakeven_180
|
|
DataFieldDescription: Price at which a stock's put options with expiration 180 days in the future break even based on its recent bid/ask mean.
|
|
DataField: call_breakeven_180
|
|
DataFieldDescription: Price at which a stock's call options with expiration 180 days in the future break even based on its recent bid/ask mean.
|
|
DataField: pcr_vol_60
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 60 days in the future.
|
|
DataField: option_breakeven_120
|
|
DataFieldDescription: Price at which a stock's options with expiration 120 days in the future break even based on its recent bid/ask mean.
|
|
DataField: pcr_oi_60
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 60 days in the future.
|
|
DataField: pcr_oi_720
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.
|
|
DataField: pcr_vol_180
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
|
|
DataField: put_breakeven_60
|
|
DataFieldDescription: Price at which a stock's put options with expiration 60 days in the future break even based on its recent bid/ask mean.
|
|
DataField: call_breakeven_270
|
|
DataFieldDescription: Price at which a stock's call options with expiration 270 days in the future break even based on its recent bid/ask mean.
|
|
DataField: pcr_oi_120
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 120 days in the future.
|
|
DataField: 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: put_breakeven_150
|
|
DataFieldDescription: Price at which a stock's put options with expiration 150 days in the future break even based on its recent bid/ask mean.
|
|
DataField: forward_price_1080
|
|
DataFieldDescription: Forward price at 1080 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: 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_150
|
|
DataFieldDescription: Forward price at 150 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: option_breakeven_720
|
|
DataFieldDescription: Price at which a stock's options with expiration 720 days in the future break even based on its recent bid/ask mean.
|
|
DataField: put_breakeven_360
|
|
DataFieldDescription: Price at which a stock's put options with expiration 360 days in the future break even based on its recent bid/ask mean.
|
|
DataField: 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.
|
|
DataField: pcr_vol_150
|
|
DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 150 days in the future.
|
|
DataField: pcr_oi_180
|
|
DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 180 days in the future.
|
|
DataField: call_breakeven_150
|
|
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.
|
|
DataField: put_breakeven_120
|
|
DataFieldDescription: Price at which a stock's put options with expiration 120 days in the future break even based on its recent bid/ask mean.
|
|
DataField: option_breakeven_20
|
|
DataFieldDescription: Price at which a stock's options with expiration 20 days in the future break even based on its recent bid/ask mean.
|
|
DataField: option_breakeven_90
|
|
DataFieldDescription: Price at which a stock's options with expiration 90 days in the future break even based on its recent bid/ask mean.
|
|
DataField: forward_price_270
|
|
DataFieldDescription: Forward price at 270 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
|
|
DataField: option_breakeven_180
|
|
DataFieldDescription: Price at which a stock's options with expiration 180 days in the future break even based on its recent bid/ask mean.
|
|
DataField: put_breakeven_20
|
|
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.
|
|
DataField: call_breakeven_60
|
|
DataFieldDescription: Price at which a stock's call options with expiration 60 days in the future break even based on its recent bid/ask mean.
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|
DataField: fnd6_cptmfmq_atq
|
|
DataFieldDescription: Assets - Total
|
|
DataField: fnd6_newqv1300_aoq
|
|
DataFieldDescription: Assets - Other - Total
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|
DataField: fnd6_newqv1300_ibadjq
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|
DataFieldDescription: Income Before Extraordinary Items - Adjusted for Common Stock Equivalents
|
|
DataField: fnd6_newqeventv110_invwipq
|
|
DataFieldDescription: Inventory - Work in Process
|
|
DataField: fnd6_newqeventv110_ciderglq
|
|
DataFieldDescription: Comprehensive Income - Derivative Gains/Losses
|
|
DataField: fnd6_newqeventv110_acoq
|
|
DataFieldDescription: Current Assets - Other - Total
|
|
DataField: fnd6_newqv1300_rdipq
|
|
DataFieldDescription: In Process R&D
|
|
DataField: fnd6_newqeventv110_xopt12
|
|
DataFieldDescription: Implied Option Expense - 12mm
|
|
DataField: fnd6_spce
|
|
DataFieldDescription: S&P Core Earnings
|
|
DataField: fnd6_prch
|
|
DataFieldDescription: Price High - Annual
|
|
DataField: fnd6_cptnewqv1300_lctq
|
|
DataFieldDescription: Current Liabilities - Total
|
|
DataField: cash_st
|
|
DataFieldDescription: Cash and Short-Term Investments
|
|
DataField: fnd6_newa1v1300_lse
|
|
DataFieldDescription: Liabilities and Stockholders Equity - Total
|
|
DataField: fnd6_aldo
|
|
DataFieldDescription: Long-term Assets of Discontinued Operations
|
|
DataField: fnd6_newa1v1300_che
|
|
DataFieldDescription: Cash and Short-Term Investments
|
|
DataField: invested_capital
|
|
DataFieldDescription: Invested Capital - Total - Quarterly
|
|
DataField: fnd6_cptnewqeventv110_actq
|
|
DataFieldDescription: Current Assets - Total
|
|
DataField: fnd6_newa2v1300_opeps
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|
DataFieldDescription: Earnings Per Share from Operations
|
|
DataField: fnd6_tstkc
|
|
DataFieldDescription: Treasury Stock - Common
|
|
DataField: fnd6_newqeventv110_glcedq
|
|
DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS
|
|
DataField: fnd6_newqv1300_tstkq
|
|
DataFieldDescription: Treasury Stock - Total (All Capital)
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|
DataField: fnd6_aqc
|
|
DataFieldDescription: Acquisitions
|
|
DataField: fnd6_newqeventv110_cshiq
|
|
DataFieldDescription: Common Shares Issued
|
|
DataField: fnd6_newqeventv110_cshoq
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|
DataFieldDescription: Common Shares Outstanding
|
|
DataField: fnd6_newqeventv110_tfvlq
|
|
DataFieldDescription: Total Fair Value Liabilities
|
|
DataField: fnd6_newqv1300_icaptq
|
|
DataFieldDescription: Invested Capital - Total - Quarterly
|
|
DataField: fnd6_eventv110_dd1q
|
|
DataFieldDescription: Long Term Debt Due in 1 Year
|
|
DataField: fnd6_acdo
|
|
DataFieldDescription: Current Assets of Discontinued Operations
|
|
DataField: fnd6_cptmfmq_oibdpq
|
|
DataFieldDescription: Operating Income Before Depreciation - Quarterly
|
|
DataField: fnd6_cptmfmq_opepsq
|
|
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: anl4_afv4_div_std
|
|
DataFieldDescription: Dividend per share - standard deviation of estimations
|
|
DataField: goodwill_min_guidance_qtr
|
|
DataFieldDescription: Minimum guidance value for Total Goodwill
|
|
DataField: sales_estimate_median_value
|
|
DataFieldDescription: Sales - Median value among forecasts
|
|
DataField: anl4_netprofita_low
|
|
DataFieldDescription: Adjusted net income - the lowest estimation
|
|
DataField: sales_estimate_minimum
|
|
DataFieldDescription: Sales - The lowest estimation
|
|
DataField: anl4_qf_az_div_median
|
|
DataFieldDescription: Dividend per share - median of estimations
|
|
DataField: anl4_dez1basicqfv4_preest
|
|
DataFieldDescription: The previous estimation of finanicial item
|
|
DataField: anl4_gric_flag
|
|
DataFieldDescription: Gross income - forecast type (revision/new/...)
|
|
DataField: min_capital_expenditure_guidance
|
|
DataFieldDescription: Minimum guidance value for Capital Expenditures
|
|
DataField: anl4_gric_value
|
|
DataFieldDescription: Gross income- announced financial value
|
|
DataField: max_customized_eps_guidance
|
|
DataFieldDescription: The maximum guidance value for custom earnings per share on an annual basis.
|
|
DataField: min_funds_from_operations_guidance
|
|
DataFieldDescription: Funds from operation - minimum guidance value for annual period
|
|
DataField: est_netdebt
|
|
DataFieldDescription: Net debt - mean of estimations
|
|
DataField: anl4_qfv4_div_number
|
|
DataFieldDescription: Dividend - number of estimations
|
|
DataField: operating_profit_before_depr_amort_max_guidance_qtr
|
|
DataFieldDescription: Max guidance value for Earnings before interest, taxes, depreciation and amortization
|
|
DataField: total_assets_reported_value
|
|
DataFieldDescription: Total Assets - actual value
|
|
DataField: anl4_afv4_eps_high
|
|
DataFieldDescription: Earnings per share - The highest estimation
|
|
DataField: anl4_qfd1_az_cfps_number
|
|
DataFieldDescription: Cash Flow Per Share - number of estimations
|
|
DataField: anl4_ptpr_mean
|
|
DataFieldDescription: Reported Pretax income - mean of estimations
|
|
DataField: max_total_goodwill_guidance_2
|
|
DataFieldDescription: The maximum guidance value for Total Goodwill on an annual basis.
|
|
DataField: min_free_cashflow_per_share_guidance
|
|
DataFieldDescription: Free cash flow per share - minimum guidance value
|
|
DataField: anl4_qfv4_cfps_number
|
|
DataFieldDescription: Cash Flow Per Share - number of estimations
|
|
DataField: anl4_cuo1actualqfv110_actual
|
|
DataFieldDescription: Announced financial data
|
|
DataField: anl4_qf_az_cfps_mean
|
|
DataFieldDescription: Cash Flow Per Share - average of estimations
|
|
DataField: sales_estimate_median_quarterly
|
|
DataFieldDescription: Sales - median of estimations
|
|
DataField: anl4_fcfps_low
|
|
DataFieldDescription: Free Cash Flow Per Share - the lowest estimation
|
|
DataField: max_total_assets_guidance
|
|
DataFieldDescription: The maximum guidance value for Total Assets.
|
|
DataField: free_cash_flow_reported_value
|
|
DataFieldDescription: Free cash flow value for the quarter.
|
|
DataField: sales_estimate_count_2
|
|
DataFieldDescription: Number of Sales estimates
|
|
DataField: earnings_per_share_minimum
|
|
DataFieldDescription: Earnings per share - The lowest estimation
|
|
DataField: pv13_4l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_ompetitorgraphrank_hub_rank
|
|
DataFieldDescription: the HITS hub score of competitors
|
|
DataField: pv13_revere_city
|
|
DataFieldDescription: City code
|
|
DataField: pv13_hierarchy_min25_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_ustomergraphrank_page_rank
|
|
DataFieldDescription: the PageRank of customers
|
|
DataField: pv13_revere_term
|
|
DataFieldDescription: Indicates when a sector is the terminal sector (i.e., no sub-sectors)
|
|
DataField: pv13_di_6l
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_only_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min2_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_6l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min30_3000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min30_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min52_2k_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min20_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_new_3l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min10_sector_3000_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_focused_only_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: rel_num_all
|
|
DataFieldDescription: number of the companies whose product overlapped with the instrument
|
|
DataField: pv13_hierarchy_min5_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: single_sector_pureplay_company_count
|
|
DataFieldDescription: Number of companies exclusively operating in a single sector.
|
|
DataField: pv13_r2_liquid_min10_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_rha2_min2_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min10_3k_all_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min22_1000_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_hierarchy_min2_513_sector
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_1l_scibr
|
|
DataFieldDescription: grouping fields
|
|
DataField: pv13_revere_parent
|
|
DataFieldDescription: Code of parent sector
|
|
DataField: historical_volatility_10
|
|
DataFieldDescription: Close-to-close Historical volatility over 10 days
|
|
DataField: implied_volatility_put_120
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
|
|
DataField: implied_volatility_put_150
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
|
|
DataField: implied_volatility_mean_skew_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
|
|
DataField: parkinson_volatility_120
|
|
DataFieldDescription: Parkinson model's historical volatility over 120 days
|
|
DataField: parkinson_volatility_30
|
|
DataFieldDescription: Parkinson model's historical volatility over 30 days
|
|
DataField: implied_volatility_mean_150
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 150 days
|
|
DataField: implied_volatility_call_20
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
|
|
DataField: implied_volatility_mean_skew_20
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
|
|
DataField: parkinson_volatility_20
|
|
DataFieldDescription: Parkinson model's historical volatility over 20 days
|
|
DataField: parkinson_volatility_150
|
|
DataFieldDescription: Parkinson model's historical volatility over 150 days
|
|
DataField: implied_volatility_call_180
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
|
|
DataField: implied_volatility_mean_60
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 60 days
|
|
DataField: implied_volatility_mean_270
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 270 days
|
|
DataField: implied_volatility_call_720
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
|
|
DataField: implied_volatility_mean_180
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 180 days
|
|
DataField: historical_volatility_30
|
|
DataFieldDescription: Close-to-close Historical volatility over 30 days
|
|
DataField: parkinson_volatility_180
|
|
DataFieldDescription: Parkinson model's historical volatility over 180 days
|
|
DataField: implied_volatility_mean_skew_180
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
|
|
DataField: implied_volatility_mean_skew_90
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
|
|
DataField: implied_volatility_mean_90
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 90 days
|
|
DataField: implied_volatility_call_90
|
|
DataFieldDescription: At-the-money option-implied volatility for call Option for 90 days
|
|
DataField: implied_volatility_put_270
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
|
|
DataField: implied_volatility_mean_skew_10
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
|
|
DataField: implied_volatility_put_60
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
|
|
DataField: historical_volatility_60
|
|
DataFieldDescription: Close-to-close Historical volatility over 60 days
|
|
DataField: implied_volatility_mean_360
|
|
DataFieldDescription: At-the-money option-implied volatility mean for 360 days
|
|
DataField: implied_volatility_put_90
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
|
|
DataField: implied_volatility_mean_skew_720
|
|
DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
|
|
DataField: implied_volatility_put_1080
|
|
DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
|
|
DataField: nws12_mainz_4l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
|
|
DataField: nws12_afterhsz_1_minute
|
|
DataFieldDescription: The percent change in price in the first minute following the news release
|
|
DataField: nws12_afterhsz_3l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
|
|
DataField: nws12_prez_epsactual
|
|
DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
|
|
DataField: nws12_mainz_result2
|
|
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
|
|
DataField: nws12_mainz_prev_vol
|
|
DataFieldDescription: Previous day's session volume
|
|
DataField: nws12_afterhsz_02s
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
|
|
DataField: nws12_afterhsz_result2
|
|
DataFieldDescription: Percent change between the price at the time of the news release to the price at the close of the session
|
|
DataField: news_mins_20_pct_dn
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 20 percentage points
|
|
DataField: nws12_afterhsz_4l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
|
|
DataField: news_pct_30sec
|
|
DataFieldDescription: The percent change in price in the 30 seconds following the news release
|
|
DataField: nws12_mainz_tonlow
|
|
DataFieldDescription: Lowest price reached during the session before the time of the news
|
|
DataField: nws12_mainz_eodhigh
|
|
DataFieldDescription: Highest price reached between the time of news and the end of the session
|
|
DataField: nws12_mainz_result1
|
|
DataFieldDescription: Percent change between the price at the time of the news release and the price at the close of the session
|
|
DataField: nws12_mainz_spylast
|
|
DataFieldDescription: Last Price of the SPY at the time of the news
|
|
DataField: nws12_prez_lowexcstddev
|
|
DataFieldDescription: (TONLast - EODLow)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
|
|
DataField: nws12_prez_provider
|
|
DataFieldDescription: index of name of the news provider
|
|
DataField: news_mins_3_pct_dn
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
|
|
DataField: nws12_afterhsz_prev_vol
|
|
DataFieldDescription: Previous day's session volume
|
|
DataField: nws12_mainz_41rta
|
|
DataFieldDescription: 14-day Average True Range
|
|
DataField: nws12_mainz_02l
|
|
DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
|
|
DataField: nws12_mainz_90_min
|
|
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
|
|
DataField: nws12_afterhsz_5_min
|
|
DataFieldDescription: The percent change in price in the first 5 minutes following the news release
|
|
DataField: nws12_allz_reportsess
|
|
DataFieldDescription: Index of Session on which the spreadsheet is reporting
|
|
DataField: nws12_prez_prevwap
|
|
DataFieldDescription: Pre-session volume weighted average price
|
|
DataField: nws12_afterhsz_57s
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
|
|
DataField: news_session_range_pct
|
|
DataFieldDescription: (Session High Price - Session Low Price) / Session Low Price.
|
|
DataField: news_mins_2_pct_dn
|
|
DataFieldDescription: Number of minutes that elapsed before price went down 2 percentage points
|
|
DataField: nws12_mainz_open_vol
|
|
DataFieldDescription: Main open volume
|
|
DataField: news_pct_90min
|
|
DataFieldDescription: The percent change in price in the first 90 minutes following the news release
|
|
DataField: top1000
|
|
DataFieldDescription: 20140630
|
|
DataField: top200
|
|
DataFieldDescription: 20140630
|
|
DataField: top3000
|
|
DataFieldDescription: 20140630
|
|
DataField: top500
|
|
DataFieldDescription: 20140630
|
|
DataField: topsp500
|
|
DataFieldDescription: 20140630
|
|
DataField: rp_nip_earnings
|
|
DataFieldDescription: News impact projection of earnings news
|
|
DataField: rp_nip_society
|
|
DataFieldDescription: News impact projection of society-related news
|
|
DataField: rp_nip_technical
|
|
DataFieldDescription: News impact projection based on technical analysis
|
|
DataField: rp_ess_revenue
|
|
DataFieldDescription: Event sentiment score of revenue news
|
|
DataField: rp_ess_product
|
|
DataFieldDescription: Event sentiment score of product and service-related news
|
|
DataField: nws18_sse
|
|
DataFieldDescription: Sentiment of phrases impacting the company
|
|
DataField: nws18_qcm
|
|
DataFieldDescription: News sentiment of relevant news with high confidence
|
|
DataField: rp_nip_labor
|
|
DataFieldDescription: News impact projection of labor issues news
|
|
DataField: rp_ess_assets
|
|
DataFieldDescription: Event sentiment score of assets news
|
|
DataField: rp_nip_assets
|
|
DataFieldDescription: News impact projection of assets news
|
|
DataField: rp_css_insider
|
|
DataFieldDescription: Composite sentiment score of insider trading news
|
|
DataField: rp_nip_mna
|
|
DataFieldDescription: News impact projection of mergers and acquisitions-related news
|
|
DataField: nws18_qep
|
|
DataFieldDescription: News sentiment based on positive and negative words on global equity
|
|
DataField: nws18_ghc_lna
|
|
DataFieldDescription: Change in analyst recommendation
|
|
DataField: rp_nip_revenue
|
|
DataFieldDescription: News impact projection of revenue news
|
|
DataField: rp_nip_product
|
|
DataFieldDescription: News impact projection of product and service-related news
|
|
DataField: rp_css_credit_ratings
|
|
DataFieldDescription: Composite sentiment score of credit ratings news
|
|
DataField: rp_css_society
|
|
DataFieldDescription: Composite sentiment score of society-related news
|
|
DataField: rp_css_inverstor
|
|
DataFieldDescription: Composite sentiment score of investor relations news
|
|
DataField: rp_nip_legal
|
|
DataFieldDescription: News impact projection of legal news
|
|
DataField: rp_css_dividends
|
|
DataFieldDescription: Composite sentiment score of dividends news
|
|
DataField: rp_ess_price
|
|
DataFieldDescription: Event sentiment score of stock price news
|
|
DataField: rp_css_earnings
|
|
DataFieldDescription: Composite sentiment score of earnings news
|
|
DataField: rp_nip_marketing
|
|
DataFieldDescription: News impact projection of marketing news
|
|
DataField: rp_nip_credit
|
|
DataFieldDescription: News impact projection of credit news
|
|
DataField: rp_css_technical
|
|
DataFieldDescription: Composite sentiment score based on technical analysis
|
|
DataField: rp_ess_ratings
|
|
DataFieldDescription: Event sentiment score of analyst ratings-related news
|
|
DataField: rp_ess_society
|
|
DataFieldDescription: Event sentiment score of society-related news
|
|
DataField: rp_ess_labor
|
|
DataFieldDescription: Event sentiment score of labor issues news
|
|
DataField: rp_css_credit
|
|
DataFieldDescription: Composite sentiment score of credit news
|
|
DataField: fn_comp_options_out_number_a
|
|
DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
|
|
DataField: fn_excess_tax_benefit_from_share_based_comp_fin_activities_q
|
|
DataFieldDescription: Amount of cash inflow from realized tax benefit related to deductible compensation cost reported on the entity's tax return for equity instruments in excess of the compensation cost for those instruments recognized for financial reporting purposes.
|
|
DataField: fnd2_q_seniornotes
|
|
DataFieldDescription: Including the current and noncurrent portions, carrying value as of the balance sheet date of Notes with the highest claim on the assets of the issuer in case of bankruptcy or liquidation (with maturities initially due after 1 year or beyond the operating cycle if longer). Senior note holders are paid off in full before any payments are made to junior note holders.
|
|
DataField: fn_treasury_stock_shares_q
|
|
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: fnd2_dfdtxastxdfdexprssaccrs
|
|
DataFieldDescription: Amount before allocation of valuation allowances of deferred tax asset attributable to deductible temporary differences from reserves and accruals.
|
|
DataField: fn_oth_comp_grants_weighted_avg_grant_date_fair_value_a
|
|
DataFieldDescription: Annual Share-Based Compensation Equity Instruments Other Than Options Nonvested Weighted Average Grant Date Fair Value
|
|
DataField: fnd2_q_flintasamt1expytwo
|
|
DataFieldDescription: Amount of amortization expense for assets, excluding financial assets and goodwill, lacking physical substance with a finite life expected to be recognized during the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fn_income_tax_expense_q
|
|
DataFieldDescription: Income Tax Expense (Benefit)
|
|
DataField: fnd2_ebitfr
|
|
DataFieldDescription: EBIT, Foreign
|
|
DataField: fnd2_a_flintasacmamtzcsrld
|
|
DataFieldDescription: Finite Lived Intangible Assets Accumulated Amortization, Customer Related
|
|
DataField: fn_incremental_shares_attributable_to_share_based_payment_a
|
|
DataFieldDescription: Additional shares included in the calculation of diluted EPS as a result of the potentially dilutive effect of share-based payment arrangements using the treasury stock method.
|
|
DataField: fn_payments_for_repurchase_of_common_stock_a
|
|
DataFieldDescription: Value reported on Cash Flow Statement. May include shares repurchased as part of a buyback plan, as well as shares purchased for employee compensation, etc.
|
|
DataField: fn_liab_fair_val_l1_q
|
|
DataFieldDescription: Liabilities Fair Value, Recurring, Level 1
|
|
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_derivative_notional_amount_q
|
|
DataFieldDescription: Nominal or face amount used to calculate payments on the derivative liability.
|
|
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: fnd2_a_ltrmdmrepoplinytwo
|
|
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 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
|
|
DataField: fn_business_combination_assets_aquired_goodwill_q
|
|
DataFieldDescription: Business Combination, Portion of Purchase Price Allocated to Goodwill
|
|
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_debt_instrument_face_amount_q
|
|
DataFieldDescription: Debt face amount
|
|
DataField: fn_repayments_of_debt_a
|
|
DataFieldDescription: The cash outflow during the period from the repayment of aggregate short-term and long-term debt. Excludes payment of capital lease obligations.
|
|
DataField: fnd2_unremittedfrer
|
|
DataFieldDescription: Unremitted Foreign Earnings
|
|
DataField: fn_oth_income_loss_derivatives_qualifying_as_hedges_of_tax_q
|
|
DataFieldDescription: Amount after tax and reclassification adjustments, of increase (decrease) in accumulated gain (loss) from derivative instruments designated and qualifying as the effective portion of cash flow hedges and an entity's share of an equity investee's increase (decrease) in deferred hedging gain (loss).
|
|
DataField: fnd2_q_ptoacqbnsesg
|
|
DataFieldDescription: The cash outflow associated with the acquisition of business during the period. The cash portion only of the acquisition price.
|
|
DataField: fnd2_dbplanbnfpaid_ast
|
|
DataFieldDescription: The amount of payments made for which participants are entitled under a pension plan, including pension benefits, death benefits, and benefits due on termination of employment. Also includes payments made under a postretirement benefit plan, including prescription drug benefits, health care benefits, life insurance benefits, and legal, educational and advisory services. This item represents a periodic decrease to the plan obligations and a decrease to plan assets.
|
|
DataField: fn_taxes_payable_q
|
|
DataFieldDescription: Carrying value as of the balance sheet date of obligations incurred and payable for statutory income, sales, use, payroll, excise, real, property and other taxes. 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_def_tax_assets_liab_net_q
|
|
DataFieldDescription: Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.
|
|
DataField: fnd2_a_inventoryfinishedgoods
|
|
DataFieldDescription: Amount before valuation and LIFO reserves of completed merchandise or goods expected to be sold within 1 year or operating cycle, if longer.
|
|
DataField: fnd2_a_dbplctrbyemp
|
|
DataFieldDescription: Amount of contributions made by the employer to defined benefit plans.
|
|
DataField: fn_assets_fair_val_l3_q
|
|
DataFieldDescription: Asset Fair Value, Recurring, Level 3
|
|
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 =======================================
|
|
|
|
|