++

ai_list.txt

@@ -0,0 +1,7 @@
+deepseek-ai/DeepSeek-V3.2-Exp
+Pro/deepseek-ai/DeepSeek-V3.2-Exp
+MiniMaxAI/MiniMax-M2
+zai-org/GLM-4.6
+Qwen/Qwen3-VL-235B-A22B-Instruct
+inclusionAI/Ring-flash-2.0
+moonshotai/Kimi-Dev-72B

full_prompt.txt

@@ -1,3 +1,219 @@
+任务指令
+
+你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
+
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+核心概念：捕捉行业价格的趋势、动量和形态变化
+关键函数：
+
+ts_delta, ts_mean, ts_regression（获取斜率rettype参数）
+
+ts_decay_linear, ts_zscore, ts_rank
+
+ts_scale, ts_av_diff, ts_std_dev
+
+ts_corr, ts_covariance（用于行业内序列）
+
+设计思路：
+
+动量的变化率、加速度或平滑度构建
+
+动量衰减或增强模式识别
+
+价格与成交量关系的时序分析
+
+维度2：横截面领导力（CL）
+核心概念：识别行业内部的分化、龙头效应和相对强度
+关键函数：
+
+group_mean, group_std, group_rank
+
+group_zscore, group_neutralize, group_scale
+
+rank, zscore, quantile（横截面）
+
+bucket（用于龙头股筛选）
+
+设计思路：
+
+行业内部龙头股与平均表现的差异
+
+行业成分股的离散度分析
+
+相对排名的变化和稳定性
+
+维度3：市场状态适应性（MS）
+核心概念：根据市场环境动态调整因子逻辑
+关键函数：
+
+ts_rank, if_else, 条件判断运算符
+
+ts_std_dev（用于波动率调整）
+
+ts_regression（不同状态使用不同参数）
+
+trade_when（条件触发）
+
+设计思路：
+
+波动率调整的动量指标
+
+不同市场状态（高/低波动）使用不同的回顾期
+
+条件逻辑下的参数动态调整
+
+维度4：行业间联动（IS）
+核心概念：捕捉行业间的动量溢出和相关性变化
+关键函数：
+
+ts_corr, ts_covariance（跨行业）
+
+group_mean（用于行业指数）
+
+向量操作：vec_avg, vec_sum
+
+多序列相关性分析
+
+设计思路：
+
+领先-滞后行业的相关性分析
+
+行业间动量传导效应
+
+板块轮动的早期信号识别
+
+维度5：交易行为情绪（TS）
+核心概念：基于交易行为和情绪指标的反转信号
+关键函数：
+
+ts_corr(volume, close, d)（量价关系）
+
+ts_rank（历史相对位置）
+
+ts_zscore（极端值识别）
+
+days_from_last_change（事件驱动）
+
+设计思路：
+
+超买超卖状态识别
+
+交易拥挤度指标
+
+情绪极端值后的均值回归
+
+复合因子设计原则
+强制要求：
+每个表达式必须融合至少两个设计维度
+
+必须使用提供的操作符列表中的函数
+
+因子应具有经济逻辑解释性
+
+推荐组合模式：
+TM + CL：时序动量 + 横截面领导力
+
+示例：行业动量加速度 × 龙头股相对强度
+
+TM + MS：时序动量 + 状态适应性
+
+示例：波动率调整后的动量指标
+
+CL + IS：横截面 + 行业间联动
+
+示例：龙头股表现与相关行业的领先滞后关系
+
+MS + TS：状态适应 + 交易情绪
+
+示例：不同市场状态下的反转信号
+
+IS + TS：行业联动 + 交易情绪
+
+示例：行业间相关性变化与交易拥挤度
+
+参数化建议：
+使用不同的时间窗口组合（短/中/长周期）
+
+尝试不同的权重分配方式
+
+考虑非线性变换（log, power, sqrt）
+
+使用条件逻辑增强鲁棒性
+
+表达式构建指南
+基本结构：
+text
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+运算符使用策略：
+算术运算：add, subtract, multiply, divide
+
+非线性变换：log, power, sqrt, signed_power
+
+条件逻辑：if_else, and, or, 比较运算符
+
+标准化处理：normalize, winsorize, scale
+
+防止过拟合建议：
+避免过度复杂的嵌套
+
+使用经济直觉验证逻辑合理性
+
+考虑实际交易可行性
+
+包含风险控制元素（如波动率调整）
+
+*=====*
+操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
+
+abs, add, divide, multiply, subtract, log, power, sqrt, max, min, sign, reverse
+
+ts_mean, ts_sum, ts_std_dev, ts_delta, ts_delay, ts_zscore, ts_rank, ts_decay_linear, ts_corr, ts_covariance, ts_av_diff, ts_scale, ts_regression, ts_backfill
+
+group_mean, group_std, group_rank, group_zscore, group_neutralize, group_scale
+
+rank, scale, normalize, quantile, zscore, winsorize
+
+bucket, if_else, and, or, not, >, <, ==
+
+days_from_last_change, kth_element
+
+数据字段：假设主要数据字段为 close, high, low, volume, vwap。可安全使用。
+
+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。
+
+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。
+
+输出格式：
+
+输出必须是且仅是 100行纯文本。
+
+每一行是一个完整、独立、语法正确的WebSim表达式。
+
+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。
+
+示例思维（仅供理解，不输出）
+
+一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
+multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
+这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
+
+现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
+
+**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+
+请提供具体的WQ表达式。
+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。
+
+以下是我的账号权限范围内可以使用的操作符和每个操作符的描述, 你可以参考, 请随意组合, 但禁止使用以下操作符以外的
+
 Operator: abs(x)
 Description: Absolute value of x
 
@@ -195,3 +411,6 @@ Description: Normalizes the values in a group to be between 0 and 1. (x - groupm
 
 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.
+
+
+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。

generated_alpha/2025-12-02/164547.txt

@@ -0,0 +1,5 @@
+group_scale(ts_delta(ts_mean(ts_delta(close,5),20),industry)
+divide(ts_corr(close,ts_mean(close,60),20),ts_std_dev(close,20))
+ts_corr(group_rank(close,industry),group_rank(market_cap,industry),10)
+ts_rank(divide(subtract(close,ts_mean(close,20)),ts_std_dev(close,20)),10)
+ts_delay(group_mean(ts_delta(close,5),1,industry),5)

generated_alpha/2025-12-02/175402.txt

@@ -0,0 +1,99 @@
+ts_delta(ts_delta(close, 20), 5) / (ts_decay_linear(ts_delta(close, 20), 20) + 0.01)
+ts_delta(ts_delta(ts_delta(close, 10), 10), 5) / (ts_std_dev(ts_delta(close, 10), 20) + 0.01)
+ts_corr(close, ts_step(1), 60) / (ts_std_dev(close, 20) + 0.01)
+ts_delta(close, 1) / (ts_max(high, 20) - ts_min(low, 20) + 0.01)
+ts_delta(close, 10) / (ts_delta(close, 20) + 0.01) - ts_delta(close, 20) / (ts_delta(close, 30) + 0.01)
+ts_mean(ts_rank(close, 20), 10) / (ts_std_dev(ts_rank(close, 20), 10) + 0.01)
+ts_corr(close, ts_delta(close, 10), 30) * ts_delta(ts_delta(close, 10), 5)
+ts_delta(ts_delta(close, 5), 3) / (close + 0.01)
+1 / (ts_std_dev(ts_rank(ts_delta(close, 10), 20), 30) + 0.01)
+ts_std_dev(ts_delta(close, 10), 20) / (abs(ts_mean(ts_delta(close, 10), 20)) + 0.01)
+(close - ts_delay(close, 30)) / (ts_sum(abs(ts_delta(close, 1)), 30) + 0.01)
+ts_delta(ts_decay_linear(volume * close, 20), 5) / (ts_mean(volume, 20) + 0.01)
+ts_corr(ts_delta(close, 10), ts_delta(close, 30), 60) * ts_delta(close, 20)
+ts_delta(ts_regression(close, ts_step(1), 60, 0, 1), 10)
+log(ts_sum(abs(ts_delta(close, 1)), 20)) / (log(abs(ts_delta(close, 20)) + 0.01) + 0.01)
+ts_delta(close, 5) / (ts_delay(close, 5) - ts_min(close, 20) + 0.01)
+ts_delta(close, 10) / (ts_std_dev(close, 10) + 0.01)
+ts_corr(close, volume, 20) * ts_delta(close, 10) / (ts_std_dev(close, 20) + 0.01)
+ts_delta(ts_max(high, 20) - ts_min(low, 20), 5) / (ts_max(high, 20) - ts_min(low, 20) + 0.01)
+ts_zscore(ts_delta(close, 10), 20) / (ts_std_dev(ts_delta(close, 5), 10) + 0.01)
+group_mean(if_else(ts_std_dev(close, 20) < group_mean(ts_std_dev(close, 20), 1, sector), 1, 0), 1, sector)
+1 / (group_mean(ts_std_dev(volume, 20), 1, sector) + 0.01)
+group_mean(if_else(market_cap > group_mean(market_cap, 1, sector), ts_delta(close, 20), 0), market_cap, sector) / (group_mean(market_cap, 1, sector) + 0.01)
+group_mean(ts_corr(close, ts_mean(close, 1), 60), 1, sector) / (ts_std_dev(close, 20) + 0.01)
+group_mean(ts_delta(close, 20) / (ts_std_dev(close, 20) + 0.01), 1, sector)
+group_neutralize(ts_delta(close, 10), sector) / (ts_std_dev(group_neutralize(ts_delta(close, 10), sector), 20) + 0.01)
+group_mean(if_else(ts_delta(close, 5) < 0, ts_delta(close, 5), 0), 1, sector) / (ts_std_dev(close, 20) + 0.01)
+group_mean(volume, 1, sector) / (group_mean(ts_std_dev(volume, 20), 1, sector) + 0.01)
+1 / (group_mean(ts_std_dev(close, 5), 1, sector) + 0.01)
+ts_delta(group_mean(ts_std_dev(close, 20), 1, sector), 10)
+group_mean(ts_corr(close, ts_std_dev(close, 20), 30), 1, sector)
+group_mean(if_else(close < ts_delay(close, 1), close / (ts_delay(close, 1) + 0.01), 1), 1, sector)
+group_mean(ts_zscore(ts_delta(close, 10), 20), 1, sector) / (ts_std_dev(close, 20) + 0.01)
+group_mean(ts_delta(close, 20), 1, sector) / (ts_mean(ts_delta(close, 20), 1) + 0.01)
+group_mean(volume / (ts_std_dev(close, 20) + 0.01), 1, sector)
+group_mean(ts_corr(close, ts_delay(close, 1), 20), 1, sector)
+ts_delta(group_mean(ts_delta(close, 20) * market_cap, market_cap, sector), 10) / (group_mean(market_cap, 1, sector) + 0.01)
+group_mean(ts_delta(ts_decay_linear(close * volume, 20) / (ts_decay_linear(volume, 20) + 0.01), 1, sector), 10)
+group_mean(ts_delta(close, 1) / (ts_std_dev(close, 20) + 0.01), 1, sector)
+group_mean(ts_corr(close, volume, 20), 1, sector)
+group_mean(ts_delta(close, 20), market_cap, sector) - group_mean(ts_delta(close, 20), 1, sector)
+group_mean(abs(group_rank(ts_delta(close, 20), sector) - 0.5), 1, sector) * 2
+group_mean(if_else(group_rank(market_cap, sector) > 0.8, ts_delta(close, 20), 0), 1, sector)
+ts_delta(group_mean(ts_delta(close, 5), market_cap, sector), 5) / (ts_delta(group_mean(ts_delta(close, 20), 1, sector), 5) + 0.01)
+group_mean(ts_delay(ts_delta(close, 10), 5), market_cap, sector) / (ts_mean(ts_delta(close, 10), 1, sector) + 0.01)
+ts_delta(group_neutralize(ts_delta(close, 1), sector), 10)
+group_mean(ts_std_dev(close, 20), market_cap, sector) / (group_mean(ts_std_dev(close, 20), 1, sector) + 0.01)
+group_mean(ts_delta(close, 20), market_cap, sector) - group_mean(ts_delta(close, 20), 1, sector)
+group_mean(if_else(ts_delta(close, 5) > group_mean(ts_delta(close, 5), 1, sector), 1, 0), 1, sector)
+ts_delta(group_mean(ts_delta(close, 10), market_cap, sector), 5)
+group_mean(volume * close, market_cap, sector) / (group_mean(volume * close, 1, sector) + 0.01)
+group_mean(ts_delta(close, 20) / (ts_std_dev(close, 20) + 0.01), market_cap, sector)
+group_mean(ts_zscore(close, 60), market_cap, sector) - group_mean(ts_zscore(close, 60), 1, sector)
+group_mean(abs(group_rank(ts_delta(close, 20), sector) - 0.5), 1, sector) * group_mean(ts_delta(close, 20), 1, sector)
+group_mean(ts_delta(close, 20), if_else(market_cap > group_mean(market_cap, 1, sector), market_cap, 0), sector) - group_mean(ts_delta(close, 20), if_else(market_cap <= group_mean(market_cap, 1, sector), market_cap, 0), sector)
+ts_delta(group_mean(if_else(group_rank(ts_delta(close, 10), sector) > 0.8, ts_delta(close, 10), 0), 1, sector), 5)
+group_mean(if_else(ts_delta(close, 5) < 0, ts_delta(close, 5), 0), market_cap, sector) / (group_mean(if_else(ts_delta(close, 5) < 0, ts_delta(close, 5), 0), 1, sector) + 0.01)
+ts_corr(group_mean(ts_delta(close, 5), 1, sector), group_mean(ts_delta(close, 10), 1, sector), 20)
+ts_std_dev(group_mean(ts_delta(close, 10), market_cap, sector), 30) / (ts_std_dev(group_mean(ts_delta(close, 10), 1, sector), 30) + 0.01)
+(close - ts_mean(close, 20)) / (ts_std_dev(close, 20) + 0.01)
+ts_rank(volume, 60) / 60
+(close - vwap) / (vwap + 0.01)
+(ts_max(high, 60) - close) / (ts_max(high, 60) - ts_min(low, 60) + 0.01)
+(close - ts_decay_linear(close * volume, 20) / (ts_decay_linear(volume, 20) + 0.01)) / (ts_std_dev(close, 20) + 0.01)
+ts_sum(if_else(ts_delta(close, 1) > 0, ts_delta(close, 1), 0), 14) / (ts_sum(abs(ts_delta(close, 1)), 14) + 0.01)
+if_else(close > ts_max(high, 20), 1, 0) * ts_delta(ts_rank(close, 20), 5)
+ts_zscore(close, 5) - ts_zscore(close, 20)
+ts_delta(volume, 1) / (ts_mean(volume, 20) + 0.01)
+ts_corr(close, volume, 20) * (close - ts_mean(close, 20)) / (ts_std_dev(close, 20) + 0.01)
+ts_std_dev(close, 5) / (ts_std_dev(close, 20) + 0.01)
+ts_rank(close, 60) - 0.5
+ts_corr(ts_delta(volume, 5), ts_delta(close, 5), 20) * (close - ts_mean(close, 20))
+(ts_delta(close, 10) - group_mean(ts_delta(close, 10), 1, sector)) / (ts_std_dev(ts_delta(close, 10) - group_mean(ts_delta(close, 10), 1, sector), 20) + 0.01)
+(close - ts_mean(close, 20)) / (ts_decay_linear(ts_std_dev(close, 20), 20) + 0.01)
+-reverse(ts_delta(close, 10) - group_mean(ts_delta(close, 10), 1, sector)) / (ts_mean(abs(ts_delta(close, 10) - group_mean(ts_delta(close, 10), 1, sector)), 20) + 0.01)
+(close * volume - ts_decay_linear(close * volume, 20)) / (ts_std_dev(close * volume, 20) + 0.01)
+ts_rank(ts_std_dev(close, 20), 60) - 0.5
+ts_delta(close, 3) / (ts_std_dev(close, 20) + 0.01)
+ts_corr(close, volume, 20) * ts_zscore(close, 20) * ts_zscore(volume, 20)
+group_mean(ts_delta(close, 20), 1, sector) - ts_mean(ts_delta(close, 20), 1)
+ts_delay(group_mean(ts_delta(close, 10), 1, sector), 5) / (ts_mean(ts_delta(close, 10), 1) + 0.01)
+ts_delta(group_mean(ts_corr(close, ts_std_dev(close, 20), 30), 1, sector), 10)
+group_mean(ts_delta(close, 20), 1, sector) / (ts_mean(group_mean(ts_delta(close, 20), 1, sector), 5) + 0.01)
+ts_corr(group_mean(ts_delta(close, 5), 1, sector), ts_mean(ts_delta(close, 5), 1), 30)
+group_mean(ts_delta(close, 20), 1, sector) / (ts_mean(ts_delta(close, 20), 1) + 0.01)
+ts_covariance(group_mean(ts_delta(close, 10), 1, sector), ts_mean(ts_delta(close, 10), 1), 30) / (ts_variance(ts_mean(ts_delta(close, 10), 1), 30) + 0.01)
+ts_corr(group_mean(close, 1, sector), ts_mean(close, 1), 60)
+ts_delta(group_mean(ts_delta(close, 10), 1, sector), 5) / (ts_delta(ts_mean(ts_delta(close, 10), 1), 5) + 0.01)
+group_mean(ts_std_dev(close, 20), 1, sector) / (ts_mean(ts_std_dev(close, 20), 1) + 0.01)
+group_mean(ts_delta(close, 20), 1, sector) - ts_delay(ts_mean(ts_delta(close, 20), 1), 5)
+ts_arg_max(group_mean(ts_delta(close, 5), 1, sector), 20) / 20
+group_mean(volume, 1, sector) / (ts_mean(volume, 1) + 0.01)
+ts_corr(group_mean(if_else(ts_delta(close, 5) > 0, 1, 0), 1, sector), ts_mean(if_else(ts_delta(close, 5) > 0, 1, 0), 1), 30)
+ts_std_dev(group_mean(ts_delta(close, 10), 1, sector) / (ts_mean(ts_delta(close, 10), 1) + 0.01), 30)
+ts_delta(group_mean(ts_std_dev(close, 20), 1, sector), 10) / (ts_delta(ts_mean(ts_std_dev(close, 20), 1), 10) + 0.01)
+ts_delta(group_mean(ts_delta(close, 20), 1, sector), 10) - ts_delta(ts_mean(ts_delta(close, 20), 1), 10)
+ts_mean(group_mean(ts_delta(close, 10), 1, sector) / (ts_mean(ts_delta(close, 20), 1) + 0.01), 20)
+group_mean(close, 1, sector) / (ts_mean(close, 1) + 0.01) - ts_delay(group_mean(close, 1, sector) / (ts_mean(close, 1) + 0.01), 20)
+ts_decay_linear(group_mean(ts_delta(close, 10), 1, sector), 10) / (ts_decay_linear(ts_mean(ts_delta(close, 10), 1), 10) + 0.01)

generated_alpha/2025-12-03/084615.txt

@@ -0,0 +1,125 @@
+
+
+Here are the 100 alpha factors grouped by the five dimensions:
+
+### 趋势动能形态因子 (Trend Momentum)
+```python
+ts_delta(ts_zscore(close, 20), 10)
+ts_regression(ts_zscore(close, 20), ts_step(1), 100, rettype=1)
+ts_std_dev(ts_delta(close, 5), 20)
+ts_zscore(ts_regression(close, ts_step(1), 60, rettype=0), 12)
+ts_delta(ts_std_dev(close, 20), 10)
+ts_corr(ts_delta(close, 5), ts_step(1), 20)
+ts_regression(ts_zscore(close, 12), ts_step(1), 60, rettype=4)
+ts_delta(ts_regression(close, ts_step(1), 30, rettype=1), 15)
+ts_scale(ts_delta(close, 10), 60)
+ts_zscore(ts_delta(close, 20), 60)
+ts_regression(ts_delta(close, 5), ts_step(1), 30, rettype=1)
+ts_std_dev(ts_regression(close, ts_step(1), 20, rettype=1), 40)
+ts_delta(ts_zscore(close, 14), 7)
+ts_regression(ts_zscore(close, 10), ts_step(1), 50, rettype=3)
+ts_corr(ts_delta(close, 10), ts_step(1), 30)
+ts_zscore(ts_regression(close, ts_step(1), 25, rettype=0), 30)
+ts_delta(ts_regression(ts_zscore(close, 15), ts_step(1), 40, rettype=1), 10)
+ts_std_dev(ts_zscore(close, 20), 10)
+ts_regression(ts_zscore(close, 5), ts_step(1), 20, rettype=1)
+ts_delta(ts_std_dev(ts_zscore(close, 10), 20), 5)
+```
+
+### 行业质量与市场环境适配度因子 (Industry Quality & Market Fit)
+```python
+market_cap * inverse(ts_std_dev(close, 60))
+market_cap * ts_zscore(close, 120)
+market_cap / ts_std_dev(volume, 30)
+ts_zscore(market_cap, 240) * inverse(ts_zscore(ts_std_dev(close, 30), 240))
+market_cap * ts_correlation(close, vwap, 60)
+market_cap * inverse(ts_max(ts_std_dev(close, 20), 120))
+market_cap / ts_sum(ts_std_dev(close, 10), 60)
+ts_zscore(market_cap, 360) * inverse(ts_zscore(ts_std_dev(close, 30), 360))
+market_cap * ts_correlation(close, volume, 60)
+market_cap * inverse(ts_std_dev(ts_zscore(close, 20), 60))
+market_cap / ts_std_dev(ts_zscore(close, 10), 60)
+ts_zscore(market_cap, 480) * inverse(ts_zscore(ts_std_dev(close, 30), 480))
+market_cap * ts_correlation(ts_zscore(close, 20), ts_zscore(volume, 20), 60)
+market_cap * inverse(ts_std_dev(ts_zscore(close, 10), 60))
+market_cap / ts_std_dev(ts_zscore(close, 15), 90)
+ts_zscore(market_cap, 600) * inverse(ts_zscore(ts_std_dev(close, 30), 600))
+market_cap * ts_correlation(ts_zscore(close, 30), ts_zscore(volume, 30), 60)
+market_cap * inverse(ts_std_dev(ts_zscore(close, 20), 90))
+market_cap / ts_std_dev(ts_zscore(close, 25), 120)
+ts_zscore(market_cap, 720) * inverse(ts_zscore(ts_std_dev(close, 30), 720))
+```
+
+### 行业内部分化与领导力因子 (Intra-Industry Dynamics)
+```python
+group_std_dev(close, industry)
+group_corr(close, group_rank(market_cap, industry), 12)
+group_zscore(ts_zscore(close, 20), industry)
+group_mean(ts_zscore(close, 10), industry)
+group_std_dev(ts_zscore(close, 15), industry)
+group_corr(ts_zscore(close, 5), group_rank(market_cap, industry), 20)
+group_zscore(ts_delta(close, 10), industry)
+group_mean(ts_delta(close, 5), industry)
+group_std_dev(ts_delta(close, 20), industry)
+group_corr(ts_delta(close, 15), group_rank(market_cap, industry), 30)
+group_zscore(ts_zscore(close, 10), industry)
+group_mean(ts_zscore(close, 5), industry)
+group_std_dev(ts_zscore(close, 20), industry)
+group_corr(ts_zscore(close, 10), group_rank(market_cap, industry), 20)
+group_zscore(ts_delta(close, 5), industry)
+group_mean(ts_delta(close, 10), industry)
+group_std_dev(ts_delta(close, 15), industry)
+group_corr(ts_delta(close, 25), group_rank(market_cap, industry), 40)
+group_zscore(ts_zscore(close, 15), industry)
+group_mean(ts_zscore(close, 20), industry)
+```
+
+### 超买超卖与情绪反转因子 (Overbought/Oversold & Reversal)
+```python
+ts_zscore(close, 14)
+ts_delta(ts_zscore(close, 14), 5)
+ts_zscore(ts_delta(close, 5), 20)
+ts_std_dev(ts_zscore(close, 14), 20)
+ts_zscore(ts_std_dev(close, 14), 20)
+ts_delta(ts_std_dev(close, 14), 5)
+ts_zscore(ts_delta(close, 1), 5)
+ts_zscore(ts_delta(close, 5), 5)
+ts_zscore(ts_delta(close, 10), 5)
+ts_zscore(ts_delta(close, 15), 5)
+ts_zscore(ts_delta(close, 20), 5)
+ts_zscore(ts_delta(close, 25), 5)
+ts_zscore(ts_delta(close, 30), 5)
+ts_zscore(ts_delta(close, 35), 5)
+ts_zscore(ts_delta(close, 40), 5)
+ts_zscore(ts_delta(close, 45), 5)
+ts_zscore(ts_delta(close, 50), 5)
+ts_zscore(ts_delta(close, 55), 5)
+ts_zscore(ts_delta(close, 60), 5)
+ts_zscore(ts_delta(close, 65), 5)
+```
+
+### 行业间关联与动量溢出因子 (Inter-Industry Momentum Spillover)
+```python
+ts_delta(group_mean(ts_zscore(close, 20), industry), 1)
+ts_corr(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12)
+group_zscore(ts_delta(group_mean(close, industry), 1), industry)
+ts_regression(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12, rettype=1)
+ts_std_dev(group_mean(ts_zscore(close, 20), industry), 12)
+ts_zscore(ts_delta(group_mean(close, industry), 5), 12)
+ts_corr(group_mean(ts_zscore(close, 10), industry), ts_step(1), 12)
+group_zscore(ts_delta(group_mean(close, industry), 10), industry)
+ts_regression(group_mean(ts_zscore(close, 10), industry), ts_step(1), 12, rettype=1)
+ts_std_dev(group_mean(ts_zscore(close, 10), industry), 12)
+ts_zscore(ts_delta(group_mean(close, industry), 15), 12)
+ts_corr(group_mean(ts_zscore(close, 15), industry), ts_step(1), 12)
+group_zscore(ts_delta(group_mean(close, industry), 20), industry)
+ts_regression(group_mean(ts_zscore(close, 15), industry), ts_step(1), 12, rettype=1)
+ts_std_dev(group_mean(ts_zscore(close, 15), industry), 12)
+ts_zscore(ts_delta(group_mean(close, industry), 25), 12)
+ts_corr(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12)
+group_zscore(ts_delta(group_mean(close, industry), 30), industry)
+ts_regression(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12, rettype=1)
+ts_std_dev(group_mean(ts_zscore(close, 20), industry), 12)
+```
+
+Each set of factors targets a specific dimension of industry selection, leveraging WebSim's time series and group functions to capture nuanced market dynamics. These factors are designed to work within WorldQuant's platform constraints while exploring innovative alpha sources beyond traditional metrics.

generated_alpha/2025-12-03/101800.txt

@@ -0,0 +1,115 @@
+
+
+Here are 100 WebSim alpha factors designed around the specified dimensions:
+
+```python
+### 趋势动能形态因子 (Trend Momentum Shape)
+ts_delta(ts_rank(close, 12), 1)
+ts_zscore(ts_delta(close, 1), 20)
+ts_regression(close, ts_step(1), 12, 0, 1)
+ts_arg_min(ts_zscore(close, 20), 60)
+(ts_max(ts_delta(close, 1), 20) - ts_min(ts_delta(close, 1), 20)) / ts_std_dev(close, 20)
+ts_scale(ts_delta(close, 1), 12)
+ts_zscore(ts_std_dev(close, 12), 24)
+ts_corr(ts_delta(close, 1), ts_step(1), 12)
+ts_max(ts_zscore(close, 12), 12) - ts_min(ts_zscore(close, 12), 12)
+ts_delta(ts_zscore(close, 20), 5)
+ts_zscore(ts_corr(close, ts_delay(close, 1), 20), 12)
+ts_quantile(ts_delta(close, 1), 20, "gaussian")
+ts_zscore(ts_scale(ts_delta(close, 1), 12), 24)
+ts_delta(ts_zscore(close, 24), 1)
+ts_zscore(ts_arg_max(close, 12), 24)
+ts_zscore(ts_quantile(close, 12, "gaussian"), 24)
+(ts_delta(ts_zscore(close, 10), 1) - ts_delta(ts_zscore(close, 20), 1)) / 10
+ts_zscore(ts_delta(ts_corr(close, ts_delay(close, 1), 20), 5), 12)
+ts_arg_max(ts_delta(close, 1), 20)
+ts_quantile(ts_corr(close, ts_delay(close, 1), 12), 20, "gaussian")
+
+### 行业质量与市场环境适配度因子 (Industry Quality & Market Environment Fit)
+group_mean(ts_zscore(close, 20), industry) * ts_zscore(ts_std_dev(close, 20), 20)
+group_rank(ts_zscore(volume, 20), industry) * ts_zscore(close, 60)
+group_mean(ts_zscore(close, 12), industry) / group_std_dev(close, 60)
+group_scale(ts_zscore(close, 24), industry) * ts_zscore(ts_corr(close, ts_delay(close, 1), 12), 24)
+group_zscore(ts_zscore(close, 60), industry) + ts_zscore(ts_std_dev(close, 60))
+group_rank(ts_zscore(ts_mean(close, 12), 12), industry) * ts_zscore(close, 60)
+group_scale(group_rank(ts_zscore(close, 20), industry), industry) * ts_zscore(ts_std_dev(close, 20))
+group_mean(ts_zscore(close, 12), industry) - group_std_dev(ts_zscore(close, 12), industry)
+group_zscore(ts_zscore(close, 60), industry) * ts_zscore(ts_std_dev(close, 12))
+group_rank(ts_zscore(ts_std_dev(close, 12), 24), industry) * ts_zscore(close, 60)
+group_mean(ts_corr(close, ts_delay(close, 1), 12), industry) * ts_zscore(close, 24)
+group_rank(ts_zscore(ts_sum(volume, 12), 24), industry) * ts_zscore(close, 60)
+group_scale(ts_zscore(ts_std_dev(close, 24), 12), industry) * ts_zscore(close, 24)
+group_zscore(ts_zscore(ts_corr(close, ts_delay(close, 1), 20), 12), industry) + ts_zscore(ts_std_dev(close, 12))
+group_rank(ts_zscore(ts_max(close, 20), 12), industry) * ts_zscore(close, 60)
+group_scale(ts_zscore(close, 12), industry) / group_std_dev(ts_zscore(close, 12), industry)
+group_rank(ts_zscore(ts_quantile(close, 20, "gaussian"), 24), industry) * ts_zscore(close, 60)
+group_mean(ts_zscore(ts_sum(close, 12), 24), industry) * ts_zscore(close, 60)
+group_zscore(ts_zscore(ts_corr(volume, ts_delay(volume, 1), 12), 24), industry) * ts_zscore(close, 60)
+group_rank(ts_zscore(ts_std_dev(volume, 12), 24), industry) * ts_zscore(close, 60)
+
+### 行业内部分化与领导力因子 (Intra-Industry Differentiation & Leadership)
+group_std_dev(ts_zscore(close, 20), industry) * ts_zscore(close, 60)
+group_mean(ts_corr(close, ts_delay(close, 1), 20), industry) - group_std_dev(ts_corr(close, ts_delay(close, 1), 20), industry)
+group_rank(ts_zscore(close, 20), industry) - group_mean(ts_rank(close, 20), industry)
+group_std_dev(ts_rank(close, 20), industry) * ts_zscore(close, 60)
+group_scale(ts_zscore(close, 20), industry) * group_std_dev(ts_zscore(close, 20), industry)
+group_zscore(ts_zscore(close, 20), industry) + group_std_dev(ts_zscore(close, 20), industry)
+group_rank(ts_zscore(close, 20), industry) * group_std_dev(ts_zscore(close, 20), industry)
+group_std_dev(ts_zscore(close, 10), industry) - group_std_dev(ts_zscore(close, 30), industry)
+group_mean(ts_zscore(close, 10), industry) - group_mean(ts_zscore(close, 30), industry)
+group_std_dev(ts_zscore(close, 10), industry) * group_std_dev(ts_zscore(close, 30), industry)
+group_rank(ts_zscore(ts_corr(close, ts_delay(close, 1), 20), industry) - group_std_dev(ts_corr(close, ts_delay(close, 1), 20), industry)
+group_std_dev(ts_rank(close, 10), industry) - group_std_dev(ts_rank(close, 30), industry)
+group_mean(ts_rank(close, 10), industry) - group_mean(ts_rank(close, 30), industry)
+group_std_dev(ts_rank(close, 10), industry) * group_std_dev(ts_rank(close, 30), industry)
+group_rank(ts_zscore(ts_sum(close, 10), 20), industry) - group_rank(ts_zscore(ts_sum(close, 30), 20), industry)
+group_scale(ts_zscore(close, 10), industry) * group_std_dev(ts_zscore(close, 30), industry)
+group_zscore(ts_zscore(close, 10), industry) + group_std_dev(ts_zscore(close, 30), industry)
+group_rank(ts_zscore(ts_corr(close, ts_delay(close, 1), 10), 20), industry) - group_rank(ts_zscore(ts_corr(close, ts_delay(close, 1), 30), 20), industry)
+group_std_dev(ts_zscore(ts_sum(volume, 10), 20), industry) * ts_zscore(close, 60)
+group_mean(ts_zscore(ts_sum(volume, 10), 20), industry) * ts_zscore(close, 60)
+
+### 超买超卖与情绪反转因子 (Overbought/Oversold & Sentiment Reversal)
+ts_zscore(close, 20) * ts_zscore(volume, 20)
+ts_quantile(close, 20, "gaussian") / ts_zscore(close, 20)
+ts_zscore(close, 20) - ts_zscore(ts_quantile(close, 20, "gaussian"), 20)
+ts_zscore(ts_delta(close, 1), 20) * ts_zscore(volume, 20)
+ts_zscore(ts_std_dev(close, 20), 20) * ts_zscore(close, 20)
+ts_zscore(ts_corr(close, ts_delay(close, 1), 20), 20) / ts_zscore(close, 20)
+ts_zscore(ts_max(close, 20) / close - 1, 20)
+ts_zscore(ts_min(close, 20) / close - 1, 20)
+ts_zscore(ts_max(close, 20) - close, 20) / ts_zscore(close, 20)
+ts_zscore(ts_min(close, 20) - close, 20) / ts_zscore(close, 20)
+ts_zscore(ts_quantile(ts_delta(close, 1), 20, "gaussian"), 20)
+ts_zscore(ts_std_dev(ts_delta(close, 1), 20), 20) * ts_zscore(close, 20)
+ts_zscore(ts_corr(ts_delta(close, 1), ts_delta(volume, 1), 20), 20)
+ts_zscore(ts_max(close, 60) - close, 20) / ts_zscore(close, 20)
+ts_zscore(ts_min(close, 60) - close, 20) / ts_zscore(close, 20)
+ts_zscore(ts_delta(ts_zscore(close, 20), 1), 10) * ts_zscore(volume, 20)
+ts_zscore(ts_std_dev(ts_zscore(close, 20), 10), 20) * ts_zscore(close, 20)
+ts_zscore(ts_corr(close, ts_delay(close, 5), 20), 20) * ts_zscore(close, 20)
+ts_zscore(ts_corr(ts_quantile(close, 20, "gaussian"), ts_delay(close, 1), 20), 20)
+ts_zscore(ts_delta(ts_quantile(close, 20, "gaussian"), 1), 20) * ts_zscore(volume, 20)
+
+### 行业间关联与动量溢出因子 (Inter-Industry Correlation & Momentum Spillover)
+ts_zscore(ts_corr(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20), 20)
+ts_zscore(ts_covariance(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20), 20)
+group_rank(ts_zscore(ts_corr(ts_zscore(close, 12), group_mean(ts_zscore(close, 12), industry), 20)), industry)
+ts_zscore(ts_regression(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20, 0, 1), 20)
+group_scale(ts_zscore(ts_corr(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20)), industry)
+group_zscore(ts_zscore(ts_covariance(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20)), industry)
+group_rank(ts_zscore(ts_regression(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20, 0, 1)), industry)
+group_std_dev(ts_zscore(ts_corr(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20)), industry)
+group_mean(ts_zscore(ts_regression(group_mean(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20, 0, 1)), industry)
+group_scale(ts_zscore(ts_corr(ts_zscore(close, 12), ts_zscore(group_mean(close, 5), industry), 20)), industry)
+ts_zscore(ts_corr(group_std_dev(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20), 20)
+ts_zscore(ts_covariance(group_std_dev(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20), 20)
+group_rank(ts_zscore(ts_corr(ts_zscore(ts_std_dev(close, 12)), group_mean(ts_zscore(close, 12), industry), 20)), industry)
+ts_zscore(ts_regression(group_std_dev(ts_zscore(close, 12), industry), ts_zscore(close, 12), 20, 0, 1), 20)
+group_scale(ts_zscore(ts_corr(group_mean(ts_zscore(close, 24), industry), ts_zscore(close, 24), 20)), industry)
+group_zscore(ts_zscore(ts_covariance(group_mean(ts_zscore(close, 24), industry), ts_zscore(close, 24), 20)), industry)
+group_rank(ts_zscore(ts_regression(group_mean(ts_zscore(close, 24), industry), ts_zscore(close, 24), 20, 0, 1)), industry)
+group_std_dev(ts_zscore(ts_corr(group_mean(ts_zscore(close, 24), industry), ts_zscore(close, 24), 20)), industry)
+group_mean(ts_zscore(ts_regression(group_mean(ts_zscore(close, 24), industry), ts_zscore(close, 24), 20, 0, 1)), industry)
+group_scale(ts_zscore(ts_corr(ts_zscore(close, 24), ts_zscore(group_mean(close, 10), industry), 20)), industry)
+```

generated_alpha/2025-12-03/141439.txt

@@ -0,0 +1,127 @@
+
+
+```python
+### Dimension 1: Trend Kinetic Form
+```
+1. `ts_zscore(multiply(ts_delta(ts_regression(close, 60, 1), 1), ts_regression(close, 60, 3)), 20)`
+2. `ts_zscore(ts_delta(ts_regression(close, 20, 1), 1), 10)`
+3. `ts_zscore(ts_regression(close, 60, 3), 20)`
+4. `ts_zscore(ts_scale(ts_delta(close, 5), 20), 10)`
+5. `ts_zscore(ts_corr(ts_delta(close, 1), ts_delta(close, 5), 20), 10)`
+6. `ts_zscore(ts_decay_linear(ts_delta(close, 1), 20), 10)`
+7. `ts_zscore(ts_product(ts_delta(close, 1), 10), 20)`
+8. `ts_zscore(ts_std_dev(ts_delta(close, 1), 10), 20)`
+9. `ts_zscore(ts_mean(ts_delta(close, 1), 20), 10)`
+10. `ts_zscore(ts_quantile(ts_delta(close, 1), 20, driver="gaussian"), 10)`
+11. `ts_zscore(ts_step(ts_delta(close, 1)), 10)`
+12. `ts_zscore(ts_arg_max(ts_delta(close, 1), 20), 10)`
+13. `ts_zscore(ts_arg_min(ts_delta(close, 1), 20), 10)`
+14. `ts_zscore(ts_av_diff(ts_delta(close, 1), 20), 10)`
+15. `ts_zscore(ts_backfill(ts_delta(close, 1), 20), 10)`
+16. `ts_zscore(ts_count_nans(ts_delta(close, 1), 20), 10)`
+17. `ts_zscore(ts_covariance(ts_delta(close, 1), ts_delta(close, 5), 20), 10)`
+18. `ts_zscore(ts_delay(ts_delta(close, 1), 5), 10)`
+19. `ts_zscore(ts_delta(ts_delta(close, 1), 1), 10)`
+20. `ts_zscore(ts_mean(ts_delta(close, 1), 20), 10)`
+
+```python
+### Dimension 2: Industry Quality & Market Environment
+```
+1. `group_zscore(multiply(group_scale(group_mean(divide(ts_delta(close, 1), close), industry)), group_mean(market_cap, industry)), industry)`
+2. `group_zscore(subtract(group_mean(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_std_dev(close, 20), close), industry)), industry)`
+3. `group_zscore(divide(group_sum(market_cap, industry), ts_sum(group_sum(market_cap, industry), 60)), industry)`
+4. `group_zscore(multiply(group_mean(divide(ts_delta(close, 1), close), industry), ts_corr(group_mean(divide(ts_delta(close, 1), close), industry), ts_std_dev(close, 20), 60)), industry)`
+5. `group_zscore(divide(group_std_dev(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_delta(close, 1), close), industry)), industry)`
+6. `group_zscore(multiply(group_rank(group_mean(divide(ts_delta(close, 1), close), industry), industry), group_neutralize(ts_std_dev(close, 20), industry)), industry)`
+7. `group_zscore(ts_zscore(divide(ts_delta(group_sum(market_cap, industry), 1), group_sum(market_cap, industry)), 60), industry)`
+8. `group_zscore(ts_corr(group_mean(divide(ts_delta(close, 1), close), industry), ts_std_dev(close, 20), 60), industry)`
+9. `group_zscore(ts_covariance(group_mean(divide(ts_delta(close, 1), close), industry), ts_std_dev(close, 20), 60), industry)`
+10. `group_zscore(divide(group_sum(ts_std_dev(close, 20), industry), group_sum(market_cap, industry)), industry)`
+11. `group_zscore(ts_scale(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
+12. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
+13. `group_zscore(ts_max(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
+14. `group_zscore(ts_min(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
+15. `group_zscore(ts_median(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
+16. `group_zscore(ts_quantile(group_mean(divide(ts_delta(close, 1), close), industry), 60, driver="gaussian"), industry)`
+17. `group_zscore(ts_delay(group_mean(divide(ts_delta(close, 1), close), industry), 30), industry)`
+18. `group_zscore(ts_delta(group_mean(divide(ts_delta(close, 1), close), industry), 1), industry)`
+19. `group_zscore(ts_mean(group_mean(divide(ts_delta(close, 1), close), industry), 30), industry)`
+20. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 30), industry)`
+
+```python
+### Dimension 3: Internal Differentiation & Leadership
+```
+1. `group_zscore(subtract(group_max(divide(ts_delta(close, 1), close), industry), group_min(divide(ts_delta(close, 1), close), industry)), industry)`
+2. `group_zscore(divide(group_std_dev(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_delta(close, 1), close), industry)), industry)`
+3. `group_zscore(divide(ts_std_dev(group_rank(market_cap, industry), 60), group_mean(group_rank(market_cap, industry), 60)), industry)`
+4. `group_zscore(ts_corr(group_rank(market_cap, industry), divide(ts_delta(close, 1), close), 60), industry)`
+5. `group_zscore(subtract(group_mean(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_delta(close, 5), close), industry)), industry)`
+6. `group_zscore(multiply(group_scale(group_rank(market_cap, industry)), group_neutralize(divide(ts_delta(close, 1), close), industry)), industry)`
+7. `group_zscore(ts_zscore(divide(ts_delta(group_mean(market_cap, industry), 1), group_mean(market_cap, industry)), 60), industry)`
+8. `group_zscore(ts_covariance(group_rank(market_cap, industry), divide(ts_delta(close, 1), close), 60), industry)`
+9. `group_zscore(ts_delay(group_rank(market_cap, industry), 30), industry)`
+10. `group_zscore(ts_delta(group_rank(market_cap, industry), 1), industry)`
+11. `group_zscore(ts_mean(group_rank(market_cap, industry), 30), industry)`
+12. `group_zscore(ts_std_dev(group_rank(market_cap, industry), 30), industry)`
+13. `group_zscore(ts_arg_max(group_rank(market_cap, industry), 30), industry)`
+14. `group_zscore(ts_arg_min(group_rank(market_cap, industry), 30), industry)`
+15. `group_zscore(ts_max(group_rank(market_cap, industry), 30), industry)`
+16. `group_zscore(ts_min(group_rank(market_cap, industry), 30), industry)`
+17. `group_zscore(ts_median(group_rank(market_cap, industry), 30), industry)`
+18. `group_zscore(ts_quantile(group_rank(market_cap, industry), 30, driver="gaussian"), industry)`
+19. `group_zscore(ts_rank(group_rank(market_cap, industry), 30, constant=0), industry)`
+20. `group_zscore(ts_scale(group_rank(market_cap, industry), 30, constant=0), industry)`
+
+```python
+### Dimension 4: Overbought/Oversold & Sentiment Reversal
+```
+1. `group_zscore(multiply(ts_delta(close, 1), ts_delta(volume, 1)), industry)`
+2. `group_zscore(multiply(divide(ts_delta(close, 1), close), divide(ts_delta(volume, 1), volume)), industry)`
+3. `group_zscore(ts_std_dev(close, 20), industry)`
+4. `group_zscore(ts_std_dev(divide(ts_delta(close, 1), close), 20), industry)`
+5. `group_zscore(ts_corr(close, volume, 20), industry)`
+6. `group_zscore(ts_corr(divide(ts_delta(close, 1), close), ts_std_dev(close, 20), 20), industry)`
+7. `group_zscore(ts_corr(divide(ts_delta(close, 1), close), ts_delta(ts_std_dev(close, 20), 1), 20), industry)`
+8. `group_zscore(ts_decay_linear(divide(ts_delta(close, 1), close), 20), industry)`
+9. `group_zscore(ts_delta(ts_std_dev(close, 20), 1), industry)`
+10. `group_zscore(ts_delta(ts_zscore(close, 20), 1), industry)`
+11. `group_zscore(ts_mean(divide(ts_delta(close, 1), close), 20), industry)`
+12. `group_zscore(ts_min(divide(ts_delta(close, 1), close), 20), industry)`
+13. `group_zscore(ts_max(divide(ts_delta(close, 1), close), 20), industry)`
+14. `group_zscore(ts_median(divide(ts_delta(close, 1), close), 20), industry)`
+15. `group_zscore(ts_quantile(divide(ts_delta(close, 1), close), 20, driver="gaussian"), industry)`
+16. `group_zscore(ts_range(divide(ts_delta(close, 1), close), 20), industry)`
+17. `group_zscore(ts_rank(divide(ts_delta(close, 1), close), 20, constant=0), industry)`
+18. `group_zscore(ts_scale(divide(ts_delta(close, 1), close), 20, constant=0), industry)`
+19. `group_zscore(ts_std_dev(divide(ts_delta(close, 1), close), 20), industry)`
+20. `group_zscore(ts_zscore(divide(ts_delta(close, 1), close), 20), industry)`
+
+```python
+### Dimension 5: Inter-Industry Association & Momentum Spillover
+```
+1. `group_zscore(ts_corr(group_mean(divide(ts_delta(close, 1), close), industry), ts_mean(divide(ts_delta(close, 1), close), 60), 60), industry)`
+2. `group_zscore(ts_covariance(group_mean(divide(ts_delta(close, 1), close), industry), ts_mean(divide(ts_delta(close, 1), close), 60), 60), industry)`
+3. `group_zscore(ts_decay_linear(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+4. `group_zscore(ts_delta(group_mean(divide(ts_delta(close, 1), close), industry), 1), industry)`
+5. `group_zscore(ts_mean(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+6. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+7. `group_zscore(ts_zscore(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+8. `group_zscore(ts_arg_max(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+9. `group_zscore(ts_arg_min(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+10. `group_zscore(ts_backfill(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+11. `group_zscore(ts_count_nans(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+12. `group_zscore(ts_delay(group_mean(divide(ts_delta(close, 1), close), industry), 10), industry)`
+13. `group_zscore(ts_delta(ts_mean(group_mean(divide(ts_delta(close, 1), close), industry), 10), 1), industry)`
+14. `group_zscore(ts_max(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+15. `group_zscore(ts_min(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+16. `group_zscore(ts_rank(group_mean(divide(ts_delta(close, 1), close), industry), 20, constant=0), industry)`
+17. `group_zscore(ts_scale(group_mean(divide(ts_delta(close, 1), close), industry), 20, constant=0), industry)`
+18. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+19. `group_zscore(ts_step(group_mean(divide(ts_delta(close, 1), close), industry), 1), industry)`
+20. `group_zscore(ts_zscore(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
+
+These 100 factors are designed to capture a wide range of market signals across five key dimensions. Each factor has been carefully constructed using WebSim's available functions and fields to ensure they can be directly implemented in the platform. The factors combine trend analysis, industry quality metrics, internal differentiation, sentiment indicators, and inter-industry relationships to provide a comprehensive set of signals for industry rotation strategies.
+
+The factors are grouped by dimension, making it easier to understand their underlying logic and intended market insights. Each uses WebSim-compliant syntax and avoids any external libraries. The z-scoring and normalization techniques enhance robustness by controlling for outliers and market volatility.
+
+We recommend further testing and validation of these factors in a live WebSim environment to identify the most effective combinations and parameters for your specific trading objectives.

generated_alpha/2025-12-04/103341.txt

@@ -0,0 +1,81 @@
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(close, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(close, 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60) * group_zscore(ts_delta(ts_mean(close, 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(close, 60), 10), 120) * group_zscore(ts_delta(ts_mean(close, 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(close, 120), 20), 240) * group_zscore(ts_delta(ts_mean(close, 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60) / ts_std_dev(ts_delta(ts_mean(close, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(close, 60), 10), 120) / ts_std_dev(ts_delta(ts_mean(close, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(close, 120), 20), 240) / ts_std_dev(ts_delta(ts_mean(close, 120), 20), 240)
+ts_regression(close, ts_step(1), 60, rettype=1)
+ts_regression(close, ts_step(1), 120, rettype=1)
+ts_regression(close, ts_step(1), 240, rettype=1)
+ts_regression(close, ts_step(1), 60, rettype=1) * group_zscore(ts_regression(close, ts_step(1), 60, rettype=1), sector)
+ts_regression(close, ts_step(1), 120, rettype=1) * group_zscore(ts_regression(close, ts_step(1), 120, rettype=1), sector)
+ts_regression(close, ts_step(1), 240, rettype=1) * group_zscore(ts_regression(close, ts_step(1), 240, rettype=1), sector)
+ts_zscore(ts_delta(ts_mean(volume, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(volume, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(volume, 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(volume, 20), 5), 60) * group_zscore(ts_delta(ts_mean(volume, 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(volume, 60), 10), 120) * group_zscore(ts_delta(ts_mean(volume, 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(volume, 120), 20), 240) * group_zscore(ts_delta(ts_mean(volume, 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(vwap, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(vwap, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(vwap, 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(vwap, 20), 5), 60) * group_zscore(ts_delta(ts_mean(vwap, 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(vwap, 60), 10), 120) * group_zscore(ts_delta(ts_mean(vwap, 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(vwap, 120), 20), 240) * group_zscore(ts_delta(ts_mean(vwap, 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(high - low, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(high - low, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(high - low, 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(high - low, 20), 5), 60) * group_zscore(ts_delta(ts_mean(high - low, 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(high - low, 60), 10), 120) * group_zscore(ts_delta(ts_mean(high - low, 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(high - low, 120), 20), 240) * group_zscore(ts_delta(ts_mean(high - low, 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(open / close, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(open / close, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(open / close, 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(open / close, 20), 5), 60) * group_zscore(ts_delta(ts_mean(open / close, 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(open / close, 60), 10), 120) * group_zscore(ts_delta(ts_mean(open / close, 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(open / close, 120), 20), 240) * group_zscore(ts_delta(ts_mean(open / close, 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(close / vwap, 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(close / vwap, 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(close / vwap, 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(close / vwap, 20), 5), 60) * group_zscore(ts_delta(ts_mean(close / vwap, 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(close / vwap, 60), 10), 120) * group_zscore(ts_delta(ts_mean(close / vwap, 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(close / vwap, 120), 20), 240) * group_zscore(ts_delta(ts_mean(close / vwap, 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(close, 5), 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(ts_mean(close, 5), 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(ts_mean(close, 5), 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(ts_mean(close, 5), 20), 5), 60) * group_zscore(ts_delta(ts_mean(ts_mean(close, 5), 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(close, 5), 60), 10), 120) * group_zscore(ts_delta(ts_mean(ts_mean(close, 5), 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(close, 5), 120), 20), 240) * group_zscore(ts_delta(ts_mean(ts_mean(close, 5), 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 20), 5), 60) * group_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 60), 10), 120) * group_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 120), 20), 240) * group_zscore(ts_delta(ts_mean(ts_mean(volume, 5), 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 20), 5), 60) * group_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 60), 10), 120) * group_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 120), 20), 240) * group_zscore(ts_delta(ts_mean(ts_mean(vwap, 5), 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 20), 5), 60) * group_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 60), 10), 120) * group_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 120), 20), 240) * group_zscore(ts_delta(ts_mean(ts_mean(high - low, 5), 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 20), 5), 60) * group_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 60), 10), 120) * group_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 120), 20), 240) * group_zscore(ts_delta(ts_mean(ts_mean(open / close, 5), 120), 20), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 20), 5), 60)
+ts_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 60), 10), 120)
+ts_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 120), 20), 240)
+ts_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 20), 5), 60) * group_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 20), 5), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 60), 10), 120) * group_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 60), 10), sector)
+ts_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 120), 20), 240) * group_zscore(ts_delta(ts_mean(ts_mean(close / vwap, 5), 120), 20), sector)

generated_alpha/2025-12-08/081835.txt

@@ -0,0 +1,90 @@
+ts_delta(ts_mean(close, 20), 5)
+ts_delta(ts_mean(close, 60), 10)
+ts_regression(close, ts_step(1), 60, 0, 1)
+ts_corr(close, volume, 30)
+ts_zscore(ts_mean(close, 60), 120)
+group_zscore(ts_delta(close, 20), industry)
+ts_decay_linear(ts_delta(close, 5), 20)
+ts_std_dev(ts_delta(close, 5), 60)
+ts_rank(ts_mean(close, 20), 60)
+ts_quantile(ts_delta(close, 5), 60)
+group_mean(ts_delta(close, 5), industry, volume)
+ts_covariance(close, vwap, 30)
+ts_arg_max(ts_delta(close, 5), 60)
+ts_backfill(ts_delta(close, 5), 10, 1, "NAN")
+ts_sum(ts_delta(close, 5), 20)
+ts_av_diff(ts_delta(close, 5), 20)
+ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 30)
+ts_regression(ts_delta(close, 5), ts_step(1), 60, 0, 0)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+group_neutralize(ts_delta(close, 20), industry)
+ts_decay_linear(ts_delta(ts_mean(close, 20), 5), 30)
+ts_std_dev(ts_delta(ts_mean(close, 20), 5), 60)
+ts_rank(ts_delta(ts_mean(close, 20), 5), 60)
+ts_quantile(ts_delta(ts_mean(close, 20), 5), 60)
+group_mean(ts_delta(ts_mean(close, 20), 5), industry, market_cap)
+ts_covariance(ts_delta(close, 5), ts_delta(vwap, 5), 30)
+ts_arg_min(ts_delta(ts_mean(close, 20), 5), 60)
+ts_backfill(ts_delta(ts_mean(close, 20), 5), 10, 1, "NAN")
+ts_sum(ts_delta(ts_mean(close, 20), 5), 20)
+ts_av_diff(ts_delta(ts_mean(close, 20), 5), 20)
+ts_corr(ts_delta(ts_mean(close, 20), 5), ts_delta(volume, 5), 30)
+ts_regression(ts_delta(ts_mean(close, 20), 5), ts_step(1), 60, 0, 0)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+group_zscore(ts_delta(ts_mean(close, 20), 5), industry)
+ts_decay_linear(ts_delta(ts_mean(close, 20), 5), 30)
+ts_std_dev(ts_delta(ts_mean(close, 20), 5), 60)
+ts_rank(ts_delta(ts_mean(close, 20), 5), 60)
+ts_quantile(ts_delta(ts_mean(close, 20), 5), 60)
+group_mean(ts_delta(ts_mean(close, 20), 5), industry, volume)
+ts_covariance(ts_delta(ts_mean(close, 20), 5), ts_delta(vwap, 5), 30)
+ts_arg_max(ts_delta(ts_mean(close, 20), 5), 60)
+ts_backfill(ts_delta(ts_mean(close, 20), 5), 10, 1, "NAN")
+ts_sum(ts_delta(ts_mean(close, 20), 5), 20)
+ts_av_diff(ts_delta(ts_mean(close, 20), 5), 20)
+ts_corr(ts_delta(ts_mean(close, 20), 5), ts_delta(volume, 5), 30)
+ts_regression(ts_delta(ts_mean(close, 20), 5), ts_step(1), 60, 0, 0)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+group_neutralize(ts_delta(ts_mean(close, 20), 5), industry)
+ts_decay_linear(ts_delta(ts_mean(close, 20), 5), 30)
+ts_std_dev(ts_delta(ts_mean(close, 20), 5), 60)
+ts_rank(ts_delta(ts_mean(close, 20), 5), 60)
+ts_quantile(ts_delta(ts_mean(close, 20), 5), 60)
+group_mean(ts_delta(ts_mean(close, 20), 5), industry, market_cap)
+ts_covariance(ts_delta(ts_mean(close, 20), 5), ts_delta(vwap, 5), 30)
+ts_arg_min(ts_delta(ts_mean(close, 20), 5), 60)
+ts_backfill(ts_delta(ts_mean(close, 20), 5), 10, 1, "NAN")
+ts_sum(ts_delta(ts_mean(close, 20), 5), 20)
+ts_av_diff(ts_delta(ts_mean(close, 20), 5), 20)
+ts_corr(ts_delta(ts_mean(close, 20), 5), ts_delta(volume, 5), 30)
+ts_regression(ts_delta(ts_mean(close, 20), 5), ts_step(1), 60, 0, 0)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+group_zscore(ts_delta(ts_mean(close, 20), 5), industry)
+ts_decay_linear(ts_delta(ts_mean(close, 20), 5), 30)
+ts_std_dev(ts_delta(ts_mean(close, 20), 5), 60)
+ts_rank(ts_delta(ts_mean(close, 20), 5), 60)
+ts_quantile(ts_delta(ts_mean(close, 20), 5), 60)
+group_mean(ts_delta(ts_mean(close, 20), 5), industry, volume)
+ts_covariance(ts_delta(ts_mean(close, 20), 5), ts_delta(vwap, 5), 30)
+ts_arg_max(ts_delta(ts_mean(close, 20), 5), 60)
+ts_backfill(ts_delta(ts_mean(close, 20), 5), 10, 1, "NAN")
+ts_sum(ts_delta(ts_mean(close, 20), 5), 20)
+ts_av_diff(ts_delta(ts_mean(close, 20), 5), 20)
+ts_corr(ts_delta(ts_mean(close, 20), 5), ts_delta(volume, 5), 30)
+ts_regression(ts_delta(ts_mean(close, 20), 5), ts_step(1), 60, 0, 0)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+group_neutralize(ts_delta(ts_mean(close, 20), 5), industry)
+ts_decay_linear(ts_delta(ts_mean(close, 20), 5), 30)
+ts_std_dev(ts_delta(ts_mean(close, 20), 5), 60)
+ts_rank(ts_delta(ts_mean(close, 20), 5), 60)
+ts_quantile(ts_delta(ts_mean(close, 20), 5), 60)
+group_mean(ts_delta(ts_mean(close, 20), 5), industry, market_cap)
+ts_covariance(ts_delta(ts_mean(close, 20), 5), ts_delta(vwap, 5), 30)
+ts_arg_min(ts_delta(ts_mean(close, 20), 5), 60)
+ts_backfill(ts_delta(ts_mean(close, 20), 5), 10, 1, "NAN")
+ts_sum(ts_delta(ts_mean(close, 20), 5), 20)
+ts_av_diff(ts_delta(ts_mean(close, 20), 5), 20)
+ts_corr(ts_delta(ts_mean(close, 20), 5), ts_delta(volume, 5), 30)
+ts_regression(ts_delta(ts_mean(close, 20), 5), ts_step(1), 60, 0, 0)
+ts_zscore(ts_delta(ts_mean(close, 20), 5), 60)
+group_zscore(ts_delta(ts_mean(close, 20), 5), industry)

generated_alpha/2025-12-08/090705.txt

@@ -0,0 +1,76 @@
+ts_regression_slope(close, 5) * group_zscore(ts_delta(close, 10), sector)
+ts_decay_linear(ts_delta(close, 3), 20) * -ts_zscore(volume, 60)
+group_mean(ts_corr(close, vwap, 10), industry, 0.3) - group_mean(close, industry)
+ts_rank(ts_delta(ts_regression_slope(close, 10), 5), 30) * group_std(ts_zscore(close, 20), sector)
+ts_corr(ts_zscore(close, 5), ts_zscore(volume, 5), 20) * group_mean(ts_return(close, 20), country, 0.2)
+ts_std_dev(ts_delta(close, 1), 60) * -group_zscore(ts_sum(volume, 10), industry)
+ts_regression_slope(ts_zscore(close, 30), 10) * group_rank(ts_delta(high, 5), sector)
+ts_mean(ts_zscore(close, 10), 20) * -ts_covariance(close, volume, 30)
+group_mean(ts_decay_linear(ts_delta(close, 3), 10), industry, 0.4) - ts_mean(close, 60)
+ts_rank(ts_delta(ts_std_dev(close, 5), 1), 20) * group_zscore(ts_mean(close, 10), country)
+ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 30) * group_mean(ts_regression_slope(close, 5), sector, 0.3)
+ts_decay_linear(ts_zscore(close, 20), 10) * -group_std(ts_delta(close, 1), industry)
+ts_regression_slope(ts_mean(close, 5), 10) * group_mean(ts_zscore(volume, 10), country, 0.2)
+ts_zscore(ts_delta(close, 10), 60) * group_rank(ts_corr(close, vwap, 10), industry)
+group_zscore(ts_sum(ts_delta(close, 1), 5), sector) * -ts_rank(ts_std_dev(close, 10), 30)
+ts_covariance(ts_zscore(close, 5), ts_zscore(volume, 5), 20) * group_mean(ts_delta(close, 5), industry, 0.3)
+ts_mean(ts_regression_slope(close, 5), 20) * -group_std(ts_zscore(close, 10), country)
+ts_delta(ts_corr(close, volume, 10), 5) * group_mean(ts_zscore(close, 20), sector, 0.2)
+ts_decay_linear(ts_delta(ts_zscore(close, 10), 1), 30) * group_rank(ts_mean(close, 5), country)
+ts_std_dev(ts_zscore(close, 5), 60) * -group_zscore(ts_delta(close, 1), industry)
+group_mean(ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 10), sector, 0.3) - ts_mean(close, 30)
+ts_rank(ts_regression_slope(close, 10), 20) * group_zscore(ts_sum(volume, 5), country)
+ts_zscore(ts_mean(ts_delta(close, 1), 5), 30) * -group_std(ts_regression_slope(close, 5), industry)
+ts_corr(ts_delta(close, 3), ts_delta(vwap, 3), 20) * group_mean(ts_zscore(close, 10), sector, 0.4)
+ts_decay_linear(ts_zscore(ts_delta(close, 1), 5), 10) * -group_rank(ts_mean(close, 10), country)
+ts_regression_slope(ts_zscore(close, 30), 5) * group_mean(ts_delta(volume, 5), industry, 0.2)
+ts_mean(ts_zscore(ts_delta(close, 1), 5), 20) * group_std(ts_covariance(close, volume, 10), sector)
+ts_rank(ts_std_dev(ts_delta(close, 1), 5), 30) * -group_zscore(ts_mean(close, 10), country)
+group_zscore(ts_delta(ts_mean(close, 5), 1), industry) * ts_corr(ts_zscore(close, 5), volume, 20)
+ts_covariance(ts_delta(close, 5), ts_delta(high, 5), 30) * group_mean(ts_regression_slope(close, 5), sector, 0.3)
+ts_decay_linear(ts_zscore(ts_mean(close, 5), 10), 20) * -group_std(ts_delta(close, 1), industry)
+ts_regression_slope(ts_mean(close, 10), 5) * group_mean(ts_zscore(volume, 5), country, 0.2)
+ts_zscore(ts_delta(ts_zscore(close, 10), 1), 60) * group_rank(ts_corr(close, vwap, 5), sector)
+ts_mean(ts_delta(ts_regression_slope(close, 5), 1), 20) * -group_zscore(ts_sum(volume, 10), industry)
+ts_corr(ts_zscore(close, 5), ts_zscore(volume, 5), 10) * group_mean(ts_delta(close, 5), country, 0.3)
+ts_decay_linear(ts_delta(ts_zscore(close, 5), 1), 30) * group_std(ts_mean(close, 10), sector)
+ts_std_dev(ts_zscore(ts_delta(close, 1), 5), 60) * -group_mean(ts_regression_slope(close, 5), industry, 0.4)
+group_mean(ts_corr(ts_delta(close, 3), ts_delta(volume, 3), 5), sector, 0.2) - ts_mean(close, 20)
+ts_rank(ts_regression_slope(ts_zscore(close, 10), 5), 30) * group_zscore(ts_sum(volume, 5), country)
+ts_zscore(ts_mean(ts_delta(close, 1), 5), 60) * -group_std(ts_zscore(close, 20), industry)
+ts_corr(ts_delta(close, 10), ts_delta(high, 10), 20) * group_mean(ts_zscore(close, 5), sector, 0.3)
+ts_decay_linear(ts_zscore(ts_mean(close, 3), 5), 10) * -group_rank(ts_mean(close, 10), country)
+ts_regression_slope(ts_zscore(close, 60), 10) * group_mean(ts_delta(volume, 3), industry, 0.2)
+ts_mean(ts_zscore(ts_delta(close, 1), 3), 30) * group_std(ts_covariance(close, volume, 5), sector)
+ts_rank(ts_std_dev(ts_delta(close, 1), 3), 20) * -group_zscore(ts_mean(close, 5), country)
+group_zscore(ts_delta(ts_mean(close, 3), 1), industry) * ts_corr(ts_zscore(close, 3), volume, 10)
+ts_covariance(ts_delta(close, 3), ts_delta(vwap, 3), 10) * group_mean(ts_regression_slope(close, 3), sector, 0.4)
+ts_decay_linear(ts_zscore(ts_delta(close, 1), 3), 20) * -group_std(ts_delta(close, 1), country)
+ts_regression_slope(ts_mean(close, 3), 10) * group_mean(ts_zscore(volume, 3), industry, 0.2)
+ts_zscore(ts_delta(ts_zscore(close, 5), 1), 30) * group_rank(ts_corr(close, vwap, 3), sector)
+ts_mean(ts_delta(ts_regression_slope(close, 3), 1), 10) * -group_zscore(ts_sum(volume, 5), industry)
+ts_corr(ts_zscore(close, 3), ts_zscore(volume, 3), 5) * group_mean(ts_delta(close, 3), country, 0.3)
+ts_decay_linear(ts_delta(ts_zscore(close, 3), 1), 10) * group_std(ts_mean(close, 5), sector)
+ts_std_dev(ts_zscore(ts_delta(close, 1), 3), 30) * -group_mean(ts_regression_slope(close, 3), industry, 0.4)
+group_mean(ts_corr(ts_delta(close, 1), ts_delta(volume, 1), 3), sector, 0.2) - ts_mean(close, 10)
+ts_rank(ts_regression_slope(ts_zscore(close, 5), 3), 10) * group_zscore(ts_sum(volume, 3), country)
+ts_zscore(ts_mean(ts_delta(close, 1), 3), 20) * -group_std(ts_zscore(close, 5), industry)
+ts_corr(ts_delta(close, 5), ts_delta(high, 5), 10) * group_mean(ts_zscore(close, 3), sector, 0.3)
+ts_decay_linear(ts_zscore(ts_mean(close, 1), 3), 5) * -group_rank(ts_mean(close, 5), country)
+ts_regression_slope(ts_zscore(close, 30), 5) * group_mean(ts_delta(volume, 1), industry, 0.2)
+ts_mean(ts_zscore(ts_delta(close, 1), 1), 10) * group_std(ts_covariance(close, volume, 3), sector)
+ts_rank(ts_std_dev(ts_delta(close, 1), 1), 10) * -group_zscore(ts_mean(close, 3), country)
+group_zscore(ts_delta(ts_mean(close, 1), 1), industry) * ts_corr(ts_zscore(close, 1), volume, 5)
+ts_covariance(ts_delta(close, 1), ts_delta(vwap, 1), 5) * group_mean(ts_regression_slope(close, 1), sector, 0.3)
+ts_decay_linear(ts_zscore(ts_delta(close, 1), 1), 5) * -group_std(ts_delta(close, 1), country)
+ts_regression_slope(ts_mean(close, 1), 5) * group_mean(ts_zscore(volume, 1), industry, 0.2)
+ts_zscore(ts_delta(ts_zscore(close, 3), 1), 10) * group_rank(ts_corr(close, vwap, 1), sector)
+ts_mean(ts_delta(ts_regression_slope(close, 1), 1), 5) * -group_zscore(ts_sum(volume, 3), industry)
+ts_corr(ts_zscore(close, 1), ts_zscore(volume, 1), 3) * group_mean(ts_delta(close, 1), country, 0.3)
+ts_decay_linear(ts_delta(ts_zscore(close, 1), 1), 5) * group_std(ts_mean(close, 3), sector)
+ts_std_dev(ts_zscore(ts_delta(close, 1), 1), 10) * -group_mean(ts_regression_slope(close, 1), industry, 0.4)
+group_mean(ts_corr(ts_delta(close, 1), ts_delta(volume, 1), 1), sector, 0.2) - ts_mean(close, 5)
+ts_rank(ts_regression_slope(ts_zscore(close, 3), 1), 5) * group_zscore(ts_sum(volume, 1), country)
+ts_zscore(ts_mean(ts_delta(close, 1), 1), 10) * -group_std(ts_zscore(close, 3), industry)
+ts_corr(ts_delta(close, 3), ts_delta(high, 3), 5) * group_mean(ts_zscore(close, 1), sector, 0.3)
+ts_decay_linear(ts_zscore(ts_mean(close, 1), 1), 3) * -group_rank(ts_mean(close, 3), country)

generated_alpha/2025-12-08/092309.txt

@@ -0,0 +1,82 @@
+ts_regression_slope(close, 10) * group_zscore(ts_zscore(volume, 20), sector)
+group_mean(ts_delta(close, 5), 0.3) - group_mean(close, sector)
+ts_corr(ts_zscore(high, 30), ts_zscore(low, 30), 20) * ts_decay_linear(ts_regression_slope(close, 5), 10)
+rank(group_rank(ts_return(close, 20), industry)) * -ts_zscore(ts_std_dev(close, 60), 120)
+ts_delta(ts_mean(close, 10), 5) * group_scale(ts_rank(close, 30), sector)
+group_mean(ts_delta(close, 3), 0.2) / ts_std_dev(close, 20) * ts_zscore(volume, 10)
+ts_rank(ts_regression_slope(close, 15), 60) * group_zscore(ts_zscore(ts_sum(volume, 5), 20), industry)
+ts_decay_linear(ts_delta(close, 10), 30) * group_mean(ts_zscore(close, 60), 0.4)
+ts_corr(ts_delta(high, 5), ts_delta(low, 5), 20) * group_neutralize(ts_return(close, 30), sector)
+ts_zscore(ts_mean(close, 20), 120) * group_rank(ts_regression_slope(close, 10), industry)
+ts_std_dev(ts_return(close, 5), 30) * group_mean(ts_zscore(volume, 5), 0.3)
+ts_regression_slope(ts_zscore(close, 30), 10) * group_zscore(ts_rank(volume, 20), sector)
+group_mean(ts_delta(close, 5), 0.35) - ts_mean(close, 60) * ts_decay_linear(ts_zscore(close, 10), 20)
+ts_corr(ts_zscore(close, 10), ts_zscore(volume, 10), 5) * group_scale(ts_delta(close, 3), industry)
+ts_rank(ts_std_dev(close, 20), 60) * group_zscore(ts_regression_slope(close, 5), sector)
+ts_decay_linear(ts_delta(close, 5), 15) * group_mean(ts_zscore(ts_sum(volume, 3), 10), 0.4)
+ts_zscore(ts_mean(close, 5), 30) * group_rank(ts_delta(close, 3), industry)
+ts_regression_slope(ts_mean(close, 10), 20) * group_zscore(ts_rank(volume, 10), sector)
+ts_corr(ts_zscore(high, 20), ts_zscore(close, 20), 10) * group_mean(ts_delta(close, 5), 0.3)
+ts_delta(ts_regression_slope(close, 10), 5) * group_zscore(ts_std_dev(close, 60), industry)
+ts_rank(ts_zscore(close, 30), 120) * group_mean(ts_regression_slope(close, 5), 0.25)
+ts_std_dev(ts_zscore(close, 10), 20) * group_scale(ts_delta(close, 3), sector)
+ts_decay_linear(ts_zscore(ts_return(close, 5), 20), 10) * group_mean(ts_rank(close, 30), 0.3)
+ts_corr(ts_mean(close, 5), ts_mean(close, 20), 30) * group_zscore(ts_delta(close, 3), industry)
+ts_regression_slope(ts_zscore(close, 20), 5) * group_rank(ts_mean(close, 10), sector)
+ts_zscore(ts_delta(close, 3), 10) * group_mean(ts_regression_slope(close, 5), 0.4)
+ts_rank(ts_mean(close, 30), 60) * group_zscore(ts_corr(close, volume, 5), industry)
+ts_decay_linear(ts_delta(close, 10), 20) * group_mean(ts_zscore(ts_sum(volume, 5), 30), 0.3)
+ts_corr(ts_zscore(close, 10), ts_zscore(close, 30), 20) * group_scale(ts_regression_slope(close, 5), sector)
+ts_regression_slope(ts_mean(close, 5), 10) * group_zscore(ts_rank(ts_sum(volume, 3), 10), industry)
+ts_delta(ts_zscore(close, 10), 5) * group_mean(ts_std_dev(close, 20), 0.25)
+ts_rank(ts_regression_slope(close, 5), 30) * group_zscore(ts_mean(close, 10), sector)
+ts_std_dev(ts_delta(close, 3), 20) * group_mean(ts_zscore(volume, 5), 0.35)
+ts_decay_linear(ts_corr(close, volume, 5), 10) * group_rank(ts_delta(close, 5), industry)
+ts_zscore(ts_mean(close, 20), 60) * group_mean(ts_regression_slope(close, 10), 0.4)
+ts_regression_slope(ts_zscore(close, 30), 15) * group_zscore(ts_delta(close, 3), sector)
+ts_corr(ts_zscore(high, 5), ts_zscore(low, 5), 10) * group_mean(ts_rank(close, 20), 0.3)
+ts_rank(ts_delta(close, 3), 60) * group_zscore(ts_std_dev(close, 10), industry)
+ts_decay_linear(ts_zscore(ts_return(close, 3), 20), 5) * group_mean(ts_regression_slope(close, 5), 0.2)
+ts_corr(ts_mean(close, 3), ts_mean(close, 10), 20) * group_scale(ts_delta(close, 5), sector)
+ts_regression_slope(ts_mean(close, 20), 30) * group_zscore(ts_rank(volume, 20), industry)
+ts_delta(ts_regression_slope(close, 10), 3) * group_mean(ts_zscore(ts_sum(volume, 5), 20), 0.35)
+ts_zscore(ts_mean(close, 10), 30) * group_rank(ts_delta(close, 3), sector)
+ts_std_dev(ts_zscore(close, 5), 10) * group_mean(ts_regression_slope(close, 5), 0.3)
+ts_decay_linear(ts_delta(close, 5), 15) * group_zscore(ts_rank(close, 10), industry)
+ts_corr(ts_zscore(close, 5), ts_zscore(volume, 5), 5) * group_mean(ts_mean(close, 10), 0.4)
+ts_rank(ts_regression_slope(close, 5), 20) * group_zscore(ts_delta(close, 3), sector)
+ts_regression_slope(ts_zscore(close, 10), 5) * group_rank(ts_std_dev(close, 20), industry)
+ts_delta(ts_mean(close, 5), 3) * group_mean(ts_zscore(volume, 5), 0.35)
+ts_zscore(ts_std_dev(close, 10), 30) * group_scale(ts_regression_slope(close, 5), sector)
+ts_corr(ts_zscore(high, 10), ts_zscore(close, 10), 10) * group_mean(ts_rank(close, 30), 0.2)
+ts_decay_linear(ts_zscore(ts_return(close, 3), 10), 5) * group_zscore(ts_delta(close, 5), industry)
+ts_regression_slope(ts_mean(close, 10), 20) * group_mean(ts_zscore(ts_sum(volume, 3), 10), 0.3)
+ts_rank(ts_delta(close, 3), 30) * group_zscore(ts_mean(close, 20), sector)
+ts_std_dev(ts_delta(close, 5), 20) * group_mean(ts_regression_slope(close, 5), 0.4)
+ts_corr(ts_zscore(close, 20), ts_zscore(close, 60), 30) * group_scale(ts_delta(close, 3), industry)
+ts_decay_linear(ts_regression_slope(close, 5), 10) * group_zscore(ts_rank(volume, 10), sector)
+ts_zscore(ts_mean(close, 30), 120) * group_mean(ts_delta(close, 5), 0.3)
+ts_regression_slope(ts_zscore(close, 5), 10) * group_rank(ts_std_dev(close, 10), industry)
+ts_delta(ts_zscore(close, 5), 3) * group_mean(ts_zscore(ts_sum(volume, 5), 20), 0.25)
+ts_rank(ts_mean(close, 10), 60) * group_zscore(ts_corr(close, volume, 3), sector)
+ts_corr(ts_zscore(high, 3), ts_zscore(low, 3), 5) * group_mean(ts_regression_slope(close, 3), 0.4)
+ts_decay_linear(ts_zscore(ts_return(close, 5), 30), 20) * group_zscore(ts_delta(close, 3), industry)
+ts_regression_slope(ts_mean(close, 5), 10) * group_mean(ts_rank(close, 10), 0.35)
+ts_std_dev(ts_zscore(close, 10), 20) * group_scale(ts_delta(close, 5), sector)
+ts_rank(ts_regression_slope(close, 3), 20) * group_zscore(ts_mean(close, 10), industry)
+ts_delta(ts_mean(close, 3), 5) * group_mean(ts_zscore(volume, 10), 0.3)
+ts_zscore(ts_delta(close, 3), 10) * group_rank(ts_regression_slope(close, 5), sector)
+ts_corr(ts_zscore(close, 5), ts_zscore(volume, 5), 10) * group_mean(ts_std_dev(close, 20), 0.4)
+ts_decay_linear(ts_delta(close, 3), 10) * group_zscore(ts_rank(close, 20), industry)
+ts_regression_slope(ts_zscore(close, 20), 10) * group_mean(ts_zscore(ts_sum(volume, 5), 10), 0.35)
+ts_rank(ts_mean(close, 20), 120) * group_zscore(ts_delta(close, 5), sector)
+ts_std_dev(ts_delta(close, 5), 30) * group_mean(ts_regression_slope(close, 5), 0.3)
+ts_corr(ts_zscore(high, 5), ts_zscore(close, 5), 5) * group_scale(ts_delta(close, 3), industry)
+ts_decay_linear(ts_zscore(ts_return(close, 5), 20), 10) * group_zscore(ts_mean(close, 10), sector)
+ts_regression_slope(ts_mean(close, 10), 15) * group_mean(ts_rank(close, 30), 0.25)
+ts_delta(ts_zscore(close, 10), 5) * group_zscore(ts_std_dev(close, 30), industry)
+ts_rank(ts_delta(close, 5), 60) * group_mean(ts_zscore(ts_sum(volume, 3), 20), 0.4)
+ts_zscore(ts_mean(close, 5), 20) * group_rank(ts_regression_slope(close, 3), sector)
+ts_corr(ts_zscore(close, 10), ts_zscore(Vector, 10), 20) * group_mean(ts_delta(close, 3), 0.35)
+ts_decay_linear(ts_regression_slope(close, 5), 15) * group_zscore(ts_rank(volume, 30), industry)
+ts_std_dev(ts_zscore(close, 5), 10) * group_scale(ts_delta(close, 5), sector)

generated_alpha/2025-12-08/095105.txt

@@ -0,0 +1,96 @@
+multiply(ts_delta(ts_regression(close, ts_step(1), 20, 0, 1), 5), group_zscore(ts_rank(close, 10), "industry"))
+subtract(group_mean(ts_zscore(close, 30), "industry"), ts_mean(close, 20))
+multiply(ts_corr(ts_zscore(volume, 10), ts_zscore(close, 10), 20), group_rank(bucket(rank(close), "0.8,1"), "industry"))
+ts_regression(ts_zscore(close, 60), ts_step(1), 30, 0, 1)
+multiply(ts_decay_linear(ts_delta(close, 10), 20), group_neutralize(ts_rank(close, 30), "sector"))
+ts_zscore(group_mean(ts_delta(close, 5), "industry"), 10)
+multiply(ts_av_diff(volume, 20), reverse(ts_rank(group_mean(close, "industry"), 60)))
+group_scale(ts_regression(close, ts_step(1), 10, 0, 1), "sector")
+ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 30)
+subtract(ts_mean(close, 120), group_mean(close, "industry"))
+multiply(ts_rank(ts_zscore(close, 20), 30), group_zscore(ts_delta(close, 10), "industry"))
+ts_regression(ts_zscore(ts_mean(close, 5), 20), ts_step(1), 60, 0, 1)
+group_rank(ts_decay_linear(close, 30), "industry")
+ts_covariance(ts_zscore(close, 10), ts_zscore(volume, 10), 20)
+multiply(ts_scale(close, 60), group_neutralize(ts_regression(close, ts_step(1), 20, 0, 1), "sector"))
+ts_delta(group_mean(ts_regression(close, ts_step(1), 30, 0, 1), "industry"), 5)
+ts_zscore(ts_corr(close, volume, 30), 60)
+multiply(group_zscore(ts_delta(close, 10), "industry"), ts_rank(ts_zscore(close, 20), 10))
+group_mean(ts_av_diff(close, 20), "sector")
+subtract(ts_regression(close, ts_step(1), 60, 0, 1), ts_regression(close, ts_step(1), 20, 0, 1))
+multiply(ts_delay(ts_zscore(close, 30), 5), group_rank(close, "industry"))
+ts_regression(ts_delta(close, 5), ts_step(1), 20, 0, 1)
+group_scale(ts_zscore(close, 60), "industry")
+multiply(ts_corr(ts_mean(close, 5), ts_mean(volume, 5), 10), group_neutralize(ts_delta(close, 10), "sector"))
+ts_delta(ts_regression(ts_zscore(close, 20), ts_step(1), 30, 0, 1), 5)
+group_zscore(ts_decay_linear(close, 20), "industry")
+multiply(ts_rank(ts_corr(close, volume, 20), 30), group_mean(ts_zscore(close, 10), "sector"))
+ts_mean(group_zscore(ts_delta(close, 5), "industry"), 10)
+subtract(ts_zscore(close, 60), group_zscore(close, "sector"))
+multiply(ts_covariance(close, volume, 10), group_rank(ts_regression(close, ts_step(1), 20, 0, 1), "industry"))
+ts_regression(ts_av_diff(close, 20), ts_step(1), 30, 0, 1)
+group_neutralize(ts_delta(ts_zscore(close, 10), 5), "sector")
+multiply(ts_scale(ts_delta(close, 10), 30), group_zscore(ts_rank(close, 20), "industry"))
+ts_corr(group_mean(close, "industry"), ts_mean(close, 20), 60)
+subtract(ts_regression(close, ts_step(1), 20, 0, 1), group_mean(ts_regression(close, ts_step(1), 20, 0, 1), "industry"))
+multiply(ts_rank(ts_zscore(volume, 30), 10), group_zscore(ts_delta(volume, 5), "sector"))
+ts_decay_linear(group_mean(ts_zscore(close, 20), "industry"), 30)
+group_rank(ts_corr(close, volume, 20), "industry")
+multiply(ts_zscore(ts_mean(close, 10), 20), group_neutralize(ts_regression(close, ts_step(1), 60, 0, 1), "sector"))
+ts_delta(group_scale(ts_zscore(close, 30), "industry"), 5)
+multiply(ts_av_diff(ts_zscore(close, 10), 20), group_mean(ts_delta(close, 5), "industry"))
+ts_regression(ts_delta(ts_zscore(close, 20), 5), ts_step(1), 30, 0, 1)
+group_zscore(ts_mean(close, 20), "sector")
+multiply(ts_corr(ts_zscore(close, 5), ts_zscore(volume, 5), 10), group_rank(ts_zscore(close, 30), "industry"))
+ts_mean(group_neutralize(ts_delta(close, 10), "sector"), 20)
+subtract(ts_regression(ts_zscore(close, 60), ts_step(1), 30, 0, 1), ts_regression(ts_zscore(close, 60), ts_step(1), 10, 0, 1))
+multiply(ts_scale(ts_delta(volume, 5), 30), group_zscore(ts_regression(close, ts_step(1), 20, 0, 1), "industry"))
+ts_covariance(ts_zscore(group_mean(close, "industry"), 20), ts_zscore(close, 20), 30)
+group_mean(ts_decay_linear(ts_delta(close, 5), 20), "sector")
+multiply(ts_rank(ts_zscore(close, 10), 60), group_zscore(ts_av_diff(close, 20), "industry"))
+ts_regression(ts_zscore(ts_mean(close, 5), 20), ts_step(1), 60, 0, 1)
+group_scale(ts_delta(ts_zscore(close, 30), 5), "industry")
+multiply(ts_corr(ts_delta(close, 5), ts_delta(volume, 5), 20), group_neutralize(ts_rank(close, 30), "sector"))
+ts_delta(ts_regression(ts_zscore(close, 10), ts_step(1), 20, 0, 1), 5)
+group_zscore(ts_mean(ts_delta(close, 5), 10), "sector")
+multiply(ts_rank(ts_corr(close, volume, 30), 20), group_mean(ts_zscore(close, 10), "industry"))
+ts_mean(group_zscore(ts_delta(volume, 5), "industry"), 5)
+subtract(ts_zscore(close, 20), group_zscore(close, "industry"))
+multiply(ts_covariance(ts_zscore(close, 5), ts_zscore(volume, 5), 10), group_rank(ts_regression(close, ts_step(1), 30, 0, 1), "sector"))
+ts_regression(ts_av_diff(close, 10), ts_step(1), 20, 0, 1)
+group_neutralize(ts_delta(ts_zscore(close, 30), 5), "industry")
+multiply(ts_scale(ts_delta(close, 10), 60), group_zscore(ts_rank(close, 10), "sector"))
+ts_corr(group_mean(ts_zscore(close, 20), "industry"), ts_mean(close, 20), 30)
+subtract(ts_regression(close, ts_step(1), 30, 0, 1), group_mean(ts_regression(close, ts_step(1), 30, 0, 1), "sector"))
+multiply(ts_rank(ts_zscore(volume, 20), 10), group_zscore(ts_delta(volume, 20), "industry"))
+ts_decay_linear(group_mean(ts_zscore(close, 10), "sector"), 20)
+group_rank(ts_corr(ts_zscore(close, 5), ts_zscore(volume, 5), 10), "industry")
+multiply(ts_zscore(ts_mean(close, 5), 30), group_neutralize(ts_regression(close, ts_step(1), 120, 0, 1), "sector"))
+ts_delta(group_scale(ts_zscore(close, 10), "industry"), 5)
+multiply(ts_av_diff(ts_zscore(close, 30), 20), group_mean(ts_delta(close, 10), "sector"))
+ts_regression(ts_delta(ts_zscore(close, 10), 5), ts_step(1), 60, 0, 1)
+group_zscore(ts_mean(close, 60), "industry")
+multiply(ts_corr(ts_zscore(close, 20), ts_zscore(volume, 20), 10), group_rank(ts_zscore(close, 30), "sector"))
+ts_mean(group_neutralize(ts_delta(close, 5), "industry"), 30)
+subtract(ts_regression(ts_zscore(close, 30), ts_step(1), 60, 0, 1), ts_regression(ts_zscore(close, 30), ts_step(1), 20, 0, 1))
+multiply(ts_scale(ts_delta(volume, 10), 20), group_zscore(ts_regression(close, ts_step(1), 30, 0, 1), "industry"))
+ts_covariance(ts_zscore(group_mean(close, "sector"), 10), ts_zscore(close, 10), 20)
+group_mean(ts_decay_linear(ts_delta(close, 10), 30), "industry")
+multiply(ts_rank(ts_zscore(close, 30), 60), group_zscore(ts_av_diff(close, 10), "sector"))
+ts_regression(ts_zscore(ts_mean(close, 10), 20), ts_step(1), 120, 0, 1)
+group_scale(ts_delta(ts_zscore(close, 60), 5), "sector")
+multiply(ts_corr(ts_delta(close, 10), ts_delta(volume, 10), 30), group_neutralize(ts_rank(close, 60), "industry"))
+ts_delta(ts_regression(ts_zscore(close, 20), ts_step(1), 30, 0, 1), 5)
+group_zscore(ts_mean(ts_delta(close, 10), 20), "industry")
+multiply(ts_rank(ts_corr(close, volume, 60), 30), group_mean(ts_zscore(close, 20), "sector"))
+ts_mean(group_zscore(ts_delta(volume, 10), "sector"), 10)
+subtract(ts_zscore(close, 120), group_zscore(close, "industry"))
+multiply(ts_covariance(ts_zscore(close, 10), ts_zscore(volume, 10), 30), group_rank(ts_regression(close, ts_step(1), 60, 0, 1), "industry"))
+ts_regression(ts_av_diff(close, 30), ts_step(1), 60, 0, 1)
+group_neutralize(ts_delta(ts_zscore(close, 60), 5), "sector")
+multiply(ts_scale(ts_delta(close, 20), 60), group_zscore(ts_rank(close, 30), "industry"))
+ts_corr(group_mean(ts_zscore(close, 30), "industry"), ts_mean(close, 60), 30)
+subtract(ts_regression(close, ts_step(1), 60, 0, 1), group_mean(ts_regression(close, ts_step(1), 60, 0, 1), "industry"))
+multiply(ts_rank(ts_zscore(volume, 60), 30), group_zscore(ts_delta(volume, 30), "sector"))
+ts_decay_linear(group_mean(ts_zscore(close, 30), "industry"), 60)
+group_rank(ts_corr(ts_zscore(close, 10), ts_zscore(volume, 10), 20), "sector")

generated_alpha/2025-12-08/100004.txt

@@ -0,0 +1,106 @@
+
+
+Here are 100 composite Alpha factors combining at least two dimensions for industry rotation strategies:
+
+1. multiply( ts_delta(close,5), reverse(ts_zscore(close,20)) )
+2. multiply( ts_delta(close,10), reverse(ts_zscore(close,30)) )
+3. multiply( ts_delta(close,20), reverse(ts_zscore(close,60)) )
+4. multiply( ts_delta(close,30), reverse(ts_zscore(close,120)) )
+5. multiply( ts_delta(close,5), reverse(ts_rank(close,20)) )
+6. multiply( ts_delta(close,10), reverse(ts_rank(close,30)) )
+7. multiply( ts_delta(close,20), reverse(ts_rank(close,60)) )
+8. multiply( ts_delta(close,30), reverse(ts_rank(close,120)) )
+9. multiply( ts_regression(close, ts_step(1), 20, 0, 1), reverse(ts_corr(volume, close, 20)) )
+10. multiply( ts_regression(close, ts_step(1), 30, 0, 1), reverse(ts_corr(volume, close, 30)) )
+11. multiply( group_rank(ts_delta(close,5), group), reverse(ts_zscore(close,20)) )
+12. multiply( group_rank(ts_delta(close,10), group), reverse(ts_zscore(close,30)) )
+13. multiply( group_rank(ts_delta(close,20), group), reverse(ts_zscore(close,60)) )
+14. multiply( group_rank(ts_delta(close,30), group), reverse(ts_zscore(close,120)) )
+15. multiply( group_rank(close, group), reverse(ts_rank(close,20)) )
+16. multiply( group_rank(close, group), reverse(ts_rank(close,30)) )
+17. multiply( group_rank(close, group), reverse(ts_rank(close,60)) )
+18. multiply( group_rank(close, group), reverse(ts_rank(close,120)) )
+19. multiply( group_rank(close, group), reverse(ts_corr(volume, close, 20)) )
+20. multiply( group_rank(close, group), reverse(ts_corr(volume, close, 30)) )
+21. multiply( group_rank(close, group), reverse(ts_corr(volume, close, 60)) )
+22. multiply( group_rank(close, group), reverse(ts_corr(volume, close, 90)) )
+23. multiply( ts_decay_linear(ts_delta(close,5), 10), reverse(ts_zscore(close,20)) )
+24. multiply( ts_decay_linear(ts_delta(close,10), 10), reverse(ts_zscore(close,30)) )
+25. multiply( ts_decay_linear(ts_delta(close,20), 10), reverse(ts_zscore(close,60)) )
+26. multiply( ts_decay_linear(ts_delta(close,30), 10), reverse(ts_zscore(close,120)) )
+27. multiply( divide(ts_delta(close,5), ts_std_dev(close,20)), group_rank(close, group) )
+28. multiply( divide(ts_delta(close,10), ts_std_dev(close,30)), group_rank(close, group) )
+29. multiply( divide(ts_delta(close,20), ts_std_dev(close,60)), group_rank(close, group) )
+30. multiply( divide(ts_delta(close,30), ts_std_dev(close,120)), group_rank(close, group) )
+31. multiply( ts_zscore(ts_delta(close,5),10), reverse(ts_rank(close,10)) )
+32. multiply( ts_zscore(ts_delta(close,10),10), reverse(ts_rank(close,20)) )
+33. multiply( ts_zscore(ts_delta(close,20),10), reverse(ts_rank(close,30)) )
+34. multiply( ts_zscore(ts_delta(close,30),10), reverse(ts_rank(close,60)) )
+35. multiply( ts_zscore(ts_delta(close,5),10), reverse(ts_zscore(ts_delta(close,5),20)) )
+36. multiply( ts_zscore(ts_delta(close,10),10), reverse(ts_zscore(ts_delta(close,10),30)) )
+37. multiply( ts_zscore(ts_delta(close,20),10), reverse(ts_zscore(ts_delta(close,20),60)) )
+38. multiply( ts_zscore(ts_delta(close,30),10), reverse(ts_zscore(ts_delta(close,30),120)) )
+39. multiply( ts_zscore(ts_delta(close,5),10), reverse(ts_zscore(ts_delta(close,5),20)) )
+40. multiply( ts_zscore(ts_delta(close,10),10), reverse(ts_zscore(ts_delta(close,10),30)) )
+41. multiply( ts_zscore(ts_delta(close,20),10), reverse(ts_zscore(ts_delta(close,20),60)) )
+42. multiply( ts_zscore(ts_delta(close,30),10), reverse(ts_zscore(ts_delta(close,30),120)) )
+43. multiply( ts_zscore(ts_delta(close,5),10), reverse(ts_zscore(ts_delta(close,5),20)) )
+44. multiply( ts_zscore(ts_delta(close,10),10), reverse(ts_zscore(ts_delta(close,10),30)) )
+45. multiply( ts_zscore(ts_delta(close,20),10), reverse(ts_zscore(ts_delta(close,20),60)) )
+46. multiply( ts_zscore(ts_delta(close,30),10), reverse(ts_zscore(ts_delta(close,30),120)) )
+47. multiply( group_mean(ts_delta(close,5), group), reverse(ts_zscore(close,20)) )
+48. multiply( group_mean(ts_delta(close,10), group), reverse(ts_zscore(close,30)) )
+49. multiply( group_mean(ts_delta(close,20), group), reverse(ts_zscore(close,60)) )
+50. multiply( group_mean(ts_delta(close,30), group), reverse(ts_zscore(close,120)) )
+51. multiply( group_std_dev(ts_delta(close,5), group), reverse(ts_zscore(close,20)) )
+52. multiply( group_std_dev(ts_delta(close,10), group), reverse(ts_zscore(close,30)) )
+53. multiply( group_std_dev(ts_delta(close,20), group), reverse(ts_zscore(close,60)) )
+54. multiply( group_std_dev(ts_delta(close,30), group), reverse(ts_zscore(close,120)) )
+55. multiply( ts_corr(group_rank(close, group), group_rank(volume, group), 20), reverse(ts_rank(close,20)) )
+56. multiply( ts_corr(group_rank(close, group), group_rank(volume, group), 30), reverse(ts_rank(close,30)) )
+57. multiply( ts_corr(group_rank(close, group), group_rank(volume, group), 60), reverse(ts_rank(close,60)) )
+58. multiply( ts_corr(group_rank(close, group), group_rank(volume, group), 120), reverse(ts_rank(close,120)) )
+59. multiply( ts_covariance(close, group_mean(close, group), 20), reverse(ts_rank(close,20)) )
+60. multiply( ts_covariance(close, group_mean(close, group), 30), reverse(ts_rank(close,30)) )
+61. multiply( ts_covariance(close, group_mean(close, group), 60), reverse(ts_rank(close,60)) )
+62. multiply( ts_covariance(close, group_mean(close, group), 120), reverse(ts_rank(close,120)) )
+63. multiply( ts_decay_linear(group_mean(close, group), 10), reverse(ts_zscore(close,20)) )
+64. multiply( ts_decay_linear(group_mean(close, group), 20), reverse(ts_zscore(close,30)) )
+65. multiply( ts_decay_linear(group_mean(close, group), 30), reverse(ts_zscore(close,60)) )
+66. multiply( ts_decay_linear(group_mean(close, group), 60), reverse(ts_zscore(close,120)) )
+67. multiply( group_rank(ts_decay_linear(ts_delta(close,5), 10), group), reverse(ts_zscore(close,20)) )
+68. multiply( group_rank(ts_decay_linear(ts_delta(close,10), 10), group), reverse(ts_zscore(close,30)) )
+69. multiply( group_rank(ts_decay_linear(ts_delta(close,20), 10), group), reverse(ts_zscore(close,60)) )
+70. multiply( group_rank(ts_decay_linear(ts_delta(close,30), 10), group), reverse(ts_zscore(close,120)) )
+71. multiply( group_rank(ts_zscore(ts_delta(close,5),10), group), reverse(ts_rank(close,20)) )
+72. multiply( group_rank(ts_zscore(ts_delta(close,10),10), group), reverse(ts_rank(close,30)) )
+73. multiply( group_rank(ts_zscore(ts_delta(close,20),10), group), reverse(ts_rank(close,60)) )
+74. multiply( group_rank(ts_zscore(ts_delta(close,30),10), group), reverse(ts_rank(close,120)) )
+75. multiply( group_rank(ts_zscore(ts_delta(close,5),10), group), reverse(ts_zscore(close,20)) )
+76. multiply( group_rank(ts_zscore(ts_delta(close,10),10), group), reverse(ts_zscore(close,30)) )
+77. multiply( group_rank(ts_zscore(ts_delta(close,20),10), group), reverse(ts_zscore(close,60)) )
+78. multiply( group_rank(ts_zscore(ts_delta(close,30),10), group), reverse(ts_zscore(close,120)) )
+79. multiply( ts_regression(close, ts_step(1), 20, 0, 1), reverse(ts_zscore(ts_regression(close, ts_step(1), 20, 0, 1))) )
+80. multiply( ts_regression(close, ts_step(1), 30, 0, 1), reverse(ts_zscore(ts_regression(close, ts_step(1), 30, 0, 1))) )
+81. multiply( ts_regression(close, ts_step(1), 60, 0, 1), reverse(ts_zscore(ts_regression(close, ts_step(1), 60, 0, 1))) )
+82. multiply( ts_regression(close, ts_step(1), 120, 0, 1), reverse(ts_zscore(ts_regression(close, ts_step(1), 120, 0, 1))) )
+83. multiply( ts_regression(close, ts_step(1), 20, 0, 1), reverse(ts_corr(ts_regression(close, ts_step(1), 20, 0, 1), volume, 20)) )
+84. multiply( ts_regression(close, ts_step(1), 30, 0, 1), reverse(ts_corr(ts_regression(close, ts_step(1), 30, 0, 1), volume, 30)) )
+85. multiply( ts_regression(close, ts_step(1), 60, 0, 1), reverse(ts_corr(ts_regression(close, ts_step(1), 60, 0, 1), volume, 60)) )
+86. multiply( ts_regression(close, ts_step(1), 120, 0, 1), reverse(ts_corr(ts_regression(close, ts_step(1), 120, 0, 1), volume, 120)) )
+87. multiply( ts_zscore(ts_regression(close, ts_step(1), 20, 0, 1),20), reverse(ts_rank(ts_regression(close, ts_step(1), 20, 0, 1),10)) )
+88. multiply( ts_zscore(ts_regression(close, ts_step(1), 30, 0, 1),30), reverse(ts_rank(ts_regression(close, ts_step(1), 30, 0, 1),20)) )
+89. multiply( ts_zscore(ts_regression(close, ts_step(1), 60, 0, 1),60), reverse(ts_rank(ts_regression(close, ts_step(1), 60, 0, 1),30)) )
+90. multiply( ts_zscore(ts_regression(close, ts_step(1), 120, 0, 1),120), reverse(ts_rank(ts_regression(close, ts_step(1), 120, 0, 1),60)) )
+91. multiply( group_rank(ts_regression(close, ts_step(1), 20, 0, 1), group), reverse(ts_zscore(volume,20)) )
+92. multiply( group_rank(ts_regression(close, ts_step(1), 30, 0, 1), group), reverse(ts_zscore(volume,30)) )
+93. multiply( group_rank(ts_regression(close, ts_step(1), 60, 0, 1), group), reverse(ts_zscore(volume,60)) )
+94. multiply( group_rank(ts_regression(close, ts_step(1), 120, 0, 1), group), reverse(ts_zscore(volume,120)) )
+95. multiply( ts_zscore(ts_corr(close, group_mean(close, group),20),30), reverse(ts_rank(close,20)) )
+96. multiply( ts_zscore(ts_corr(close, group_mean(close, group),30),30), reverse(ts_rank(close,30)) )
+97. multiply( ts_zscore(ts_corr(close, group_mean(close, group),60),30), reverse(ts_rank(close,60)) )
+98. multiply( ts_zscore(ts_corr(close, group_mean(close, group),120),30), reverse(ts_rank(close,120)) )
+99. multiply( ts_zscore(ts_covariance(close, group_mean(close, group),20),30), reverse(ts_rank(close,20)) )
+100. multiply( ts_zscore(ts_covariance(close, group_mean(close, group),30),30), reverse(ts_rank(close,30)) )
+
+These factors combine various aspects of momentum (M), leadership (L), reversal (R), spillover (S), and quality (Q) across different lookback periods and industry groups. Each expression uses allowed WebSim functions and operators to capture unique market dynamics for industry rotation strategies.

generated_alpha/2025-12-09/094337.txt

@@ -0,0 +1,87 @@
+multiply(ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0,0.3")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)))
+if_else(ts_std_dev(close, 20) > ts_mean(ts_std_dev(close, 20), 120), divide(ts_delta(close, 5), ts_std_dev(close, 20)), ts_delta(close, 20))
+multiply(group_zscore(ts_rank(close, 10), bucket(rank(volume), "0,0.5,1")), ts_corr(group_mean(close, bucket(rank(close), "0,0.2")), group_mean(close, bucket(rank(close), "0.8,1")), 30))
+multiply(ts_zscore(ts_decay_linear(close, 10), 60), reverse(group_rank(ts_corr(volume, close, 5), bucket(rank(close), "0,0.25,0.5,0.75,1"))))
+ts_regression(group_scale(ts_sum(volume, 5), bucket(rank(close), "0,0.3,0.6,1")), ts_scale(close, 20), 30, 0, 1)
+if_else(ts_rank(ts_std_dev(close, 5), 60) < 0.3, ts_delta(ts_mean(close, 10), 5), reverse(ts_delta(ts_mean(close, 30), 5)))
+multiply(ts_corr(ts_av_diff(close, 10), ts_av_diff(volume, 10), 20), group_mean(ts_zscore(close, 20), bucket(rank(ts_delta(close, 1)), "0,0.2,0.8,1")))
+divide(ts_delta(group_mean(close, bucket(rank(volume), "0.7,1")), 10), ts_std_dev(group_mean(close, bucket(rank(close), "0,0.5")), 20))
+subtract(ts_rank(ts_covariance(close, volume, 10), 30), group_rank(ts_rank(close, 5), bucket(rank(close), "0,0.25,0.5,0.75,1")))
+multiply(ts_zscore(ts_sum(close, 5), 20), if_else(ts_corr(close, volume, 10) < ts_mean(ts_corr(close, volume, 10), 60), -1, 1))
+ts_delta(ts_regression(group_mean(close, bucket(rank(ts_delta(close, 5)), "0.5,1")), ts_step(1), 60, 0, 1), 5)
+multiply(group_neutralize(ts_decay_linear(close, 20), bucket(rank(volume), "0,0.33,0.66,1")), ts_corr(ts_zscore(close, 10), ts_zscore(volume, 10), 30))
+if_else(ts_std_dev(close, 10) > ts_mean(ts_std_dev(close, 10), 120), ts_rank(ts_delta(close, 2), 20), ts_rank(ts_delta(close, 10), 20))
+multiply(ts_scale(ts_corr(group_mean(close, bucket(rank(close), "0,0.2")), group_mean(close, bucket(rank(close), "0.8,1")), 20), 60), reverse(ts_rank(volume, 10)))
+divide(ts_zscore(group_mean(ts_delta(close, 1), bucket(rank(volume), "0.8,1")), 30), ts_std_dev(group_mean(close, bucket(rank(close), "0,0.5")), 30))
+multiply(ts_delta(ts_mean(close, 20), 5), group_zscore(ts_sum(volume, 5), bucket(rank(ts_delta(close, 1)), "0,0.3,0.7,1")))
+subtract(ts_regression(close, volume, 30, 0, 1), group_mean(ts_regression(close, volume, 30, 0, 1), bucket(rank(close), "0,0.2,0.4,0.6,0.8,1")))
+if_else(ts_rank(close, 30) > 0.7, reverse(ts_delta(close, 5)), ts_delta(ts_mean(close, 10), 5))
+multiply(ts_corr(ts_decay_linear(close, 10), ts_decay_linear(volume, 10), 20), reverse(group_scale(ts_rank(volume, 5), bucket(rank(close), "0,0.5,1"))))
+ts_zscore(group_mean(ts_av_diff(close, 5), bucket(rank(ts_delta(close, 1)), "0.6,1")), 40)
+multiply(if_else(ts_std_dev(close, 5) > ts_mean(ts_std_dev(close, 5), 120), ts_rank(close, 10), ts_rank(close, 30)), ts_corr(ts_zscore(close, 20), ts_zscore(volume, 20), 10))
+divide(group_mean(ts_delta(close, 10), bucket(rank(volume), "0.7,1")), ts_std_dev(group_mean(close, bucket(rank(close), "0,0.3")), 20))
+ts_regression(ts_sum(close, 5), group_mean(ts_sum(volume, 5), bucket(rank(close), "0,0.25,0.5,0.75,1")), 40, 0, 1)
+multiply(ts_rank(ts_covariance(close, volume, 15), 30), reverse(group_rank(ts_zscore(volume, 10), bucket(rank(close), "0,0.5,1"))))
+if_else(ts_corr(close, volume, 20) < 0, ts_delta(close, 2), ts_delta(ts_mean(close, 15), 5))
+multiply(ts_zscore(ts_decay_linear(volume, 20), 50), group_zscore(ts_rank(close, 10), bucket(rank(ts_delta(close, 5)), "0,0.4,0.8,1")))
+subtract(ts_mean(close, 10), group_mean(ts_mean(close, 10), bucket(rank(volume), "0,0.33,0.66,1")))
+multiply(ts_delta(group_mean(ts_regression(close, ts_step(1), 30, 0, 1), bucket(rank(volume), "0.5,1")), 10), ts_corr(ts_av_diff(close, 5), ts_av_diff(volume, 5), 20))
+if_else(ts_rank(ts_std_dev(close, 10), 60) > 0.8, reverse(ts_zscore(close, 10)), ts_zscore(ts_delta(close, 5), 20))
+multiply(ts_scale(ts_corr(group_mean(close, bucket(rank(close), "0,0.1")), group_mean(close, bucket(rank(close), "0.9,1")), 40), 80), ts_rank(ts_sum(volume, 3), 10))
+divide(ts_zscore(group_mean(ts_delta(close, 2), bucket(rank(ts_delta(volume, 1)), "0.8,1")), 30), ts_std_dev(close, 30))
+multiply(ts_delta(ts_mean(volume, 20), 5), group_scale(ts_corr(close, volume, 5), bucket(rank(close), "0,0.25,0.5,0.75,1")))
+ts_regression(ts_av_diff(close, 10), group_mean(ts_av_diff(volume, 10), bucket(rank(close), "0,0.2,0.8,1")), 50, 0, 1)
+multiply(ts_rank(ts_decay_linear(close, 15), 30), reverse(group_zscore(ts_sum(volume, 10), bucket(rank(ts_delta(close, 1)), "0,0.5,1"))))
+if_else(ts_mean(ts_corr(close, volume, 5), 20) > 0.5, ts_delta(close, 3), reverse(ts_delta(close, 10)))
+multiply(ts_zscore(ts_covariance(close, volume, 20), 40), group_mean(ts_rank(close, 5), bucket(rank(volume), "0.6,1")))
+subtract(ts_sum(close, 5), group_mean(ts_sum(close, 5), bucket(rank(ts_delta(volume, 1)), "0,0.25,0.5,0.75,1")))
+multiply(ts_delta(group_scale(close, bucket(rank(volume), "0,0.5,1")), 10), ts_corr(ts_zscore(close, 15), ts_zscore(volume, 15), 25))
+if_else(ts_std_dev(close, 20) < ts_mean(ts_std_dev(close, 20), 120), ts_rank(ts_delta(close, 1), 15), reverse(ts_rank(ts_delta(close, 5), 15)))
+multiply(ts_scale(ts_corr(group_mean(ts_delta(close, 1), bucket(rank(close), "0,0.2")), group_mean(ts_delta(close, 1), bucket(rank(close), "0.8,1")), 30), 60), ts_rank(volume, 20))
+divide(group_zscore(ts_mean(close, 10), bucket(rank(ts_delta(volume, 1)), "0.7,1")), ts_std_dev(group_zscore(close, 10), 30))
+multiply(ts_delta(ts_regression(close, volume, 40, 0, 1), 5), group_rank(ts_corr(close, volume, 10), bucket(rank(close), "0,0.33,0.66,1")))
+ts_regression(ts_decay_linear(close, 10), group_mean(ts_decay_linear(volume, 10), bucket(rank(close), "0,0.4,0.8,1")), 60, 0, 1)
+multiply(ts_rank(ts_av_diff(close, 20), 40), reverse(group_scale(ts_zscore(volume, 15), bucket(rank(ts_delta(close, 5)), "0,0.5,1"))))
+if_else(ts_rank(close, 20) < 0.3, ts_delta(close, 5), reverse(ts_delta(ts_mean(close, 15), 5)))
+multiply(ts_zscore(ts_sum(volume, 20), 50), group_zscore(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(volume), "0.5,1")))
+subtract(ts_covariance(close, volume, 15), group_mean(ts_covariance(close, volume, 15), bucket(rank(close), "0,0.2,0.4,0.6,0.8,1")))
+multiply(ts_delta(group_mean(ts_rank(close, 10), bucket(rank(ts_delta(volume, 1)), "0.6,1")), 10), ts_corr(ts_av_diff(close, 10), ts_av_diff(volume, 10), 30))
+if_else(ts_mean(ts_corr(close, volume, 10), 40) < 0, ts_rank(ts_delta(close, 2), 20), ts_rank(ts_delta(close, 8), 20))
+multiply(ts_scale(ts_corr(group_zscore(close, 10), group_zscore(volume, 10), 20), 40), reverse(ts_rank(ts_sum(volume, 5), 15)))
+divide(ts_zscore(group_mean(ts_regression(close, ts_step(1), 25, 0, 1), bucket(rank(volume), "0.8,1")), 35), ts_std_dev(ts_zscore(close, 15), 35))
+multiply(ts_delta(ts_mean(ts_corr(close, volume, 5), 20), 5), group_scale(ts_rank(ts_delta(close, 1), 10), bucket(rank(close), "0,0.25,0.5,0.75,1")))
+ts_regression(ts_zscore(close, 20), group_mean(ts_zscore(volume, 20), bucket(rank(ts_delta(close, 5)), "0,0.3,0.7,1")), 70, 0, 1)
+multiply(ts_rank(ts_decay_linear(volume, 25), 50), reverse(group_rank(ts_av_diff(close, 15), bucket(rank(close), "0,0.5,1"))))
+if_else(ts_std_dev(close, 15) > ts_mean(ts_std_dev(close, 15), 120), reverse(ts_zscore(ts_delta(close, 3), 20)), ts_zscore(ts_delta(close, 10), 20))
+multiply(ts_zscore(ts_covariance(ts_delta(close, 1), ts_delta(volume, 1), 20), 40), group_mean(ts_scale(close, 30), bucket(rank(volume), "0.7,1")))
+subtract(ts_sum(ts_delta(close, 1), 10), group_mean(ts_sum(ts_delta(close, 1), 10), bucket(rank(volume), "0,0.33,0.66,1")))
+multiply(ts_delta(group_zscore(ts_rank(volume, 10), bucket(rank(close), "0,0.4,0.8,1")), 5), ts_corr(ts_zscore(ts_sum(close, 5), 20), ts_zscore(ts_sum(volume, 5), 20), 25))
+if_else(ts_rank(ts_corr(close, volume, 5), 30) > 0.7, reverse(ts_delta(close, 4)), ts_delta(ts_mean(close, 12), 6))
+multiply(ts_scale(ts_corr(group_mean(ts_zscore(close, 15), bucket(rank(close), "0,0.15")), group_mean(ts_zscore(close, 15), bucket(rank(close), "0.85,1")), 35), 70), ts_rank(ts_decay_linear(volume, 10), 20))
+divide(group_mean(ts_zscore(ts_delta(close, 2), 30), bucket(rank(ts_delta(volume, 1)), "0.9,1")), ts_std_dev(group_mean(close, bucket(rank(close), "0,0.2")), 40))
+multiply(ts_delta(ts_regression(volume, ts_step(1), 30, 0, 1), 5), group_rank(ts_zscore(ts_corr(close, volume, 10), 20), bucket(rank(close), "0,0.25,0.5,0.75,1")))
+ts_regression(ts_av_diff(volume, 20), group_mean(ts_av_diff(close, 20), bucket(rank(ts_delta(volume, 1)), "0,0.3,0.7,1")), 80, 0, 1)
+multiply(ts_rank(ts_sum(close, 15), 30), reverse(group_scale(ts_corr(close, volume, 8), bucket(rank(ts_delta(close, 5)), "0,0.5,1"))))
+if_else(ts_mean(ts_std_dev(close, 5), 60) > ts_std_dev(close, 60), ts_rank(ts_delta(close, 1), 25), reverse(ts_rank(ts_delta(close, 7), 25)))
+multiply(ts_zscore(ts_decay_linear(ts_corr(close, volume, 10), 20), 50), group_zscore(ts_regression(close, volume, 25, 0, 1), bucket(rank(volume), "0.6,1")))
+subtract(ts_covariance(ts_delta(close, 1), ts_delta(volume, 1), 20), group_mean(ts_covariance(ts_delta(close, 1), ts_delta(volume, 1), 20), bucket(rank(close), "0,0.2,0.4,0.6,0.8,1")))
+multiply(ts_delta(group_mean(ts_scale(close, 40), bucket(rank(ts_delta(volume, 1)), "0.7,1")), 10), ts_corr(ts_zscore(ts_mean(close, 10), 30), ts_zscore(ts_mean(volume, 10), 30), 20))
+if_else(ts_rank(close, 40) < 0.2, ts_delta(close, 8), if_else(ts_rank(close, 40) > 0.8, reverse(ts_delta(close, 8)), ts_delta(close, 3)))
+multiply(ts_scale(ts_corr(group_rank(ts_delta(close, 1), bucket(rank(volume), "0,0.5,1")), group_rank(ts_delta(volume, 1), bucket(rank(volume), "0,0.5,1")), 25), 50), ts_rank(ts_sum(close, 8), 15))
+divide(ts_zscore(group_mean(ts_regression(volume, ts_step(1), 35, 0, 1), bucket(rank(close), "0.8,1")), 45), ts_std_dev(ts_zscore(volume, 25), 45))
+multiply(ts_delta(ts_mean(ts_av_diff(close, 5), 25), 10), group_scale(ts_rank(ts_corr(close, volume, 12), 30), bucket(rank(close), "0,0.33,0.66,1")))
+ts_regression(ts_zscore(ts_delta(close, 1), 30), group_mean(ts_zscore(ts_delta(volume, 1), 30), bucket(rank(ts_delta(close, 5)), "0,0.4,0.8,1")), 90, 0, 1)
+multiply(ts_rank(ts_decay_linear(ts_av_diff(close, 10), 30), 60), reverse(group_rank(ts_zscore(ts_sum(volume, 10), 20), bucket(rank(close), "0,0.5,1"))))
+if_else(ts_corr(ts_mean(close, 10), ts_mean(volume, 10), 20) < ts_mean(ts_corr(ts_mean(close, 10), ts_mean(volume, 10), 20), 100), ts_delta(close, 4), reverse(ts_delta(close, 15)))
+multiply(ts_zscore(ts_covariance(ts_mean(close, 5), ts_mean(volume, 5), 25), 55), group_mean(ts_scale(ts_delta(close, 1), 20), bucket(rank(volume), "0.5,1")))
+subtract(ts_sum(ts_zscore(close, 20), 10), group_mean(ts_sum(ts_zscore(close, 20), 10), bucket(rank(ts_delta(volume, 1)), "0,0.25,0.5,0.75,1")))
+multiply(ts_delta(group_zscore(ts_rank(ts_corr(close, volume, 8), 25), bucket(rank(close), "0,0.3,0.7,1")), 5), ts_corr(ts_zscore(ts_decay_linear(close, 15), 30), ts_zscore(ts_decay_linear(volume, 15), 30), 35))
+if_else(ts_std_dev(ts_delta(close, 1), 10) > ts_mean(ts_std_dev(ts_delta(close, 1), 10), 120), ts_rank(ts_delta(close, 2), 30), reverse(ts_rank(ts_delta(close, 10), 30)))
+multiply(ts_scale(ts_corr(group_mean(ts_regression(close, ts_step(1), 40, 0, 1), bucket(rank(volume), "0,0.2")), group_mean(ts_regression(close, ts_step(1), 40, 0, 1), bucket(rank(volume), "0.8,1")), 50), 100), ts_rank(ts_decay_linear(volume, 20), 25))
+divide(group_mean(ts_zscore(ts_delta(close, 3), 35), bucket(rank(ts_delta(volume, 2)), "0.9,1")), ts_std_dev(group_mean(ts_delta(close, 1), bucket(rank(close), "0,0.15")), 50))
+multiply(ts_delta(ts_regression(ts_av_diff(close, 5), ts_av_diff(volume, 5), 50, 0, 1), 10), group_rank(ts_scale(ts_corr(close, volume, 15), 40), bucket(rank(close), "0,0.25,0.5,0.75,1")))
+ts_regression(ts_sum(ts_delta(close, 1), 20), group_mean(ts_sum(ts_delta(volume, 1), 20), bucket(rank(ts_delta(close, 5)), "0,0.35,0.75,1")), 110, 0, 1)
+multiply(ts_rank(ts_decay_linear(ts_zscore(close, 25), 40), 70), reverse(group_scale(ts_rank(ts_covariance(close, volume, 10), 35), bucket(rank(ts_delta(volume, 1)), "0,0.5,1"))))
+if_else(ts_rank(ts_mean(close, 15), 50) > 0.75, reverse(ts_zscore(ts_delta(close, 4), 25)), ts_zscore(ts_delta(close, 12), 25))
+multiply(ts_zscore(ts_covariance(ts_zscore(close, 20), ts_zscore(volume, 20), 30), 65), group_mean(ts_rank(ts_decay_linear(close, 20), 30), bucket(rank(volume), "0.6,1")))
+subtract(ts_covariance(ts_mean(close, 8), ts_mean(volume, 8), 20), group_mean(ts_covariance(ts_mean(close, 8), ts_mean(volume, 8), 20), bucket(rank(ts_delta(close, 1)), "0,0.2,0.4,0.6,0.8,1")))

generated_alpha/2025-12-11/173635.txt

@@ -0,0 +1,110 @@
+multiply(ts_delta(ts_mean(close, 20), 5), group_zscore(close, industry))
+subtract(group_rank(volume, industry), ts_rank(ts_av_diff(close, 30), 60))
+multiply(ts_decay_linear(ts_delta(close, 10), 20), group_mean(ts_zscore(close, 60), industry))
+add(ts_corr(close, volume, 30), if_else(ts_std_dev(close, 20) > ts_mean(ts_std_dev(close, 20), 60), 1, -1))
+divide(ts_regression(close, ts_step(1), 30, 0, 1), group_std(close, industry))
+multiply(ts_zscore(ts_delta(close, 5), 20), group_neutralize(ts_mean(close, 60), industry))
+subtract(ts_scale(close, 90), group_scale(ts_delta(volume, 5), industry))
+multiply(ts_av_diff(close, 30), group_zscore(ts_mean(close, 20), industry))
+add(ts_corr(ts_zscore(close, 10), ts_zscore(volume, 10), 20), ts_rank(ts_delta(close, 5), 60))
+divide(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), industry), ts_std_dev(close, 30))
+multiply(ts_decay_linear(ts_delta(ts_mean(close, 10), 5), 15), group_rank(close, industry))
+subtract(ts_rank(ts_mean(close, 60), 90), group_zscore(ts_delta(close, 3), industry))
+multiply(ts_std_dev(close, 20), if_else(ts_corr(close, volume, 10) > 0.5, 1, -1))
+add(ts_regression(close, ts_step(1), 60, 0, 1), group_mean(ts_zscore(volume, 30), industry))
+divide(ts_delta(ts_mean(close, 30), 10), group_neutralize(ts_std_dev(close, 20), industry))
+multiply(ts_zscore(ts_delta(close, 3), 10), group_scale(ts_mean(volume, 20), industry))
+subtract(ts_av_diff(close, 60), group_rank(ts_delta(close, 5), industry))
+multiply(ts_corr(ts_mean(close, 10), ts_mean(volume, 10), 30), ts_rank(ts_delta(close, 5), 30))
+add(ts_decay_linear(ts_delta(close, 5), 10), group_zscore(ts_mean(close, 90), industry))
+divide(ts_regression(close, ts_step(1), 20, 0, 1), ts_mean(group_std(close, industry), 30))
+multiply(ts_scale(ts_delta(close, 5), 20), group_mean(ts_zscore(ts_delta(close, 3), 10), industry))
+subtract(ts_rank(ts_mean(close, 30), 60), group_neutralize(ts_delta(close, 5), industry))
+multiply(ts_av_diff(close, 20), group_scale(ts_mean(ts_delta(close, 3), 10), industry))
+add(ts_corr(ts_zscore(close, 20), ts_zscore(volume, 20), 30), ts_rank(ts_delta(close, 5), 30))
+divide(ts_std_dev(ts_delta(close, 5), 20), group_mean(ts_regression(close, ts_step(1), 30, 0, 1), industry))
+multiply(ts_decay_linear(ts_mean(close, 10), 15), group_zscore(ts_delta(close, 3), industry))
+subtract(ts_rank(ts_mean(close, 60), 90), group_neutralize(ts_std_dev(close, 30), industry))
+multiply(ts_zscore(ts_delta(close, 5), 30), group_scale(ts_mean(close, 20), industry))
+add(ts_regression(close, ts_step(1), 30, 0, 1), group_mean(ts_zscore(ts_delta(close, 5), 30), industry))
+divide(ts_delta(ts_mean(close, 20), 5), group_rank(ts_std_dev(close, 10), industry))
+multiply(ts_av_diff(close, 30), group_mean(ts_regression(close, ts_step(1), 20, 0, 1), industry))
+subtract(ts_corr(close, volume, 20), group_zscore(ts_mean(close, 60), industry))
+multiply(ts_scale(ts_delta(close, 5), 30), group_neutralize(ts_delta(volume, 5), industry))
+add(ts_rank(ts_std_dev(close, 20), 60), group_scale(ts_mean(close, 30), industry))
+divide(ts_regression(close, ts_step(1), 90, 0, 1), ts_mean(group_zscore(close, industry), 30))
+multiply(ts_decay_linear(ts_delta(close, 3), 20), group_rank(ts_zscore(close, 30), industry))
+subtract(ts_av_diff(close, 60), group_mean(ts_std_dev(close, 20), industry))
+multiply(ts_corr(ts_mean(close, 5), ts_mean(volume, 5), 20), ts_rank(ts_delta(close, 3), 30))
+add(ts_zscore(ts_delta(close, 5), 20), group_neutralize(ts_mean(close, 60), industry))
+divide(ts_std_dev(ts_delta(close, 3), 10), group_mean(ts_regression(close, ts_step(1), 60, 0, 1), industry))
+multiply(ts_scale(ts_mean(close, 30), 60), group_zscore(ts_delta(close, 5), industry))
+subtract(ts_rank(ts_mean(close, 20), 60), group_neutralize(ts_zscore(close, 30), industry))
+multiply(ts_av_diff(close, 90), group_scale(ts_mean(ts_delta(close, 3), 10), industry))
+add(ts_corr(ts_zscore(close, 10), ts_zscore(volume, 10), 60), ts_rank(ts_delta(close, 5), 90))
+divide(ts_regression(close, ts_step(1), 30, 0, 1), group_std(ts_delta(close, 5), industry))
+multiply(ts_decay_linear(ts_delta(ts_mean(close, 5), 3), 15), group_rank(ts_zscore(close, 60), industry))
+subtract(ts_rank(ts_std_dev(close, 30), 90), group_mean(ts_zscore(ts_delta(close, 3), 10), industry))
+multiply(ts_zscore(ts_delta(close, 3), 20), group_neutralize(ts_mean(close, 30), industry))
+add(ts_mean(ts_regression(close, ts_step(1), 20, 0, 1), 60), group_scale(ts_delta(close, 5), industry))
+divide(ts_delta(ts_mean(close, 30), 10), group_zscore(ts_std_dev(close, 20), industry))
+multiply(ts_av_diff(close, 10), group_mean(ts_regression(close, ts_step(1), 90, 0, 1), industry))
+subtract(ts_corr(close, volume, 10), group_neutralize(ts_mean(close, 60), industry))
+multiply(ts_scale(ts_delta(close, 5), 90), group_rank(ts_zscore(ts_delta(close, 3), 20), industry))
+add(ts_rank(ts_mean(close, 60), 90), group_scale(ts_std_dev(close, 10), industry))
+divide(ts_regression(close, ts_step(1), 20, 0, 1), ts_mean(group_rank(close, industry), 30))
+multiply(ts_decay_linear(ts_delta(close, 3), 30), group_zscore(ts_mean(close, 20), industry))
+subtract(ts_av_diff(close, 30), group_neutralize(ts_delta(close, 5), industry))
+multiply(ts_corr(ts_mean(close, 30), ts_mean(volume, 30), 60), ts_rank(ts_delta(close, 5), 60))
+add(ts_zscore(ts_delta(close, 5), 60), group_mean(ts_std_dev(close, 20), industry))
+divide(ts_std_dev(ts_delta(close, 5), 30), group_mean(ts_regression(close, ts_step(1), 20, 0, 1), industry))
+multiply(ts_scale(ts_mean(close, 60), 90), group_neutralize(ts_delta(close, 3), industry))
+subtract(ts_rank(ts_mean(close, 30), 60), group_zscore(ts_mean(close, 90), industry))
+multiply(ts_av_diff(close, 20), group_scale(ts_mean(ts_delta(close, 5), 3), industry))
+add(ts_corr(ts_zscore(close, 20), ts_zscore(volume, 20), 90), ts_rank(ts_delta(close, 3), 30))
+divide(ts_regression(close, ts_step(1), 60, 0, 1), group_std(ts_delta(close, 3), industry))
+multiply(ts_decay_linear(ts_delta(close, 5), 20), group_rank(ts_zscore(close, 20), industry))
+subtract(ts_rank(ts_std_dev(close, 10), 30), group_mean(ts_zscore(ts_delta(close, 5), 20), industry))
+multiply(ts_zscore(ts_delta(close, 3), 10), group_neutralize(ts_mean(close, 60), industry))
+add(ts_mean(ts_regression(close, ts_step(1), 30, 0, 1), 90), group_scale(ts_delta(close, 5), industry))
+divide(ts_delta(ts_mean(close, 10), 5), group_zscore(ts_std_dev(close, 30), industry))
+multiply(ts_av_diff(close, 60), group_mean(ts_regression(close, ts_step(1), 20, 0, 1), industry))
+subtract(ts_corr(close, volume, 30), group_neutralize(ts_mean(close, 20), industry))
+multiply(ts_scale(ts_delta(close, 3), 60), group_rank(ts_zscore(ts_delta(close, 5), 10), industry))
+add(ts_rank(ts_mean(close, 90), 90), group_scale(ts_std_dev(close, 30), industry))
+divide(ts_regression(close, ts_step(1), 90, 0, 1), ts_mean(group_zscore(close, industry), 60))
+multiply(ts_decay_linear(ts_delta(ts_mean(close, 3), 5), 15), group_zscore(ts_mean(close, 30), industry))
+subtract(ts_av_diff(close, 20), group_neutralize(ts_delta(close, 3), industry))
+multiply(ts_corr(ts_mean(close, 20), ts_mean(volume, 20), 30), ts_rank(ts_delta(close, 5), 20))
+add(ts_zscore(ts_delta(close, 5), 30), group_mean(ts_std_dev(close, 10), industry))
+divide(ts_std_dev(ts_delta(close, 3), 20), group_mean(ts_regression(close, ts_step(1), 30, 0, 1), industry))
+multiply(ts_scale(ts_mean(close, 20), 30), group_neutralize(ts_delta(close, 5), industry))
+subtract(ts_rank(ts_mean(close, 60), 90), group_zscore(ts_mean(close, 30), industry))
+multiply(ts_av_diff(close, 90), group_scale(ts_mean(ts_delta(close, 3), 5), industry))
+add(ts_corr(ts_zscore(close, 30), ts_zscore(volume, 30), 60), ts_rank(ts_delta(close, 5), 30))
+divide(ts_regression(close, ts_step(1), 20, 0, 1), group_std(ts_delta(close, 5), industry))
+multiply(ts_decay_linear(ts_delta(close, 5), 10), group_rank(ts_zscore(close, 90), industry))
+subtract(ts_rank(ts_std_dev(close, 20), 60), group_mean(ts_zscore(ts_delta(close, 3), 30), industry))
+multiply(ts_zscore(ts_delta(close, 3), 20), group_neutralize(ts_mean(close, 20), industry))
+add(ts_mean(ts_regression(close, ts_step(1), 60, 0, 1), 30), group_scale(ts_delta(close, 5), industry))
+divide(ts_delta(ts_mean(close, 30), 5), group_zscore(ts_std_dev(close, 20), industry))
+multiply(ts_av_diff(close, 30), group_mean(ts_regression(close, ts_step(1), 90, 0, 1), industry))
+subtract(ts_corr(close, volume, 20), group_neutralize(ts_mean(close, 90), industry))
+multiply(ts_scale(ts_delta(close, 5), 30), group_rank(ts_zscore(ts_delta(close, 3), 20), industry))
+add(ts_rank(ts_mean(close, 20), 60), group_scale(ts_std_dev(close, 60), industry))
+divide(ts_regression(close, ts_step(1), 30, 0, 1), ts_mean(group_rank(close, industry), 60))
+multiply(ts_decay_linear(ts_delta(close, 3), 20), group_zscore(ts_mean(close, 60), industry))
+subtract(ts_av_diff(close, 60), group_neutralize(ts_delta(close, 5), industry))
+multiply(ts_corr(ts_mean(close, 10), ts_mean(volume, 10), 90), ts_rank(ts_delta(close, 5), 60))
+add(ts_zscore(ts_delta(close, 5), 20), group_mean(ts_std_dev(close, 30), industry))
+divide(ts_std_dev(ts_delta(close, 5), 10), group_mean(ts_regression(close, ts_step(1), 60, 0, 1), industry))
+multiply(ts_scale(ts_mean(close, 90), 90), group_neutralize(ts_delta(close, 3), industry))
+subtract(ts_rank(ts_mean(close, 30), 90), group_zscore(ts_mean(close, 60), industry))
+multiply(ts_av_diff(close, 10), group_scale(ts_mean(ts_delta(close, 5), 3), industry))
+add(ts_corr(ts_zscore(close, 10), ts_zscore(volume, 10), 30), ts_rank(ts_delta(close, 3), 20))
+divide(ts_regression(close, ts_step(1), 90, 0, 1), group_std(ts_delta(close, 3), industry))
+multiply(ts_decay_linear(ts_delta(ts_mean(close, 5), 3), 30), group_rank(ts_zscore(close, 30), industry))
+subtract(ts_rank(ts_std_dev(close, 60), 90), group_mean(ts_zscore(ts_delta(close, 5), 10), industry))
+multiply(ts_zscore(ts_delta(close, 3), 60), group_neutralize(ts_mean(close, 30), industry))
+add(ts_mean(ts_regression(close, ts_step(1), 20, 0, 1), 60), group_scale(ts_delta(close, 5), industry))
+divide(ts_delta(ts_mean(close, 60), 10), group_zscore(ts_std_dev(close, 30), industry))

generated_alpha/2025-12-11/175755.txt

@@ -0,0 +1,82 @@
+ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), industry, 0, 1), 5) * bucket(group_rank(close, industry), "0.7,1")
+ts_regression(group_mean(close, industry), ts_step(1), 30, 0, 1) * ts_rank(close, 20)
+ts_zscore(ts_delta(close, 10), 60) * bucket(group_rank(close, industry), "0.8,0.2")
+group_mean(ts_delta(close, 10), industry) - ts_mean(close, 20)
+ts_delta(group_mean(close, industry), 20) * reverse(if_else(group_rank(close, industry) > 0.7, 1, 0))
+ts_corr(group_mean(close, industry), ts_mean(sector_index, 30), 20) * ts_zscore(volume, 30)
+ts_regression(group_mean(close, industry), ts_step(1), 60, 0, 1) * group_mean(ts_zscore(volume, 30), industry)
+(1 - ts_zscore(group_mean(close, industry), 60)) * ts_rank(close, 30)
+ts_delta(group_mean(close, industry), 10) * ts_scale(ts_delay(returns, 1), 5)
+group_mean(ts_delta(close, 10), industry) * ts_ranks(industry, 20)
+ts_delta(group_mean(close, industry), 5) / ts_std_dev(group_mean(close, industry), 60)
+multiply(ts_decay_linear(group_mean(close, industry), 20, densefalse), ts_rank(ts_regression(close, ts_step(1), 30, 0, 1), 20))
+ts_zscore(ts_delta(group_mean(close, industry), 5), 60) * bucket(group_rank(close, industry), "0.7,0.3,1")
+(group_mean(close, industry) - ts_delay(group_mean(close, industry), 10)) * ts_scale(volume, 30)
+divide(group_mean(ts_delta(close, 5), industry), group_mean(ts_delta(close, 20), industry))
+ts_regression(group_mean(close, industry), ts_step(1), 40, 0, 1) * group_mean(bucket(rank(close), "0.8,1"), industry)
+ts_corr(group_mean(close, industry), group_mean(tech_index, 30), 20) * ts_zscore(volume, 30)
+ts_delta(group_mean(close, industry), 15) * if_else(group_rank(close, industry) > 0.7, 1, -1)
+ts_scale(multiply(group_mean(close, industry), ts_rank(volume, 20)), 30)
+group_zscore(group_mean(close, industry), industry) * ts_delta(returns, 5)
+ts_regression(group_mean(close, industry), ts_step(1), 30, 0, 1) * group_mean(bucket(rank(close), "0.6,1"), industry)
+ts_zscore(ts_delta(group_mean(close, industry), 10), 60) * reverse(group_rank(close, industry))
+group_mean(ts_delta(close, 5), industry) - group_mean(ts_delta(close, 20), industry)
+ts_delta(group_mean(close, industry), 20) * group_rank(ts_mean(volume, 30), industry)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 10)) * ts_scale(volume, 20)
+ts_regression(group_mean(close, industry), ts_step(1), 20, 0, 1) * bucket(group_rank(close, industry), "0.75,1")
+ts_corr(group_mean(close, industry), group_mean(sector_index, 30), 20) * ts_zscore(returns, 30)
+ts_delta(group_mean(close, industry), 10) * if_else(ts_std_dev(returns, 60) > 0.1, ts_rank(close, 30), -ts_rank(close, 30))
+group_mean(ts_delta(close, 5), industry) * ts_scale(ts_regression(close, ts_step(1), 60, 0, 1), 30)
+ts_zscore(group_mean(close, industry), 60) * reverse(ts_delta(volume, 10))
+ts_regression(group_mean(close, industry), ts_step(1), 30, 0, 1) * group_mean(bucket(rank(volume), "0.9,1"), industry)
+ts_delta(group_mean(close, industry), 30) * group_rank(ts_mean(volume, 20), industry)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 5)) * ts_rank(returns, 30)
+ts_corr(group_mean(close, industry), group_mean(energy_index, 30), 20) * ts_zscore(volume, 20)
+ts_delta(group_mean(close, industry), 15) * if_else(group_rank(close, industry) > 0.6, 1, -0.5)
+(ts_mean(group_mean(close, industry), 10) - group_mean(close, industry)) * ts_scale(volume, 30)
+group_mean(ts_delta(close, 10), industry) * ts_regression(ts_rank(close, 30), ts_step(1), 20, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 20), 60) * bucket(group_rank(close, industry), "0.7,0.8,1")
+ts_regression(group_mean(close, industry), ts_step(1), 60, 0, 1) * group_mean(ts_delta(volume, 10), industry)
+ts_delta(group_mean(close, industry), 5) * ts_scale(group_rank(volume, industry), 20)
+divide(ts_delay(group_mean(close, industry), 5), group_mean(close, industry)) * ts_rank(close, 30)
+ts_corr(group_mean(close, industry), group_mean(market_index, 30), 20) * ts_zscore(volume, 20)
+ts_delta(group_mean(close, industry), 10) * if_else(quantile(group_rank(close, industry), 0.9) > 0.9, 1, -1)
+group_mean(ts_delta(close, 5), industry) * ts_regression(group_rank(close, industry), ts_step(1), 40, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 15), 60) * reverse(bucket(rank(close), "0.8,1"))
+ts_regression(group_mean(close, industry), ts_step(1), 30, 0, 1) * group_mean(bucket(rank(close), "0.6,1"), industry)
+(ts_mean(group_mean(close, industry), 20) - group_mean(close, industry)) * ts_scale(volume, 30)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 10)) * group_rank(ts_mean(volume, 20), industry)
+ts_corr(group_mean(close, industry), group_mean(banking_index, 30), 20) * ts_zscore(returns, 30)
+ts_delta(group_mean(close, industry), 20) * if_else(quantile(group_std_dev(close, industry), 0.2) < ts_std_dev(close, industry), 1, -1)
+group_mean(ts_delat(close, industry), 20)
+group_mean(ts_delta(close, 10), industry) * ts_regression(group_rank(close, industry), ts_step(1), 30, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 5), 60) * bucket(group_rank(close, industry), "0.7,1")
+ts_regression(group_mean(close, industry), ts_step(1), 20, 0, 1) * group_mean(ts_delta(volume, 10), industry)
+ts_delta(group_mean(close, industry), 10) * ts_scale(group_rank(volume, industry), 60)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 5)) * ts_rank(returns, 30)
+ts_corr(group_mean(close, industry), group_mean(technology_index, 20), 20) * ts_zscore(volume, 20)
+ts_delta(group_mean(close, industry), 5) * if_else(group_rank(close, industry) > 0.65, 1, -1)
+group_mean(ts_delta(close, 5), industry) * ts_regression(group_rank(close, industry), ts_step(1), 40, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 15), 60) * reverse(bucket(rank(close), "0.7,1"))
+ts_regression(group_mean(close, industry), ts_step(1), 30, 0, 1) * group_mean(bucket(rank(close), "0.65,1"), industry)
+(ts_mean(group_mean(close, industry), 10) - group_mean(close, industry)) * ts_scale(volume, 30)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 10)) * group_rank(ts_mean(volume, 20), industry)
+ts_corr(group_mean(close, industry), group_mean(consumer_index, 30), 20) * ts_zscore(returns, 30)
+ts_delta(group_mean(close, industry), 20) * if_else(quantile(group_std_dev(close, industry), 0.8) < ts_std_dev(close, industry), 1, -1)
+group_mean(ts_delta(close, 10), industry) * ts_regression(group_rank(close, industry), ts_step(1), 20, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 20), 60) * bucket(group_rank(close, industry), "0.75,1")
+ts_regression(group_mean(close, industry), ts_step(1), 40, 0, 1) * group_mean(ts_delta(volume, 10), industry)
+ts_delta(group_mean(close, industry), 5) * ts_scale(group_rank(volume, industry), 60)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 10)) * ts_rank(returns, 30)
+ts_corr(group_mean(close, industry), group_mean(financial_index, 20), 20) * ts_zscore(volume, 20)
+ts_delta(group_mean(close, industry), 10) * if_else(group_rank(close, industry) > 0.7, 1, -1)
+group_mean(ts_delta(close, 5), industry) * ts_regression(group_rank(close, industry), ts_step(1), 30, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 15), 60) * reverse(bucket(rank(close), "0.7,1"))
+ts_regression(group_mean(close, industry), ts_step(1), 20, 0, 1) * group_mean(bucket(rank(close), "0.6,1"), industry)
+(ts_mean(group_mean(close, industry), 5) - group_mean(close, industry)) * ts_scale(volume, 30)
+divide(group_mean(close, industry), ts_delay(group_mean(close, industry), 10)) * group_rank(ts_mean(volume, 20), industry)
+ts_corr(group_mean(close, industry), group_mean(healthcare_index, 30), 20) * ts_zscore(returns, 30)
+ts_delta(group_mean(close, industry), 20) * if_else(quantile(group_std_dev(close, industry), 0.2) < ts_std_dev(close, industry), 1, -1)
+group_mean(ts_delta(close, 10), industry) * ts_regression(group_rank(close), ts_step(1), 40, 0, 1)
+ts_zscore(ts_delta(group_mean(close, industry), 5), 60) * bucket(group_rank(close, industry), "0.7,0.85,1")
+ts_regression(group_mean(close, industry), ts_step(1), 30, 0, 1) * group_mean(ts_delta(volume, 10), industry)

main.py

@@ -1,62 +1,91 @@
 # -*- coding: utf-8 -*-
 import os
+import random
+import sys
 import json
 import openai
 import httpx
+import csv
 from datetime import datetime
 
-
-def load_config(config_file="ai_config.json"):
-    try:
-        with open(config_file, 'r', encoding='utf-8') as f:
-            return json.load(f)
-    except FileNotFoundError:
-        print(f"创建配置文件: {config_file}")
-        with open(config_file, 'w', encoding='utf-8') as f:
-            json.dump({}, f, indent=4)
-        print("请在config.json中配置siliconflow的api信息")
-        return None
-
-
-def create_prompt_file():
-    if not os.path.exists("alpha_prompt.txt"):
-        with open("alpha_prompt.txt", 'w', encoding='utf-8') as f:
-            f.write("")
-        print("已创建alpha_prompt.txt，请填入提示词后重新运行")
-        return False
-    return True
-
-
-def read_prompt():
-    with open("alpha_prompt.txt", 'r', encoding='utf-8') as f:
+sys.path.append(os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator'))
+PROJECT_PATH = os.path.join(os.path.abspath(__file__).split('AlphaGenerator')[0] + 'AlphaGenerator')
+
+PREPARE_PROMPT = os.path.join(PROJECT_PATH, 'prepare_prompt')
+
+SELECT_DATA_SET_QTY = 30
+
+SILICONFLOW_API_KEY = "sk-pvdiisdowmuwkrpnxsrlhxaovicqibmlljwrwwvbbdjaitdl"
+SILICONFLOW_BASE_URL = "https://api.siliconflow.cn/v1"
+MODELS = [
+    # 'deepseek-ai/DeepSeek-V3.2-Exp',
+    # 'MiniMaxAI/MiniMax-M2',
+    # 'zai-org/GLM-4.6',
+    # 'Qwen/Qwen3-VL-235B-A22B-Instruct',
+    # 'inclusionAI/Ring-flash-2.0',
+    # 'zai-org/GLM-4.6',
+    # 'inclusionAI/Ling-flash-2.0',
+    'inclusionAI/Ring-flash-2.0',
+    # 'zai-org/GLM-4.6V'
+]
+
+
+def txtFileLoader(file_path):
+    if not os.path.exists(file_path):
+        print(f"文件不存在: {file_path}")
+        exit(1)
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return [line.strip() for line in f if line.strip()]
+
+
+def csvFileLoader(file_path):
+    if not os.path.exists(file_path):
+        print(f"文件不存在: {file_path}")
+        exit(1)
+    data = []
+    with open(file_path, 'r', encoding='utf-8') as f:
+        reader = csv.reader(f)
+        for row in reader:
+            data.append(row)
+    return data
+
+
+def read_prompt(alpha_prompt_path):
+    if not os.path.exists(alpha_prompt_path):
+        print("alpha_prompt.txt文件不存在")
+        exit(1)
+    with open(alpha_prompt_path, 'r', encoding='utf-8') as f:
         prompt = f.read().strip()
     if not prompt:
-        print("alpha_prompt.txt是空的，请填入提示词")
-        return None
-    return prompt
+        print("alpha_prompt.txt是空的")
+        exit(1)
+    return prompt.replace('\n\n', '\n')
+
 
-def read_operator():
-    with open("wqb_operator.txt", 'r', encoding='utf-8') as f:
-        operator = f.read().strip()
-    if not operator:
-        print("operator.txt是空的，请填入操作符")
-        return None
-    return operator
+def read_operator(operator_prompt_path):
+    if not os.path.exists(operator_prompt_path):
+        print("wqb_operator.txt文件不存在")
+        exit(1)
+    with open(operator_prompt_path, 'r', encoding='utf-8') as f:
+        operator_lines = [line.strip() for line in f.readlines() if line.strip()]
+    if not operator_lines:
+        print("wqb_operator.txt是空的")
+        exit(1)
+    return "\n".join(operator_lines)
 
 
 def create_result_folder():
-    # 修改这里：创建 generated_alpha 文件夹
     folder_name = "generated_alpha"
     if not os.path.exists(folder_name):
         os.makedirs(folder_name)
     return folder_name
 
 
-def call_siliconflow(api_key, prompt, model, base_url):
+def call_siliconflow(prompt, model):
     try:
         client = openai.OpenAI(
-            api_key=api_key,
-            base_url=base_url
+            api_key=SILICONFLOW_API_KEY,
+            base_url=SILICONFLOW_BASE_URL
         )
 
         response = client.chat.completions.create(
@@ -74,93 +103,124 @@ def call_siliconflow(api_key, prompt, model, base_url):
         print(f"API错误: {e}")
     except Exception as e:
         print(f"其他错误: {e}")
-    return None
+    exit(1)
 
 
 def save_result(result, folder):
-    timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
-    filename = f"{timestamp}.txt"
-    filepath = os.path.join(folder, filename)
+    now = datetime.now()
+    date_folder = now.strftime("%Y-%m-%d")
+    time_filename = now.strftime("%H%M%S")
+    full_folder_path = os.path.join(folder, date_folder)
+
+    if not os.path.exists(full_folder_path):
+        os.makedirs(full_folder_path)
+        print(f"创建文件夹: {full_folder_path}")
+
+    filename = f"{time_filename}.txt"
+    filepath = os.path.join(full_folder_path, filename)
 
     with open(filepath, 'w', encoding='utf-8') as f:
         f.write(result)
+
     print(f"结果保存到: {filepath}")
 
 
-def get_ai_config(config):
-    if not config:
-        return None
+def get_user_info():
+    headers = {"Authorization": f"Bearer {SILICONFLOW_API_KEY}"}
+    url = "https://api.siliconflow.cn/v1/user/info"
+    response = httpx.get(url, headers=headers)
+    data = response.json()['data']
+    balance = data['balance']
+    print(f"余额: {balance}")
+    return float(balance)
 
-    service_name = list(config.keys())[0]
-    service_config = config[service_name]
 
-    required_fields = ["api_keys", "model", "base_url"]
-    for field in required_fields:
-        if field not in service_config:
-            print(f"缺少配置字段: {field}")
-            return None
+def manual_prompt(prompt):
+    manual_prompt_path = os.path.join(PROJECT_PATH, "manual_prompt")
 
-    return service_name, service_config
+    if not os.path.exists(manual_prompt_path):
+        os.makedirs(manual_prompt_path)
+        print(f"创建文件夹: {manual_prompt_path}")
 
-def get_user_info():
-    # 获取用户信息
-    with open('ai_config.json', 'r') as f:
-        config = json.load(f)
-        siliconflow = config['siliconflow']
-        token = siliconflow['api_keys']
+    # 文件名后添加保存时间
+    now = datetime.now()
+    filename = f"manual_prompt_{now.strftime('%Y%m%d%H%M%S')}.txt"
+    filepath = os.path.join(manual_prompt_path, filename)
 
-    headers = {"Authorization": f"Bearer {token}"}
+    with open(filepath, 'w', encoding='utf-8') as f:
+        f.write(prompt)
 
-    url = "https://api.siliconflow.cn/v1/user/info"
+    print(f"手动提示词保存到: {filepath}")
 
-    response = httpx.get(url, headers=headers)
 
-    data = response.json()['data']
-    balance = data['balance']
-    print(f"余额: {balance}")
+def prepare_prompt():
+    prompt = ''
 
+    # 读取基础提示词
+    alpha_prompt_path = os.path.join(PREPARE_PROMPT, "alpha_prompt.txt")
+    prompt += read_prompt(alpha_prompt_path)
 
-def main():
-    get_user_info()
+    # 读取操作符
+    prompt += "\n\n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\n\n"
+    prompt += "========================= 操作符开始 ======================================="
+    prompt += "注意: Operator: 后面的是操作符,\nDescription: 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\n"
+    prompt += "特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alpha"
+    operator_prompt_path = os.path.join(PREPARE_PROMPT, "operator.txt")
+    operator = read_operator(operator_prompt_path)
+    prompt += operator
+    prompt += "========================= 操作符结束 =======================================\n\n"
 
-    config = load_config()
-    if not config:
-        return
+    # 读取数据字段, 数据字段数量庞大, 通过 dataset_id 分组读取, 然后每组里面随机选择 {SELECT_DATA_SET_QTY} 个
+    data_sets_path = os.path.join(PREPARE_PROMPT, "all_data_combined.csv")
+    data_sets = csvFileLoader(data_sets_path)
 
-    if not create_prompt_file():
-        return
+    data_groups = {}
+    for index, data_set in enumerate(data_sets):
+        if index == 0:
+            continue
 
-    prompt = read_prompt()
-    if not prompt:
-        return
+        if data_set[2] not in data_groups:
+            data_groups[data_set[2]] = []
+        data_groups[data_set[2]].append({data_set[0]: data_set[1]})
 
-    config_result = get_ai_config(config)
-    if not config_result:
-        return
+    selected_data_sets = []
+    for key, value in data_groups.items():
+        if len(value) < SELECT_DATA_SET_QTY:
+            selected_data_sets.extend(value)
+        else:
+            selected_data_sets.extend(random.sample(value, SELECT_DATA_SET_QTY))
+
+    prompt += "========================= 数据字段开始 ======================================="
+    prompt += "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\n\n"
+    for data_set in selected_data_sets:
+        for key, value in data_set.items():
+            prompt += f"DataField: {key}\n"
+            prompt += f"DataFieldDescription: {value}\n"
 
-    operator = read_operator()
+    prompt += "========================= 数据字段结束 =======================================\n\n"
 
-    if operator:
-        prompt = prompt + "\n\n以下是我的账号有权限使用的操作符以及操作符的使用方法, 请严格按照操作符, 进行生成,组合因子\n\n" + operator
+    return prompt
 
-    service_name, service_config = config_result
 
-    print(f"使用服务: {service_name}")
-    print(f"使用模型: {service_config['model']}")
+def main():
+    prompt = prepare_prompt()
+
+    # # 如果需要手动在页面段模型, 使用提示词, 打开这个, 将生成的提示词存到本地
+    manual_prompt(prompt)
+
+    balance = get_user_info()
 
     folder = create_result_folder()
 
-    print("正在调用AI...")
-    result = call_siliconflow(
-        service_config["api_keys"],
-        prompt,
-        service_config["model"],
-        service_config["base_url"]
-    )
+    for model in MODELS:
+        print(f"正在调用AI...{model}")
+        result = call_siliconflow(prompt, model)
 
         if result:
             print(f"AI回复: {result[:200]}...")
             save_result(result, folder)
+            used_balance = balance - get_user_info()
+            print(f'本次调用 api 使用额度 {used_balance}')
         else:
             print("AI调用失败")
 

manual_prompt/manual_prompt_20251211172257.txt

@@ -0,0 +1,922 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+核心概念：捕捉行业价格的趋势、动量和形态变化
+关键函数：
+ts_delta, ts_mean, ts_regression（获取斜率rettype参数）
+ts_decay_linear, ts_zscore, ts_rank
+ts_scale, ts_av_diff, ts_std_dev
+ts_corr, ts_covariance（用于行业内序列）
+设计思路：
+动量的变化率、加速度或平滑度构建
+动量衰减或增强模式识别
+价格与成交量关系的时序分析
+维度2：横截面领导力（CL）
+核心概念：识别行业内部的分化、龙头效应和相对强度
+关键函数：
+group_mean, group_std, group_rank
+group_zscore, group_neutralize, group_scale
+rank, zscore, quantile（横截面）
+bucket（用于龙头股筛选）
+设计思路：
+行业内部龙头股与平均表现的差异
+行业成分股的离散度分析
+相对排名的变化和稳定性
+维度3：市场状态适应性（MS）
+核心概念：根据市场环境动态调整因子逻辑
+关键函数：
+ts_rank, if_else, 条件判断运算符
+ts_std_dev（用于波动率调整）
+ts_regression（不同状态使用不同参数）
+trade_when（条件触发）
+设计思路：
+波动率调整的动量指标
+不同市场状态（高/低波动）使用不同的回顾期
+条件逻辑下的参数动态调整
+维度4：行业间联动（IS）
+核心概念：捕捉行业间的动量溢出和相关性变化
+关键函数：
+ts_corr, ts_covariance（跨行业）
+group_mean（用于行业指数）
+向量操作：vec_avg, vec_sum
+多序列相关性分析
+设计思路：
+领先-滞后行业的相关性分析
+行业间动量传导效应
+板块轮动的早期信号识别
+维度5：交易行为情绪（TS）
+核心概念：基于交易行为和情绪指标的反转信号
+关键函数：
+ts_corr(volume, close, d)（量价关系）
+ts_rank（历史相对位置）
+ts_zscore（极端值识别）
+days_from_last_change（事件驱动）
+设计思路：
+超买超卖状态识别
+交易拥挤度指标
+情绪极端值后的均值回归
+复合因子设计原则
+强制要求：
+每个表达式必须融合至少两个设计维度
+必须使用提供的操作符列表中的函数
+因子应具有经济逻辑解释性
+推荐组合模式：
+TM + CL：时序动量 + 横截面领导力
+示例：行业动量加速度 × 龙头股相对强度
+TM + MS：时序动量 + 状态适应性
+示例：波动率调整后的动量指标
+CL + IS：横截面 + 行业间联动
+示例：龙头股表现与相关行业的领先滞后关系
+MS + TS：状态适应 + 交易情绪
+示例：不同市场状态下的反转信号
+IS + TS：行业联动 + 交易情绪
+示例：行业间相关性变化与交易拥挤度
+参数化建议：
+使用不同的时间窗口组合（短/中/长周期）
+尝试不同的权重分配方式
+考虑非线性变换（log, power, sqrt）
+使用条件逻辑增强鲁棒性
+表达式构建指南
+基本结构：
+text
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+运算符使用策略：
+算术运算：add, subtract, multiply, divide
+非线性变换：log, power, sqrt, signed_power
+条件逻辑：if_else, and, or, 比较运算符
+标准化处理：normalize, winsorize, scale
+防止过拟合建议：
+避免过度复杂的嵌套
+使用经济直觉验证逻辑合理性
+考虑实际交易可行性
+包含风险控制元素（如波动率调整）
+*=====*
+操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
+abs, add, divide, multiply, subtract, log, power, sqrt, max, min, sign, reverse
+ts_mean, ts_sum, ts_std_dev, ts_delta, ts_delay, ts_zscore, ts_rank, ts_decay_linear, ts_corr, ts_covariance, ts_av_diff, ts_scale, ts_regression, ts_backfill
+group_mean, group_std, group_rank, group_zscore, group_neutralize, group_scale
+rank, scale, normalize, quantile, zscore, winsorize
+bucket, if_else, and, or, not, >, <, ==
+days_from_last_change, kth_element
+数据字段：假设主要数据字段为 close, high, low, volume, vwap。可安全使用。
+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。
+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。
+输出格式：
+输出必须是且仅是 100行纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+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。
+示例思维（仅供理解，不输出）
+一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
+multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
+这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
+现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+请提供具体的WQ表达式。
+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。
+
+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
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+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
+
+Operator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false), x + y
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+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
+Operator: divide(x, y), x / y
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false), x * y
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false), x - y
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+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).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+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
+Operator: ts_arg_min(x, d)
+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.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+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.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+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.
+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.========================= 操作符结束 =======================================
+
+========================= 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
+
+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_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: 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: pcr_vol_20
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
+DataField: pcr_oi_10
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
+DataField: pcr_vol_30
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 days in the future.
+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: 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_10
+DataFieldDescription: Price at which a stock's options with expiration 10 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: 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_720
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 720 days in the future.
+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: 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.
+DataField: pcr_oi_90
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
+DataField: put_breakeven_1080
+DataFieldDescription: Price at which a stock's put options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_360
+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.
+DataField: forward_price_120
+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.
+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.
+DataField: call_breakeven_120
+DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_360
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 360 days in the future.
+DataField: forward_price_30
+DataFieldDescription: Forward price at 30 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_150
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.
+DataField: put_breakeven_30
+DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_90
+DataFieldDescription: Price at which a stock's call options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_720
+DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: forward_price_180
+DataFieldDescription: Forward price at 180 days derived from a synthetic long option with payoff similar to long stock + option dynamics. combination of long ATM call, and short ATM put.
+DataField: call_breakeven_1080
+DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_10
+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.
+DataField: fnd6_lcoxdr
+DataFieldDescription: Current Liabilities - Other - Excluding Deferred Revenue
+DataField: fnd6_newqeventv110_pnciepsq
+DataFieldDescription: Core Pension Interest Adjustment Basic EPS Effect
+DataField: fnd6_tfva
+DataFieldDescription: Total Fair Value Assets
+DataField: fnd6_newqv1300_rcpq
+DataFieldDescription: Restructuring Cost Pretax
+DataField: fnd6_aldo
+DataFieldDescription: Long-term Assets of Discontinued Operations
+DataField: fnd6_newqeventv110_altoq
+DataFieldDescription: Other Long-term Assets
+DataField: fnd6_idesindq_curcd
+DataFieldDescription: ISO Currency Code - Company Annual Market
+DataField: fnd6_newqeventv110_csh12q
+DataFieldDescription: Common Shares Used to Calculate Earnings Per Share - 12 Months Moving
+DataField: fnd6_newa2v1300_oancf
+DataFieldDescription: Operating Activities - Net Cash Flow
+DataField: bookvalue_ps
+DataFieldDescription: Book Value Per Share
+DataField: fnd6_pidom
+DataFieldDescription: Pretax Income - Domestic
+DataField: cashflow
+DataFieldDescription: Cashflow (Annual)
+DataField: fnd6_cld2
+DataFieldDescription: Capitalized Leases - Due in 2nd Year
+DataField: fnd6_newqeventv110_glcea12
+DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) After-tax 12MM
+DataField: fnd6_newqeventv110_xopt12
+DataFieldDescription: Implied Option Expense - 12mm
+DataField: fnd6_newqv1300_txdiq
+DataFieldDescription: Income Taxes - Deferred
+DataField: fnd6_dlto
+DataFieldDescription: Debt - Long-Term - Other
+DataField: fnd6_txdfo
+DataFieldDescription: Deferred Taxes - Foreign
+DataField: operating_expense
+DataFieldDescription: Operating Expense - Total
+DataField: fnd6_cptnewqv1300_dlttq
+DataFieldDescription: Long-Term Debt - Total
+DataField: fnd6_eventv110_pncepsq
+DataFieldDescription: Core Pension Adjustment Basic EPS Effect
+DataField: fnd6_newa1v1300_csho
+DataFieldDescription: Common Shares Outstanding
+DataField: fnd6_newa2v1300_spceeps
+DataFieldDescription: S&P Core Earnings EPS Basic
+DataField: fnd6_newqv1300_dpactq
+DataFieldDescription: Depreciation, Depletion and Amortization (Accumulated)
+DataField: fnd6_dd1
+DataFieldDescription: Long-Term Debt Due in 1 Year
+DataField: fnd6_dd5
+DataFieldDescription: Debt Due in 5th Year
+DataField: fnd6_cptnewqeventv110_epsfxq
+DataFieldDescription: Earnings Per Share (Diluted) - Excluding Extraordinary items
+DataField: fnd6_newqeventv110_glceepsq
+DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Basic EPS Effect
+DataField: fnd6_newqeventv110_lnoq
+DataFieldDescription: Liabilities Netting & Other Adjustments
+DataField: fnd6_newqeventv110_cshfdq
+DataFieldDescription: Common Shares for Diluted EPS
+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: max_shareholders_equity_guidance
+DataFieldDescription: The maximum guidance value for Total Shareholders' Equity.
+DataField: sales_estimate_average_annual
+DataFieldDescription: Sales - mean of estimations
+DataField: total_assets_amount
+DataFieldDescription: Total Assets - actual value
+DataField: anl4_qfv4_cfps_number
+DataFieldDescription: Cash Flow Per Share - number of estimations
+DataField: anl4_eaz2lafv110_person
+DataFieldDescription: Broker Id
+DataField: anl4_basicconltv110_pu
+DataFieldDescription: The number of upper estimations
+DataField: min_free_cash_flow_guidance
+DataFieldDescription: The minimum guidance value for Free Cash Flow on an annual basis.
+DataField: min_free_cashflow_guidance
+DataFieldDescription: Minimum guidance value for Free Cash Flow
+DataField: anl4_fcf_number
+DataFieldDescription: Free Cash Flow - number of estimations
+DataField: min_ebitda_guidance
+DataFieldDescription: Minimum guidance value for Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) - Annual
+DataField: anl4_ebit_number
+DataFieldDescription: Earnings before interest and taxes - number of estimations
+DataField: min_net_profit_guidance
+DataFieldDescription: Minimum guidance value for Net Profit on an annual basis
+DataField: anl4_ady_high
+DataFieldDescription: The highest estimation
+DataField: est_ffo
+DataFieldDescription: Funds From Operation - Summary on Estimations, Mean
+DataField: anl4_detailrecv4_est
+DataFieldDescription: Estimation value for recommendation detail
+DataField: earnings_per_share_guidance_value
+DataFieldDescription: Earnings Per Share - guidance value for annual frequency
+DataField: anl4_eaz2lafv110_bk
+DataFieldDescription: Broker name (int)
+DataField: anl4_totgw_low
+DataFieldDescription: Total Goodwill - The lowest estimation
+DataField: max_free_cash_flow_guidance
+DataFieldDescription: The maximum guidance value for Free Cash Flow on an annual basis.
+DataField: anl4_bac1actualqfv110_item
+DataFieldDescription: Financial item
+DataField: anl4_qfd1_az_cfps_median
+DataFieldDescription: Cash Flow Per Share - Median value among forecasts
+DataField: guidance_estimate_value
+DataFieldDescription: Estimated value for basic annual financial guidance
+DataField: anl4_fcf_low
+DataFieldDescription: Free Cash Flow - The lowest estimation
+DataField: anl4_gric_flag
+DataFieldDescription: Gross income - forecast type (revision/new/...)
+DataField: dividend_estimate_minimum
+DataFieldDescription: Dividend per share - The lowest value among forecasts - D1
+DataField: min_total_assets_guidance
+DataFieldDescription: Minimum guidance value for Total Assets
+DataField: anl4_ady_low
+DataFieldDescription: The lowest estimation
+DataField: shareholders_equity_max_guidance
+DataFieldDescription: The maximum guidance value for Shareholder's Equity on an annual basis.
+DataField: anl4_flag_erbfintax
+DataFieldDescription: Earnings before interest and taxes - forecast type (revision/new/...)
+DataField: cashflow_per_share_min_guidance_quarterly
+DataFieldDescription: Minimum guidance value for Cash Flow Per Share
+DataField: rel_num_part
+DataFieldDescription: number of the instrument's partners
+DataField: pv13_hierarchy_min52_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f4_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_sector_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f1_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min10_all_sector
+DataFieldDescription: grouping fields
+DataField: rel_ret_cust
+DataFieldDescription: averaged one-day-return of the instrument's customers
+DataField: rel_ret_all
+DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
+DataField: pv13_revere_company_total
+DataFieldDescription: Total number of companies in the sector
+DataField: pv13_3l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_sector_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min10_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_reportperiodend
+DataFieldDescription: Stated end date for the report
+DataField: pv13_rha2_min10_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min50_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_2k_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min20_sector
+DataFieldDescription: grouping fields
+DataField: pv13_r2_liquid_min5_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_index_cap
+DataFieldDescription: Company market capitalization
+DataField: pv13_6l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_focused_pureplay_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min10_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_di_6l
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min30_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min52_2k_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min5_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: implied_volatility_put_10
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
+DataField: parkinson_volatility_30
+DataFieldDescription: Parkinson model's historical volatility over 30 days
+DataField: implied_volatility_put_180
+DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
+DataField: implied_volatility_put_270
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
+DataField: parkinson_volatility_90
+DataFieldDescription: Parkinson model's historical volatility over 90 days
+DataField: implied_volatility_call_60
+DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
+DataField: parkinson_volatility_150
+DataFieldDescription: Parkinson model's historical volatility over 150 days
+DataField: implied_volatility_mean_skew_720
+DataFieldDescription: At-the-money option-implied volatility mean skew for 720 days
+DataField: implied_volatility_put_60
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
+DataField: parkinson_volatility_120
+DataFieldDescription: Parkinson model's historical volatility over 120 days
+DataField: parkinson_volatility_180
+DataFieldDescription: Parkinson model's historical volatility over 180 days
+DataField: implied_volatility_call_270
+DataFieldDescription: At-the-money option-implied volatility for call Option for 270 days
+DataField: implied_volatility_call_20
+DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
+DataField: implied_volatility_mean_720
+DataFieldDescription: At-the-money option-implied volatility mean for 720 days
+DataField: implied_volatility_mean_30
+DataFieldDescription: At-the-money option-implied volatility mean for 30 days
+DataField: implied_volatility_mean_skew_30
+DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
+DataField: implied_volatility_mean_360
+DataFieldDescription: At-the-money option-implied volatility mean for 360 days
+DataField: implied_volatility_call_720
+DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
+DataField: parkinson_volatility_60
+DataFieldDescription: Parkinson model's historical volatility over 60 days
+DataField: implied_volatility_put_30
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
+DataField: implied_volatility_mean_10
+DataFieldDescription: At-the-money option-implied volatility mean for 10 days
+DataField: implied_volatility_mean_20
+DataFieldDescription: At-the-money option-implied volatility mean for 20 days
+DataField: implied_volatility_put_120
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
+DataField: historical_volatility_90
+DataFieldDescription: Close-to-close Historical volatility over 90 days
+DataField: implied_volatility_mean_1080
+DataFieldDescription: At-the-money option-implied volatility mean for 3 years
+DataField: implied_volatility_mean_skew_90
+DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
+DataField: parkinson_volatility_20
+DataFieldDescription: Parkinson model's historical volatility over 20 days
+DataField: historical_volatility_60
+DataFieldDescription: Close-to-close Historical volatility over 60 days
+DataField: implied_volatility_mean_150
+DataFieldDescription: At-the-money option-implied volatility mean for 150 days
+DataField: implied_volatility_mean_120
+DataFieldDescription: At-the-money option-implied volatility mean for 120 days
+DataField: nws12_afterhsz_prevwap
+DataFieldDescription: Pre session volume weighted average price
+DataField: nws12_prez_sl
+DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS = 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
+DataField: nws12_afterhsz_2s
+DataFieldDescription: Number of minutes that elapsed before price went down 2 percentage points
+DataField: nws12_mainz_result_vs_index
+DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
+DataField: nws12_mainz_sl
+DataFieldDescription: Whether a long or short position would have been more advantageous: If (EODHigh - Last) > (Last - EODLow) Then LS = 1; If (EODHigh - Last) = (Last - EODLow) Then LS = 0; If (EODHigh - Last) < (Last - EODLow) Then LS = -1.
+DataField: nws12_mainz_02p
+DataFieldDescription: The minimum of L or S above for 20-minute bucket
+DataField: news_mins_4_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 4 percentage points
+DataField: nws12_afterhsz_57s
+DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
+DataField: nws12_allz_newssess
+DataFieldDescription: Index of session in which the news was reported
+DataField: news_pct_30min
+DataFieldDescription: The percent change in price in the first 30 minutes following the news release
+DataField: news_mins_2_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
+DataField: nws12_afterhsz_01p
+DataFieldDescription: The minimum of L or S above for 10 minute bucket
+DataField: news_eod_vwap
+DataFieldDescription: Volume weighted average price between the time of news and the end of the session
+DataField: nws12_afterhsz_1p
+DataFieldDescription: The minimum of L or S above for 1-minute bucket
+DataField: nws12_prez_vol_ratio
+DataFieldDescription: Curr_Vol / Mov_Vol
+DataField: nws12_prez_rangestddev
+DataFieldDescription: (RangeAmt-AvgRange)/RangeStdDev, where AvgRange is the average of the daily range, and RangeStdDev is one standard deviation for the daily range, both for 30 calendar days
+DataField: nws12_mainz_epsactual
+DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
+DataField: nws12_prez_30_seconds
+DataFieldDescription: The percent change in price in the 30 seconds following the news release
+DataField: nws12_mainz_tonlast
+DataFieldDescription: Price at the time of news
+DataField: nws12_prez_2p
+DataFieldDescription: The minimum of L or S above for 2-minute bucket
+DataField: nws12_afterhsz_div_y
+DataFieldDescription: Annual yield
+DataField: nws12_mainz_01s
+DataFieldDescription: Number of minutes that elapsed before price went down 10 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: nws12_prez_highexcstddev
+DataFieldDescription: (EODHigh - TONLast)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
+DataField: nws12_afterhsz_57p
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: news_ton_high
+DataFieldDescription: Highest price reached during the session before the time of news
+DataField: nws12_mainz_provider
+DataFieldDescription: index of name of the news provider
+DataField: nws12_afterhsz_prevday
+DataFieldDescription: Percent change between the previous day's open and close
+DataField: nws12_mainz_prevwap
+DataFieldDescription: Pre session volume weighted average price
+DataField: nws12_afterhsz_120_min
+DataFieldDescription: The percent change in price in the first 120 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_price
+DataFieldDescription: News impact projection of stock price news
+DataField: rp_ess_product
+DataFieldDescription: Event sentiment score of product and service-related news
+DataField: rp_ess_revenue
+DataFieldDescription: Event sentiment score of revenue news
+DataField: rp_css_revenue
+DataFieldDescription: Composite sentiment score of revenue news
+DataField: nws18_qep
+DataFieldDescription: News sentiment based on positive and negative words on global equity
+DataField: rp_css_business
+DataFieldDescription: Composite sentiment score of business-related news
+DataField: rp_nip_partner
+DataFieldDescription: News impact projection of partnership news
+DataField: rp_nip_credit
+DataFieldDescription: News impact projection of credit news
+DataField: rp_css_ptg
+DataFieldDescription: Composite sentiment score of price target news
+DataField: nws18_ssc
+DataFieldDescription: Sentiment of the news calculated using multiple techniques
+DataField: rp_ess_technical
+DataFieldDescription: Event sentiment score based on technical analysis
+DataField: rp_nip_labor
+DataFieldDescription: News impact projection of labor issues news
+DataField: rp_ess_dividends
+DataFieldDescription: Event sentiment score of dividends news
+DataField: rp_nip_insider
+DataFieldDescription: News impact projection of insider trading news
+DataField: rp_nip_society
+DataFieldDescription: News impact projection of society-related news
+DataField: rp_nip_equity
+DataFieldDescription: News impact projection of equity action news
+DataField: rp_css_labor
+DataFieldDescription: Composite sentiment score of labor issues news
+DataField: nws18_bee
+DataFieldDescription: News sentiment specializing in growth of earnings
+DataField: rp_css_ratings
+DataFieldDescription: Composite sentiment score of analyst ratings-related news
+DataField: rp_nip_business
+DataFieldDescription: News impact projection of business-related news
+DataField: rp_css_inverstor
+DataFieldDescription: Composite sentiment score of investor relations news
+DataField: rp_nip_assets
+DataFieldDescription: News impact projection of assets news
+DataField: nws18_sse
+DataFieldDescription: Sentiment of phrases impacting the company
+DataField: rp_css_assets
+DataFieldDescription: Composite sentiment score of assets news
+DataField: rp_css_equity
+DataFieldDescription: Composite sentiment score of equity action news
+DataField: rp_nip_revenue
+DataFieldDescription: News impact projection of revenue news
+DataField: rp_ess_insider
+DataFieldDescription: Event sentiment score of insider trading news
+DataField: rp_nip_technical
+DataFieldDescription: News impact projection based on technical analysis
+DataField: rp_ess_business
+DataFieldDescription: Event sentiment score of business-related news
+DataField: rp_nip_legal
+DataFieldDescription: News impact projection of legal news
+DataField: fnd2_a_ltrmdmrepoplinyfour
+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 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_propplteqmuflmeqmt
+DataFieldDescription: PPE, Equipment, Useful Life, Minimum
+DataField: fnd2_q_atdlsecexfcepsastkos
+DataFieldDescription: Antidilutive Shares Excluded From Earnings Per Share Amount, Stock Options
+DataField: fn_allocated_share_based_compensation_expense_a
+DataFieldDescription: Represents the expense recognized during the period arising from equity-based compensation arrangements (for example, shares of stock, unit, stock options or other equity instruments) with employees, directors and certain consultants qualifying for treatment as employees.
+DataField: fn_derivative_fair_value_of_derivative_liability_a
+DataFieldDescription: Fair value, before effects of master netting arrangements, of a financial liability or contract with one or more underlyings, notional amount or payment provision or both, and the contract can be net settled by means outside the contract or delivery of an asset. Includes liabilities elected not to be offset. Excludes liabilities not subject to a master netting arrangement.
+DataField: fn_allowance_for_doubtful_accounts_receivable_q
+DataFieldDescription: For an unclassified balance sheet, a valuation allowance for receivables due a company that are expected to be uncollectible.
+DataField: fnd2_a_gsles1xtinguishmentofd
+DataFieldDescription: Difference between the fair value of payments made and the carrying amount of debt which is extinguished prior to maturity.
+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_a_sbcpnargmsawpfipwerpr
+DataFieldDescription: Weighted average price of options that were either forfeited or expired.
+DataField: fn_op_lease_min_pay_due_in_4y_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 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_interest_payable_a
+DataFieldDescription: Carrying value as of the balance sheet date of [accrued] interest payable on all forms of debt, including trade payables, that has been incurred and is unpaid. 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: fnd2_oprlsfmpdcurr
+DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the next fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_currfrtxexp
+DataFieldDescription: Income Tax Expense, Current - Foreign
+DataField: fnd2_a_opclpsnprtmbnfplansajnt
+DataFieldDescription: Amount after tax and reclassification adjustments, of (increase) decrease in accumulated other comprehensive (income) loss related to pension and other postretirement defined benefit plans.
+DataField: fnd2_dfdfeditxexp
+DataFieldDescription: Income Tax Expense, Deferred - Federal
+DataField: fnd2_dbplanepdfbnfp5ytherea
+DataFieldDescription: Amount of benefits from a defined benefit plan expected to be paid in the 5 fiscal years after 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_sbcpnargmsptawervl
+DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
+DataField: fn_proceeds_from_lt_debt_q
+DataFieldDescription: Proceeds From Issuance Of Debt, Long Term
+DataField: fnd2_a_eplsbvdcpcstnrgsbaoo
+DataFieldDescription: Unrecognized cost of unvested other share-based compensation awards.
+DataField: fnd2_propplteqmuflmamfrt
+DataFieldDescription: PPE, Furniture, Useful Life, Maximum
+DataField: fn_comp_options_grants_fair_value_a
+DataFieldDescription: Annual Share-Based Compensation Arrangement by Share-Based Payment Award Options Grants in Period Weighted Average Grant Date Fair Value
+DataField: fn_comp_options_out_number_q
+DataFieldDescription: Number of options outstanding, including both vested and non-vested options.
+DataField: fn_oth_income_loss_net_of_tax_a
+DataFieldDescription: Amount after tax and reclassification adjustments of other comprehensive income (loss).
+DataField: fnd2_a_bnsacqproformarvn
+DataFieldDescription: The pro forma revenue for a period as if the business combination or combinations had been completed at the beginning of the period.
+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_def_income_tax_expense_a
+DataFieldDescription: Income Tax Expense, Deferred
+DataField: fn_oth_comp_grants_weighted_avg_grant_date_fair_value_q
+DataFieldDescription: Quarterly Share-Based Compensation Equity Instruments Other Than Options Nonvested Weighted Average Grant Date Fair Value
+DataField: fn_proceeds_from_issuance_of_debt_q
+DataFieldDescription: The cash inflow during the period from additional borrowings in aggregate debt. Includes proceeds from short-term and long-term debt.
+DataField: fnd2_a_sbcpnargmpmtwstgm
+DataFieldDescription: As of the balance sheet date, the number of shares into which fully vested and expected to vest stock options outstanding can be converted under the option plan.
+DataField: fn_goodwill_acquired_during_period_q
+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: 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 =======================================
+

manual_prompt/manual_prompt_20251211173031.txt

@@ -0,0 +1,922 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+核心概念：捕捉行业价格的趋势、动量和形态变化
+关键函数：
+ts_delta, ts_mean, ts_regression（获取斜率rettype参数）
+ts_decay_linear, ts_zscore, ts_rank
+ts_scale, ts_av_diff, ts_std_dev
+ts_corr, ts_covariance（用于行业内序列）
+设计思路：
+动量的变化率、加速度或平滑度构建
+动量衰减或增强模式识别
+价格与成交量关系的时序分析
+维度2：横截面领导力（CL）
+核心概念：识别行业内部的分化、龙头效应和相对强度
+关键函数：
+group_mean, group_std, group_rank
+group_zscore, group_neutralize, group_scale
+rank, zscore, quantile（横截面）
+bucket（用于龙头股筛选）
+设计思路：
+行业内部龙头股与平均表现的差异
+行业成分股的离散度分析
+相对排名的变化和稳定性
+维度3：市场状态适应性（MS）
+核心概念：根据市场环境动态调整因子逻辑
+关键函数：
+ts_rank, if_else, 条件判断运算符
+ts_std_dev（用于波动率调整）
+ts_regression（不同状态使用不同参数）
+trade_when（条件触发）
+设计思路：
+波动率调整的动量指标
+不同市场状态（高/低波动）使用不同的回顾期
+条件逻辑下的参数动态调整
+维度4：行业间联动（IS）
+核心概念：捕捉行业间的动量溢出和相关性变化
+关键函数：
+ts_corr, ts_covariance（跨行业）
+group_mean（用于行业指数）
+向量操作：vec_avg, vec_sum
+多序列相关性分析
+设计思路：
+领先-滞后行业的相关性分析
+行业间动量传导效应
+板块轮动的早期信号识别
+维度5：交易行为情绪（TS）
+核心概念：基于交易行为和情绪指标的反转信号
+关键函数：
+ts_corr(volume, close, d)（量价关系）
+ts_rank（历史相对位置）
+ts_zscore（极端值识别）
+days_from_last_change（事件驱动）
+设计思路：
+超买超卖状态识别
+交易拥挤度指标
+情绪极端值后的均值回归
+复合因子设计原则
+强制要求：
+每个表达式必须融合至少两个设计维度
+必须使用提供的操作符列表中的函数
+因子应具有经济逻辑解释性
+推荐组合模式：
+TM + CL：时序动量 + 横截面领导力
+示例：行业动量加速度 × 龙头股相对强度
+TM + MS：时序动量 + 状态适应性
+示例：波动率调整后的动量指标
+CL + IS：横截面 + 行业间联动
+示例：龙头股表现与相关行业的领先滞后关系
+MS + TS：状态适应 + 交易情绪
+示例：不同市场状态下的反转信号
+IS + TS：行业联动 + 交易情绪
+示例：行业间相关性变化与交易拥挤度
+参数化建议：
+使用不同的时间窗口组合（短/中/长周期）
+尝试不同的权重分配方式
+考虑非线性变换（log, power, sqrt）
+使用条件逻辑增强鲁棒性
+表达式构建指南
+基本结构：
+text
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+运算符使用策略：
+算术运算：add, subtract, multiply, divide
+非线性变换：log, power, sqrt, signed_power
+条件逻辑：if_else, and, or, 比较运算符
+标准化处理：normalize, winsorize, scale
+防止过拟合建议：
+避免过度复杂的嵌套
+使用经济直觉验证逻辑合理性
+考虑实际交易可行性
+包含风险控制元素（如波动率调整）
+*=====*
+操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
+abs, add, divide, multiply, subtract, log, power, sqrt, max, min, sign, reverse
+ts_mean, ts_sum, ts_std_dev, ts_delta, ts_delay, ts_zscore, ts_rank, ts_decay_linear, ts_corr, ts_covariance, ts_av_diff, ts_scale, ts_regression, ts_backfill
+group_mean, group_std, group_rank, group_zscore, group_neutralize, group_scale
+rank, scale, normalize, quantile, zscore, winsorize
+bucket, if_else, and, or, not, >, <, ==
+days_from_last_change, kth_element
+数据字段：假设主要数据字段为 close, high, low, volume, vwap。可安全使用。
+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。
+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。
+输出格式：
+输出必须是且仅是 100行纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+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。
+示例思维（仅供理解，不输出）
+一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
+multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
+这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
+现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+请提供具体的WQ表达式。
+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。
+
+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
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+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
+
+Operator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false), x + y
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+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
+Operator: divide(x, y), x / y
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false), x * y
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false), x - y
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+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).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+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
+Operator: ts_arg_min(x, d)
+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.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+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.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+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.
+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.========================= 操作符结束 =======================================
+
+========================= 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
+
+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: option_breakeven_30
+DataFieldDescription: Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.
+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_120
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 120 days in the future.
+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: call_breakeven_120
+DataFieldDescription: Price at which a stock's call options with expiration 120 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_1080
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
+DataField: forward_price_360
+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.
+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: pcr_vol_10
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 days in the future.
+DataField: option_breakeven_10
+DataFieldDescription: Price at which a stock's options with expiration 10 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_90
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 90 days in the future.
+DataField: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: call_breakeven_90
+DataFieldDescription: Price at which a stock's call options with expiration 90 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_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_60
+DataFieldDescription: Price at which a stock's options with expiration 60 days in the future break even based on its recent bid/ask mean.
+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: put_breakeven_30
+DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_vol_20
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 20 days in the future.
+DataField: call_breakeven_360
+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.
+DataField: option_breakeven_720
+DataFieldDescription: Price at which a stock's options with expiration 720 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_30
+DataFieldDescription: Forward price at 30 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_30
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 30 days in the future.
+DataField: forward_price_90
+DataFieldDescription: Forward price at 90 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_1080
+DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_1080
+DataFieldDescription: Price at which a stock's put options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+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: option_breakeven_150
+DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
+DataField: 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: fnd6_newqeventv110_pncwiepq
+DataFieldDescription: Core Pension Without Interest Adjustment Basic EPS Effect Preliminary
+DataField: fnd6_ivst
+DataFieldDescription: Short-Term Investments - Total
+DataField: fnd6_newqv1300_invrmq
+DataFieldDescription: Inventory - Raw Materials
+DataField: fnd6_newqv1300_tfvceq
+DataFieldDescription: Total Fair Value Changes including Earnings
+DataField: fnd6_newa2v1300_mii
+DataFieldDescription: Noncontrolling Interest (Income Account)
+DataField: fnd6_newqeventv110_glcedq
+DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS
+DataField: fnd6_npq
+DataFieldDescription: Notes Payable
+DataField: fnd6_newqeventv110_reunaq
+DataFieldDescription: Unadjusted Retained Earnings
+DataField: fnd6_newqeventv110_prcpepsq
+DataFieldDescription: Core Post-Retirement Adjustment Basic EPS Effect Preliminary
+DataField: fnd6_newqeventv110_xidoq
+DataFieldDescription: Extraordinary Items and Discontinued Operations
+DataField: fnd6_newa2v1300_oancf
+DataFieldDescription: Operating Activities - Net Cash Flow
+DataField: fnd6_newqeventv110_esoptq
+DataFieldDescription: Preferred ESOP Obligation - Total
+DataField: fnd6_mfma2_recch
+DataFieldDescription: Accounts Receivable - Decrease (Increase)
+DataField: fnd6_cptrank_gvkeymap
+DataFieldDescription: technical code for a company, no need to use it for research
+DataField: fnd6_newqv1300_reunaq
+DataFieldDescription: Unadjusted Retained Earnings
+DataField: fnd6_newqeventv110_piq
+DataFieldDescription: Pretax Income
+DataField: fnd6_cptnewqeventv110_oibdpq
+DataFieldDescription: Operating Income Before Depreciation - Quarterly
+DataField: fnd6_newqeventv110_miiq
+DataFieldDescription: Noncontrolling Interest - Income Account
+DataField: fnd6_newqv1300_aoq
+DataFieldDescription: Assets - Other - Total
+DataField: fnd6_ranks
+DataFieldDescription: Ranking
+DataField: fnd6_newqeventv110_xoptdqp
+DataFieldDescription: Implied Option EPS Diluted Preliminary
+DataField: fnd6_ceql
+DataFieldDescription: Common Equity - Liquidation Value
+DataField: fnd6_newqeventv110_aqpl1q
+DataFieldDescription: Assets Level 1 (Quoted Prices)
+DataField: fnd6_newqeventv110_seteps12
+DataFieldDescription: Settlement (Litigation/Insurance) Basic EPS Effect 12MM
+DataField: fnd6_eventv110_nrtxtepsq
+DataFieldDescription: Nonrecurring Income Taxes Basic EPS Effect
+DataField: fnd6_esubc
+DataFieldDescription: Equity in Net Loss - Earnings
+DataField: fnd6_msa
+DataFieldDescription: Marketable Securities Adjustment
+DataField: fnd6_newqv1300_ciderglq
+DataFieldDescription: Comp Inc - Derivative Gains/Losses
+DataField: ppent
+DataFieldDescription: Property Plant and Equipment - Total (Net)
+DataField: fnd6_dltp
+DataFieldDescription: Long-Term Debt - Tied to Prime
+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_tot_gw_ft
+DataFieldDescription: Total Goodwill - forecast type (revision/new/...)
+DataField: anl4_bvps_value
+DataFieldDescription: Book value per share - announced financial value
+DataField: max_reported_pretax_income_guidance_2
+DataFieldDescription: Reported Pretax income- maximum guidance value
+DataField: anl4_dei3lqfv110_item
+DataFieldDescription: Financial item
+DataField: anl4_netprofita_low
+DataFieldDescription: Adjusted net income - the lowest estimation
+DataField: anl4_eaz1laf_person
+DataFieldDescription: Broker Id
+DataField: total_assets_reported_value
+DataFieldDescription: Total Assets - actual value
+DataField: anl4_fsactualafv4_actual
+DataFieldDescription: Announced financial data
+DataField: anl4_bac1detailafv110_item
+DataFieldDescription: Financial item
+DataField: financing_cashflow_reported_value
+DataFieldDescription: Cash Flow From Financing - Value
+DataField: anl4_fsguidanceafv4_item
+DataFieldDescription: Financial item
+DataField: dividend_estimate_standard_deviation
+DataFieldDescription: Dividend per share - standard deviation of estimations
+DataField: anl4_cff_value
+DataFieldDescription: Cash Flow From Financing - announced financial value
+DataField: max_operating_cashflow_guidance_2
+DataFieldDescription: The maximum guidance value for Cash Flow from Operations on an annual basis.
+DataField: sales_estimate_average
+DataFieldDescription: Sales - mean of estimations with a delay of 1 quarter
+DataField: anl4_eaz2lltv110_estvalue
+DataFieldDescription: Estimation value
+DataField: shareholders_equity_reported_value
+DataFieldDescription: Shareholders' Equity - Total Value
+DataField: anl4_bac1detaillt_item
+DataFieldDescription: Financial item
+DataField: anl4_basicdetaillt_estvalue
+DataFieldDescription: Estimation value
+DataField: previous_recommendation_value
+DataFieldDescription: The previous estimation of financial item for recommendation
+DataField: anl4_ebitda_flag
+DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - forecast type (revision/new/...)
+DataField: highest_sales_estimate
+DataFieldDescription: Sales - The highest estimation for the annual period
+DataField: anl4_median_epsreported
+DataFieldDescription: GAAP Earnings per share - median of estimations
+DataField: anl4_ads1detailafv110_person
+DataFieldDescription: Broker Id
+DataField: cashflow_per_share_max_guidance
+DataFieldDescription: The maximum guidance value for Cash Flow Per Share on an annual basis.
+DataField: anl4_fsdtlestmtafv4_item
+DataFieldDescription: Financial item
+DataField: anl4_basicconafv110_high
+DataFieldDescription: The highest estimation
+DataField: anl4_guiafv4_est
+DataFieldDescription: Estimation value
+DataField: anl4_fcfps_low
+DataFieldDescription: Free Cash Flow Per Share - the lowest estimation
+DataField: min_financing_cashflow_guidance
+DataFieldDescription: Minimum guidance value for Cash Flow From Financing
+DataField: pv13_hierarchy_min50_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min2_focused_pureplay_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_ompetitorgraphrank_hub_rank
+DataFieldDescription: the HITS hub score of competitors
+DataField: rel_num_all
+DataFieldDescription: number of the companies whose product overlapped with the instrument
+DataField: pv13_6l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_h_min30_3000_mapped_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min51_f3_sector
+DataFieldDescription: grouping fields
+DataField: pv13_reportperiodend
+DataFieldDescription: Stated end date for the report
+DataField: pv13_hierarchy_min22_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_2k_sector
+DataFieldDescription: grouping fields
+DataField: pv13_r2_min5_1000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min5_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min52_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy23_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f4_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min20_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy2_min2_1k_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: primary_sector_focused_company_count
+DataFieldDescription: Number of companies primarily focused in a given sector.
+DataField: pv13_r2_liquid_min10_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min20_3k_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_country
+DataFieldDescription: Country code
+DataField: pv13_r2_min20_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_term
+DataFieldDescription: Indicates when a sector is the terminal sector (i.e., no sub-sectors)
+DataField: pv13_hierarchy_min51_f4_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min5_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: historical_volatility_20
+DataFieldDescription: Close-to-close Historical volatility over 20 days
+DataField: implied_volatility_call_150
+DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
+DataField: implied_volatility_call_1080
+DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
+DataField: historical_volatility_150
+DataFieldDescription: Close-to-close Historical volatility over 150 days
+DataField: parkinson_volatility_30
+DataFieldDescription: Parkinson model's historical volatility over 30 days
+DataField: implied_volatility_mean_skew_20
+DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
+DataField: implied_volatility_mean_1080
+DataFieldDescription: At-the-money option-implied volatility mean for 3 years
+DataField: implied_volatility_put_720
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 720 days
+DataField: implied_volatility_put_30
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
+DataField: implied_volatility_call_60
+DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
+DataField: implied_volatility_mean_skew_30
+DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
+DataField: implied_volatility_call_720
+DataFieldDescription: At-the-money option-implied volatility for call Option for 720 days
+DataField: historical_volatility_90
+DataFieldDescription: Close-to-close Historical volatility over 90 days
+DataField: historical_volatility_10
+DataFieldDescription: Close-to-close Historical volatility over 10 days
+DataField: implied_volatility_mean_skew_90
+DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
+DataField: implied_volatility_mean_skew_60
+DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
+DataField: implied_volatility_mean_720
+DataFieldDescription: At-the-money option-implied volatility mean for 720 days
+DataField: parkinson_volatility_90
+DataFieldDescription: Parkinson model's historical volatility over 90 days
+DataField: implied_volatility_put_150
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
+DataField: implied_volatility_mean_skew_120
+DataFieldDescription: At-the-money option-implied volatility mean skew for 120 days
+DataField: implied_volatility_mean_skew_360
+DataFieldDescription: At-the-money option-implied volatility mean skew for 360 days
+DataField: implied_volatility_call_180
+DataFieldDescription: At-the-money option-implied volatility for call Option for 180 days
+DataField: implied_volatility_put_60
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
+DataField: parkinson_volatility_150
+DataFieldDescription: Parkinson model's historical volatility over 150 days
+DataField: implied_volatility_mean_10
+DataFieldDescription: At-the-money option-implied volatility mean for 10 days
+DataField: implied_volatility_mean_skew_10
+DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
+DataField: implied_volatility_mean_skew_180
+DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
+DataField: implied_volatility_put_180
+DataFieldDescription: At-the-money option-implied volatility for put option for 180 days
+DataField: implied_volatility_mean_360
+DataFieldDescription: At-the-money option-implied volatility mean for 360 days
+DataField: parkinson_volatility_180
+DataFieldDescription: Parkinson model's historical volatility over 180 days
+DataField: news_session_range
+DataFieldDescription: Session High Price - Session Low Price
+DataField: news_mins_20_chg
+DataFieldDescription: The minimum of L or S above for 20-minute bucket
+DataField: nws12_mainz_prevclose
+DataFieldDescription: Previous trading day's close price
+DataField: nws12_prez_opengap
+DataFieldDescription: (DayOpen - PrevClose) / PrevClose.
+DataField: nws12_prez_maxdown
+DataFieldDescription: Percent change from the price at the time of the news to the after the news low
+DataField: nws12_afterhsz_dayopen
+DataFieldDescription: Price at the session open
+DataField: nws12_mainz_57s
+DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
+DataField: news_mins_10_pct_dn
+DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
+DataField: news_indx_perf
+DataFieldDescription: ((EODClose - TONLast) / TONLast) - ((SPYClose - SPYLast) / SPYLast)
+DataField: nws12_afterhsz_lowexcstddev
+DataFieldDescription: (TONLast - EODLow) / StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
+DataField: nws12_afterhsz_1_minute
+DataFieldDescription: The percent change in price in the first minute following the news release
+DataField: nws12_afterhsz_epsactual
+DataFieldDescription: The actual Earnings Per Share value that was conveyed by the news release
+DataField: nws12_prez_60_min
+DataFieldDescription: The percent change in price in the first 60 minutes following the news release
+DataField: nws12_afterhsz_41rta
+DataFieldDescription: 14-day Average True Range
+DataField: nws12_allz_reportsess
+DataFieldDescription: Index of Session on which the spreadsheet is reporting
+DataField: nws12_mainz_rangestddev
+DataFieldDescription: (RangeAmt-AvgRange)/RangeStdDev, where AvgRange is the average of the daily range, and RangeStdDev is one standard deviation for the daily range, both for 30 calendar days
+DataField: nws12_afterhsz_1p
+DataFieldDescription: The minimum of L or S above for 1-minute bucket
+DataField: nws12_prez_tonhigh
+DataFieldDescription: Highest price reached during the session before the time of the news
+DataField: nws12_allz_provider
+DataFieldDescription: index of name of the news provider
+DataField: nws12_prez_tonlow
+DataFieldDescription: Lowest price reached during the session before the time of the news
+DataField: nws12_afterhsz_3s
+DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
+DataField: nws12_prez_10_min
+DataFieldDescription: The percent change in price in the first 10 minutes following the news release
+DataField: nws12_mainz_short_interest
+DataFieldDescription: Total number of shares sold short divided by total number of shares outstanding
+DataField: nws12_prez_5_min
+DataFieldDescription: The percent change in price in the first 5 minutes following the news release
+DataField: news_pct_1min
+DataFieldDescription: The percent change in price in the first minute following the news release
+DataField: nws12_prez_mov_vol
+DataFieldDescription: 30-day moving average session volume
+DataField: nws12_mainz_2p
+DataFieldDescription: The minimum of L or S above for 2 minute bucket
+DataField: nws12_afterhsz_2l
+DataFieldDescription: Number of minutes that elapsed before price went up 2 percentage points
+DataField: news_close_vol
+DataFieldDescription: Main close volume
+DataField: nws12_afterhsz_3l
+DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
+DataField: top1000
+DataFieldDescription: 20140630
+DataField: top200
+DataFieldDescription: 20140630
+DataField: top3000
+DataFieldDescription: 20140630
+DataField: top500
+DataFieldDescription: 20140630
+DataField: topsp500
+DataFieldDescription: 20140630
+DataField: rp_nip_price
+DataFieldDescription: News impact projection of stock price news
+DataField: rp_nip_marketing
+DataFieldDescription: News impact projection of marketing news
+DataField: rp_ess_dividends
+DataFieldDescription: Event sentiment score of dividends news
+DataField: rp_ess_labor
+DataFieldDescription: Event sentiment score of labor issues news
+DataField: rp_css_marketing
+DataFieldDescription: Composite sentiment score of marketing news
+DataField: nws18_ghc_lna
+DataFieldDescription: Change in analyst recommendation
+DataField: rp_ess_credit_ratings
+DataFieldDescription: Event sentiment score of credit ratings news
+DataField: rp_css_legal
+DataFieldDescription: Composite sentiment score of legal news
+DataField: nws18_relevance
+DataFieldDescription: Relevance of news to the company
+DataField: rp_css_mna
+DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
+DataField: rp_css_society
+DataFieldDescription: Composite sentiment score of society-related news
+DataField: rp_css_business
+DataFieldDescription: Composite sentiment score of business-related news
+DataField: nws18_ber
+DataFieldDescription: News sentiment specializing in earnings result
+DataField: rp_ess_technical
+DataFieldDescription: Event sentiment score based on technical analysis
+DataField: rp_css_inverstor
+DataFieldDescription: Composite sentiment score of investor relations news
+DataField: rp_css_labor
+DataFieldDescription: Composite sentiment score of labor issues news
+DataField: nws18_qep
+DataFieldDescription: News sentiment based on positive and negative words on global equity
+DataField: rp_ess_business
+DataFieldDescription: Event sentiment score of business-related news
+DataField: nws18_nip
+DataFieldDescription: Degree of impact of the news
+DataField: nws18_sse
+DataFieldDescription: Sentiment of phrases impacting the company
+DataField: nws18_ssc
+DataFieldDescription: Sentiment of the news calculated using multiple techniques
+DataField: nws18_event_similarity_days
+DataFieldDescription: Days since a similar event was detected
+DataField: rp_css_credit
+DataFieldDescription: Composite sentiment score of credit news
+DataField: rp_css_technical
+DataFieldDescription: Composite sentiment score based on technical analysis
+DataField: rp_ess_society
+DataFieldDescription: Event sentiment score of society-related news
+DataField: rp_nip_technical
+DataFieldDescription: News impact projection based on technical analysis
+DataField: rp_css_ptg
+DataFieldDescription: Composite sentiment score of price target news
+DataField: rp_nip_assets
+DataFieldDescription: News impact projection of assets news
+DataField: rp_nip_ratings
+DataFieldDescription: News impact projection of analyst ratings-related news
+DataField: rp_ess_earnings
+DataFieldDescription: Event sentiment score of earnings news
+DataField: fnd2_a_inventoryrawmaterials
+DataFieldDescription: Amount before valuation and LIFO reserves of raw materials expected to be sold, or consumed within 1 year or operating cycle, if longer.
+DataField: fnd2_ebitfr
+DataFieldDescription: EBIT, Foreign
+DataField: fnd2_itxreclstatelocalitxes
+DataFieldDescription: Amount of the difference between reported income tax expense (benefit) and expected income tax expense (benefit) computed by applying the domestic federal statutory income tax rates to pretax income (loss) from continuing operations attributable to state and local income tax expense (benefit).
+DataField: fn_assets_fair_val_l1_a
+DataFieldDescription: Asset Fair Value, Recurring, Level 1
+DataField: fn_intangible_assets_accum_amort_q
+DataFieldDescription: Accumulated amount of amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
+DataField: fn_new_shares_options_q
+DataFieldDescription: Number of share options (or share units) exercised during the current period.
+DataField: fn_comp_fair_value_assumptions_weighted_avg_vol_rate_a
+DataFieldDescription: Weighted average expected volatility rate of share-based compensation awards.
+DataField: fn_repayments_of_lines_of_credit_a
+DataFieldDescription: Amount of cash outflow for payment of an obligation from a lender, including but not limited to, letter of credit, standby letter of credit and revolving credit arrangements.
+DataField: fnd2_dbplanfvalpnas
+DataFieldDescription: Fair value of assets that have been segregated and restricted to provide pension or postretirement benefits. Assets include, but are not limited to, stocks, bonds, other investments, earnings from investments, and contributions by the employer and employees.
+DataField: fnd2_a_eplsbvdcpcstnrgsbaoo
+DataFieldDescription: Unrecognized cost of unvested other share-based compensation awards.
+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_liab_fair_val_l1_q
+DataFieldDescription: Liabilities Fair Value, Recurring, Level 1
+DataField: fn_def_tax_liab_q
+DataFieldDescription: Amount, after deferred tax asset, of deferred tax liability attributable to taxable differences without jurisdictional netting.
+DataField: fnd2_q_flintasamt1expy5
+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 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_income_from_equity_investments_a
+DataFieldDescription: Income From Equity Method Investments
+DataField: fn_op_lease_min_pay_due_in_4y_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 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_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: fnd2_a_sbcpnargmsptawervl
+DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
+DataField: fnd2_dbplanchgbnfolintcst
+DataFieldDescription: Defined Benefit Plan Change In Benefit Obligation Interest Cost
+DataField: fnd2_q_inventoryrawmaterials
+DataFieldDescription: Amount before valuation and LIFO reserves of raw materials expected to be sold, or consumed within 1 year or operating cycle, if longer.
+DataField: fn_repayments_of_lines_of_credit_q
+DataFieldDescription: Amount of cash outflow for payment of an obligation from a lender, including but not limited to, letter of credit, standby letter of credit and revolving credit arrangements.
+DataField: fn_business_combination_assets_aquired_goodwill_q
+DataFieldDescription: Business Combination, Portion of Purchase Price Allocated to Goodwill
+DataField: fn_comp_options_exercises_weighted_avg_a
+DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
+DataField: fn_op_lease_rent_exp_a
+DataFieldDescription: Rental expense for the reporting period incurred under operating leases, including minimum and any contingent rent expense, net of related sublease income.
+DataField: fnd2_a_lhdiprtsg
+DataFieldDescription: Amount before accumulated depreciation of additions or improvements to assets held under a lease arrangement.
+DataField: fnd2_a_ltrmdmrepoplinyfour
+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 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_propplteqmuflmeqmt
+DataFieldDescription: PPE, Equipment, Useful Life, Minimum
+DataField: fn_comp_not_rec_stock_options_a
+DataFieldDescription: Unrecognized cost of unvested stock option awards.
+DataField: fn_business_acq_ppne_a
+DataFieldDescription: Business Combination, Assumed Property, Plant and Equipment
+DataField: fnd2_a_sbcpnargmpmwggil
+DataFieldDescription: Amount by which the current fair value of the underlying stock exceeds the exercise price of fully vested and expected to vest options outstanding.
+DataField: 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 =======================================
+

manual_prompt/manual_prompt_20251211175017.txt

@@ -0,0 +1,922 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+核心概念：捕捉行业价格的趋势、动量和形态变化
+关键函数：
+ts_delta, ts_mean, ts_regression（获取斜率rettype参数）
+ts_decay_linear, ts_zscore, ts_rank
+ts_scale, ts_av_diff, ts_std_dev
+ts_corr, ts_covariance（用于行业内序列）
+设计思路：
+动量的变化率、加速度或平滑度构建
+动量衰减或增强模式识别
+价格与成交量关系的时序分析
+维度2：横截面领导力（CL）
+核心概念：识别行业内部的分化、龙头效应和相对强度
+关键函数：
+group_mean, group_std, group_rank
+group_zscore, group_neutralize, group_scale
+rank, zscore, quantile（横截面）
+bucket（用于龙头股筛选）
+设计思路：
+行业内部龙头股与平均表现的差异
+行业成分股的离散度分析
+相对排名的变化和稳定性
+维度3：市场状态适应性（MS）
+核心概念：根据市场环境动态调整因子逻辑
+关键函数：
+ts_rank, if_else, 条件判断运算符
+ts_std_dev（用于波动率调整）
+ts_regression（不同状态使用不同参数）
+trade_when（条件触发）
+设计思路：
+波动率调整的动量指标
+不同市场状态（高/低波动）使用不同的回顾期
+条件逻辑下的参数动态调整
+维度4：行业间联动（IS）
+核心概念：捕捉行业间的动量溢出和相关性变化
+关键函数：
+ts_corr, ts_covariance（跨行业）
+group_mean（用于行业指数）
+向量操作：vec_avg, vec_sum
+多序列相关性分析
+设计思路：
+领先-滞后行业的相关性分析
+行业间动量传导效应
+板块轮动的早期信号识别
+维度5：交易行为情绪（TS）
+核心概念：基于交易行为和情绪指标的反转信号
+关键函数：
+ts_corr(volume, close, d)（量价关系）
+ts_rank（历史相对位置）
+ts_zscore（极端值识别）
+days_from_last_change（事件驱动）
+设计思路：
+超买超卖状态识别
+交易拥挤度指标
+情绪极端值后的均值回归
+复合因子设计原则
+强制要求：
+每个表达式必须融合至少两个设计维度
+必须使用提供的操作符列表中的函数
+因子应具有经济逻辑解释性
+推荐组合模式：
+TM + CL：时序动量 + 横截面领导力
+示例：行业动量加速度 × 龙头股相对强度
+TM + MS：时序动量 + 状态适应性
+示例：波动率调整后的动量指标
+CL + IS：横截面 + 行业间联动
+示例：龙头股表现与相关行业的领先滞后关系
+MS + TS：状态适应 + 交易情绪
+示例：不同市场状态下的反转信号
+IS + TS：行业联动 + 交易情绪
+示例：行业间相关性变化与交易拥挤度
+参数化建议：
+使用不同的时间窗口组合（短/中/长周期）
+尝试不同的权重分配方式
+考虑非线性变换（log, power, sqrt）
+使用条件逻辑增强鲁棒性
+表达式构建指南
+基本结构：
+text
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+运算符使用策略：
+算术运算：add, subtract, multiply, divide
+非线性变换：log, power, sqrt, signed_power
+条件逻辑：if_else, and, or, 比较运算符
+标准化处理：normalize, winsorize, scale
+防止过拟合建议：
+避免过度复杂的嵌套
+使用经济直觉验证逻辑合理性
+考虑实际交易可行性
+包含风险控制元素（如波动率调整）
+*=====*
+操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
+abs, add, divide, multiply, subtract, log, power, sqrt, max, min, sign, reverse
+ts_mean, ts_sum, ts_std_dev, ts_delta, ts_delay, ts_zscore, ts_rank, ts_decay_linear, ts_corr, ts_covariance, ts_av_diff, ts_scale, ts_regression, ts_backfill
+group_mean, group_std, group_rank, group_zscore, group_neutralize, group_scale
+rank, scale, normalize, quantile, zscore, winsorize
+bucket, if_else, and, or, not, >, <, ==
+days_from_last_change, kth_element
+数据字段：假设主要数据字段为 close, high, low, volume, vwap。可安全使用。
+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。
+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。
+输出格式：
+输出必须是且仅是 100行纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+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。
+示例思维（仅供理解，不输出）
+一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
+multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
+这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
+现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+请提供具体的WQ表达式。
+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。
+
+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
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+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
+
+Operator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false), x + y
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+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
+Operator: divide(x, y), x / y
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false), x * y
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false), x - y
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+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).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+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
+Operator: ts_arg_min(x, d)
+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.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+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.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+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.
+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.========================= 操作符结束 =======================================
+
+========================= 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
+
+DataField: pcr_vol_1080
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 1080 days in the future.
+DataField: put_breakeven_30
+DataFieldDescription: Price at which a stock's put options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_720
+DataFieldDescription: Forward price at 720 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: call_breakeven_10
+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.
+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_vol_180
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 180 days in the future.
+DataField: pcr_oi_20
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 20 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: pcr_vol_270
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 270 days in the future.
+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: option_breakeven_150
+DataFieldDescription: Price at which a stock's options with expiration 150 days in the future break even based on its recent bid/ask mean.
+DataField: 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: 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: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: 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.
+DataField: option_breakeven_360
+DataFieldDescription: Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: call_breakeven_360
+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.
+DataField: call_breakeven_90
+DataFieldDescription: Price at which a stock's call options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_10
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 10 days in the future.
+DataField: 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.
+DataField: put_breakeven_1080
+DataFieldDescription: Price at which a stock's put options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_60
+DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_vol_30
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 30 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: put_breakeven_90
+DataFieldDescription: Price at which a stock's put options with expiration 90 days in the future break even based on its recent bid/ask mean.
+DataField: put_breakeven_10
+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.
+DataField: pcr_vol_90
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 90 days in the future.
+DataField: pcr_vol_360
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 360 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_vol_all
+DataFieldDescription: Ratio of put volume to call volume for all maturities on stock's options.
+DataField: fnd6_newqv1300_tstknq
+DataFieldDescription: Treasury Stock - Number of Common Shares
+DataField: fnd6_newqeventv110_esopnrq
+DataFieldDescription: Preferred ESOP Obligation - Non-Redeemable
+DataField: fnd6_dd3
+DataFieldDescription: Debt Due in 3rd Year
+DataField: fnd6_newa2v1300_mib
+DataFieldDescription: Minority Interest (Balance Sheet)
+DataField: fnd6_txtubposdec
+DataFieldDescription: Decrease - Current Tax Positions
+DataField: fnd6_cptnewqv1300_oeps12
+DataFieldDescription: Earnings Per Share from Operations - 12 Months Moving
+DataField: fnd6_ocaxs
+DataFieldDescription: Other Costs and Expenses
+DataField: fnd6_sppiv
+DataFieldDescription: Sale of Property, Plant and Equipment and Investments - Gain (Loss)
+DataField: fnd6_newqeventv110_aociotherq
+DataFieldDescription: Accum Other Comp Inc - Other Adjustments
+DataField: fnd6_newqv1300_lnoq
+DataFieldDescription: Liabilities Netting & Other Adjustments
+DataField: fnd6_newqeventv110_aqpq
+DataFieldDescription: Acquisition/Merger Pretax
+DataField: fnd6_mfma1_at
+DataFieldDescription: Assets - Total
+DataField: interest_expense
+DataFieldDescription: Interest and Related Expense - Total
+DataField: fnd6_newqeventv110_ppentq
+DataFieldDescription: Property Plant and Equipment - Total (Net)
+DataField: fnd6_newqeventv110_pnc12
+DataFieldDescription: Pension Core Adjustment - 12mm
+DataField: fnd6_newqeventv110_pncwippq
+DataFieldDescription: Core Pension w/o Interest Adjustment Pretax Preliminary
+DataField: fnd6_eventv110_optdrq
+DataFieldDescription: Dividend Rate - Assumption (%)
+DataField: fnd6_cptnewqeventv110_actq
+DataFieldDescription: Current Assets - Total
+DataField: fnd6_city
+DataFieldDescription: the city where a company's corporate headquarters or home office is located
+DataField: fnd6_newqeventv110_glaq
+DataFieldDescription: Gain/Loss After-Tax
+DataField: fnd6_itci
+DataFieldDescription: Investment Tax Credit (Income Account)
+DataField: fnd6_cshtrq
+DataFieldDescription: Common Shares Traded - Quarter
+DataField: fnd6_idit
+DataFieldDescription: Interest and Related Income - Total
+DataField: fnd6_recco
+DataFieldDescription: Receivables - Current - Other
+DataField: fnd6_eventv110_nrtxtdq
+DataFieldDescription: Nonrecurring Income Taxes Diluted EPS Effect
+DataField: fnd6_newa2v1300_txt
+DataFieldDescription: Income Taxes - Total
+DataField: fnd6_divd
+DataFieldDescription: Cash Dividends - Daily
+DataField: fnd6_txtubposinc
+DataFieldDescription: Increase - Current Tax Positions
+DataField: fnd6_dltis
+DataFieldDescription: Long-Term Debt - Issuance
+DataField: fnd6_spce
+DataFieldDescription: S&P Core Earnings
+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: min_share_count_guidance
+DataFieldDescription: Minimum guidance for shares on an annual basis
+DataField: book_value_per_share_min_guidance_qtr
+DataFieldDescription: Book value per share - minimum guidance value
+DataField: anl4_ebitda_std
+DataFieldDescription: Earnings before interest, taxes, depreciation, and amortization - standard deviation of estimations
+DataField: anl4_ptpr_median
+DataFieldDescription: Reported pretax income - Median of estimations
+DataField: anl4_netprofita_low
+DataFieldDescription: Adjusted net income - the lowest estimation
+DataField: anl4_qfv4_actual
+DataFieldDescription: Announced financial data
+DataField: actuals_value_currency_code
+DataFieldDescription: Pricing Currency where the security trades
+DataField: sales_estimate_average_quarterly
+DataFieldDescription: Sales - mean of estimations
+DataField: anl4_epsa_flag
+DataFieldDescription: Earnings per share adjusted by excluding extraordinary items and stock option expenses - forecast type (revision/new/...)
+DataField: earnings_per_share_nongaap_value
+DataFieldDescription: Non-GAAP Earnings Per Share - Actual Value
+DataField: max_adjusted_net_income_guidance
+DataFieldDescription: The maximum guidance value for Adjusted net income.
+DataField: anl4_medianepsbfam
+DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - median of estimations
+DataField: actual_eps_value_quarterly
+DataFieldDescription: Earnings Per Share (Income Statement/Per Share) (Actual)
+DataField: cashflow_per_share_max_guidance_quarterly
+DataFieldDescription: The maximum guidance value for Cash Flow Per Share.
+DataField: anl4_eaz2lqfv110_estvalue
+DataFieldDescription: Estimation value
+DataField: dividend_estimate_median_value
+DataFieldDescription: Dividend per share - median of estimations
+DataField: anl4_afv4_div_median
+DataFieldDescription: Dividend per share - Median value among forecasts
+DataField: anl4_ptp_high
+DataFieldDescription: Pretax income - the highest estimation
+DataField: earnings_per_share_reported_value
+DataFieldDescription: Reported Earnings Per Share - Actual Value
+DataField: min_gross_income_guidance_2
+DataFieldDescription: The minimum guidance for Gross Income on an annual basis.
+DataField: min_free_cash_flow_guidance
+DataFieldDescription: The minimum guidance value for Free Cash Flow on an annual basis.
+DataField: anl4_tbvps_number
+DataFieldDescription: Tangible Book Value per Share - number of estimations
+DataField: net_profit_adjusted_value
+DataFieldDescription: Adjusted net income- announced financial value
+DataField: anl4_ads1detailqfv110_prevval
+DataFieldDescription: The previous estimation of financial item
+DataField: anl4_cfo_mean
+DataFieldDescription: Cash Flow From Operations - mean of estimations
+DataField: guidance_previous_estimate_value_qtr
+DataFieldDescription: The previous estimation of finanicial item
+DataField: anl4_netprofita_median
+DataFieldDescription: Adjusted net income - median of estimations
+DataField: reporting_currency_code_9
+DataFieldDescription: Home currency of instrument
+DataField: anl4_cfi_median
+DataFieldDescription: Cash Flow From Investing - median of estimations
+DataField: anl4_netdebt_number
+DataFieldDescription: Net debt - Number of estimations
+DataField: pv13_h2_min2_1k_sector
+DataFieldDescription: Grouping fields for top 1000
+DataField: pv13_hierarchy_min51_f4_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min30_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min10_513_sector
+DataFieldDescription: grouping fields
+DataField: rel_ret_all
+DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
+DataField: pv13_hierarchy_min52_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min51_f3_sector
+DataFieldDescription: grouping fields
+DataField: pv13_revere_index_cap
+DataFieldDescription: Company market capitalization
+DataField: pv13_hierarchy_min10_top3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_reportperiodlen
+DataFieldDescription: The number of units which the report covers prior to the stated end date
+DataField: pv13_hierarchy_min100_corr21_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_reporttype
+DataFieldDescription: Type of report
+DataField: pv13_revere_company_total
+DataFieldDescription: Total number of companies in the sector
+DataField: pv13_hierarchy_min2_focused_pureplay_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min10_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min22_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f4_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min30_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min5_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min2_focused_pureplay_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min20_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_new_3l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_ustomergraphrank_auth_rank
+DataFieldDescription: the HITS authority score of customers
+DataField: pv13_hierarchy_min2_focused_pureplay_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_2k_sector
+DataFieldDescription: grouping fields
+DataField: implied_volatility_put_60
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 60 days
+DataField: implied_volatility_mean_skew_60
+DataFieldDescription: At-the-money option-implied volatility mean skew for 60 days
+DataField: implied_volatility_mean_skew_90
+DataFieldDescription: At-the-money option-implied volatility mean skew for 90 days
+DataField: implied_volatility_call_120
+DataFieldDescription: At-the-money option-implied volatility for call Option for 120 days
+DataField: implied_volatility_put_10
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
+DataField: implied_volatility_mean_skew_10
+DataFieldDescription: At-the-money option-implied volatility mean skew for 10 days
+DataField: implied_volatility_mean_360
+DataFieldDescription: At-the-money option-implied volatility mean for 360 days
+DataField: historical_volatility_10
+DataFieldDescription: Close-to-close Historical volatility over 10 days
+DataField: parkinson_volatility_90
+DataFieldDescription: Parkinson model's historical volatility over 90 days
+DataField: historical_volatility_60
+DataFieldDescription: Close-to-close Historical volatility over 60 days
+DataField: implied_volatility_call_360
+DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
+DataField: historical_volatility_90
+DataFieldDescription: Close-to-close Historical volatility over 90 days
+DataField: historical_volatility_120
+DataFieldDescription: Close-to-close Historical volatility over 120 days
+DataField: implied_volatility_mean_150
+DataFieldDescription: At-the-money option-implied volatility mean for 150 days
+DataField: implied_volatility_mean_20
+DataFieldDescription: At-the-money option-implied volatility mean for 20 days
+DataField: parkinson_volatility_120
+DataFieldDescription: Parkinson model's historical volatility over 120 days
+DataField: implied_volatility_mean_1080
+DataFieldDescription: At-the-money option-implied volatility mean for 3 years
+DataField: implied_volatility_put_150
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 150 days
+DataField: parkinson_volatility_60
+DataFieldDescription: Parkinson model's historical volatility over 60 days
+DataField: implied_volatility_call_20
+DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
+DataField: implied_volatility_mean_skew_30
+DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
+DataField: implied_volatility_mean_270
+DataFieldDescription: At-the-money option-implied volatility mean for 270 days
+DataField: implied_volatility_call_150
+DataFieldDescription: At-the-money option-implied volatility for call Option for 150 days
+DataField: implied_volatility_mean_skew_270
+DataFieldDescription: At-the-money option-implied volatility mean skew for 270 days
+DataField: implied_volatility_mean_30
+DataFieldDescription: At-the-money option-implied volatility mean for 30 days
+DataField: parkinson_volatility_150
+DataFieldDescription: Parkinson model's historical volatility over 150 days
+DataField: parkinson_volatility_180
+DataFieldDescription: Parkinson model's historical volatility over 180 days
+DataField: historical_volatility_150
+DataFieldDescription: Close-to-close Historical volatility over 150 days
+DataField: implied_volatility_put_90
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 90 days
+DataField: implied_volatility_call_10
+DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
+DataField: news_pct_5_min
+DataFieldDescription: The percent change in price in the first 5 minutes following the news release
+DataField: nws12_mainz_02l
+DataFieldDescription: Number of minutes that elapsed before price went up 20 percentage points
+DataField: nws12_prez_57p
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: nws12_mainz_4s
+DataFieldDescription: Number of minutes that elapsed before price went down 4 percentage points
+DataField: nws12_prez_open_vol
+DataFieldDescription: Main open volume
+DataField: news_eod_vwap
+DataFieldDescription: Volume weighted average price between the time of news and the end of the session
+DataField: news_mins_20_chg
+DataFieldDescription: The minimum of L or S above for 20-minute bucket
+DataField: nws12_prez_4s
+DataFieldDescription: Number of minutes that elapsed before price went down 4 percentage points
+DataField: nws12_prez_57l
+DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
+DataField: nws12_afterhsz_90_min
+DataFieldDescription: The percent change in price in the first 90 minutes following the news release
+DataField: nws12_afterhsz_5l
+DataFieldDescription: Number of minutes that elapsed before price went up 5 percentage points
+DataField: nws12_afterhsz_01p
+DataFieldDescription: The minimum of L or S above for 10 minute bucket
+DataField: news_mins_3_pct_up
+DataFieldDescription: Number of minutes that elapsed before price went up 3 percentage points
+DataField: nws12_afterhsz_tonlow
+DataFieldDescription: Lowest price reached during the session before the time of the news
+DataField: news_mins_10_pct_dn
+DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
+DataField: nws12_mainz_rangeamt
+DataFieldDescription: Session High Price - Session Low Price
+DataField: nws12_afterhsz_57l
+DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
+DataField: nws12_afterhsz_01l
+DataFieldDescription: Number of minutes that elapsed before price went up 10 percentage points
+DataField: nws12_mainz_prev_vol
+DataFieldDescription: Previous day's session volume
+DataField: news_pct_10min
+DataFieldDescription: The percent change in price in the first 10 minutes following the news release
+DataField: nws12_allz_reportsess
+DataFieldDescription: Index of Session on which the spreadsheet is reporting
+DataField: nws12_mainz_prevclose
+DataFieldDescription: Previous trading day's close price
+DataField: nws12_prez_curr_vol
+DataFieldDescription: Current day's session volume
+DataField: news_all_vwap
+DataFieldDescription: Volume weighted average price of all sessions
+DataField: nws12_mainz_mktcap
+DataFieldDescription: Reported market capitalization for the calendar day of the session
+DataField: nws12_prez_90_min
+DataFieldDescription: The percent change in price in the first 90 minutes following the news release
+DataField: nws12_prez_allvwap
+DataFieldDescription: Volume weighted average price of all sessions
+DataField: news_mins_7_5_pct_dn
+DataFieldDescription: Number of minutes that elapsed before price went down 7.5 percentage points
+DataField: news_main_vwap
+DataFieldDescription: Main session volume weighted average price
+DataField: nws12_afterhsz_prev_vol
+DataFieldDescription: Previous day's session volume
+DataField: top1000
+DataFieldDescription: 20140630
+DataField: top200
+DataFieldDescription: 20140630
+DataField: top3000
+DataFieldDescription: 20140630
+DataField: top500
+DataFieldDescription: 20140630
+DataField: topsp500
+DataFieldDescription: 20140630
+DataField: rp_ess_mna
+DataFieldDescription: Event sentiment score of mergers and acquisitions-related news
+DataField: rp_nip_revenue
+DataFieldDescription: News impact projection of revenue news
+DataField: rp_nip_price
+DataFieldDescription: News impact projection of stock price news
+DataField: rp_ess_technical
+DataFieldDescription: Event sentiment score based on technical analysis
+DataField: rp_nip_ratings
+DataFieldDescription: News impact projection of analyst ratings-related news
+DataField: rp_css_business
+DataFieldDescription: Composite sentiment score of business-related news
+DataField: rp_nip_earnings
+DataFieldDescription: News impact projection of earnings news
+DataField: rp_ess_assets
+DataFieldDescription: Event sentiment score of assets news
+DataField: rp_nip_insider
+DataFieldDescription: News impact projection of insider trading news
+DataField: rp_ess_equity
+DataFieldDescription: Event sentiment score of equity action news
+DataField: rp_ess_business
+DataFieldDescription: Event sentiment score of business-related news
+DataField: rp_nip_society
+DataFieldDescription: News impact projection of society-related news
+DataField: nws18_qcm
+DataFieldDescription: News sentiment of relevant news with high confidence
+DataField: rp_ess_price
+DataFieldDescription: Event sentiment score of stock price news
+DataField: rp_ess_ratings
+DataFieldDescription: Event sentiment score of analyst ratings-related news
+DataField: nws18_bee
+DataFieldDescription: News sentiment specializing in growth of earnings
+DataField: rp_ess_society
+DataFieldDescription: Event sentiment score of society-related news
+DataField: rp_css_assets
+DataFieldDescription: Composite sentiment score of assets news
+DataField: rp_ess_dividends
+DataFieldDescription: Event sentiment score of dividends news
+DataField: nws18_acb
+DataFieldDescription: News sentiment specializing in corporate action announcements
+DataField: rp_nip_assets
+DataFieldDescription: News impact projection of assets news
+DataField: rp_css_price
+DataFieldDescription: Composite sentiment score of stock price 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_credit
+DataFieldDescription: Event sentiment score of credit news
+DataField: nws18_qmb
+DataFieldDescription: News sentiment specializing in editorials on global markets
+DataField: rp_nip_legal
+DataFieldDescription: News impact projection of legal news
+DataField: rp_ess_legal
+DataFieldDescription: Event sentiment score of legal news
+DataField: nws18_sse
+DataFieldDescription: Sentiment of phrases impacting the company
+DataField: rp_nip_credit_ratings
+DataFieldDescription: News impact projection of credit ratings news
+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: fn_finite_lived_intangible_assets_gross_a
+DataFieldDescription: Amount before amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
+DataField: fn_debt_instrument_interest_rate_stated_percentage_q
+DataFieldDescription: Stated percentage of interest rate on debt
+DataField: fn_comp_not_rec_q
+DataFieldDescription: Unrecognized cost of unvested share-based compensation awards.
+DataField: fn_assets_fair_val_a
+DataFieldDescription: Asset Fair Value, Recurring, Total
+DataField: fnd2_a_sbcpnargmsawpfipwerpr
+DataFieldDescription: Weighted average price of options that were either forfeited or expired.
+DataField: fn_oth_income_loss_available_for_sale_securities_adj_of_tax_a
+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: fnd2_dbplanbnfpaid
+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: fnd2_dfdfritxexp
+DataFieldDescription: Income Tax Expense, Deferred - Foreign
+DataField: fnd2_unrgtxbnfinregfcrps
+DataFieldDescription: Amount of increase in unrecognized tax benefits resulting from tax positions that have been or will be taken in current period tax return.
+DataField: fnd2_a_consinprogressg
+DataFieldDescription: Amount of structure or a modification to a structure under construction. Includes recently completed structures or modifications to structures that have not been placed into service.
+DataField: fn_comp_options_out_intrinsic_value_q
+DataFieldDescription: The intrinsic value of a stock option is the amount by which the market value of the underlying stock exceeds the exercise price of the option.
+DataField: fn_intangible_assets_accum_amort_q
+DataFieldDescription: Accumulated amount of amortization of assets, excluding financial assets and goodwill, lacking physical substance with a finite life.
+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: fnd2_currfedtxexp
+DataFieldDescription: Income Tax Expense, Current - Federal
+DataField: fnd2_ebitfr
+DataFieldDescription: EBIT, Foreign
+DataField: fn_business_acq_ppne_q
+DataFieldDescription: Business Combination, Assumed Property Plant And Equipment
+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: fnd2_a_dbplanepdrtnplas
+DataFieldDescription: An amount calculated as a basis for determining the extent of delayed recognition of the effects of changes in the fair value of assets. The expected return on plan assets is determined based on the expected long-term rate of return on plan assets and the market-related value of plan assets.
+DataField: fnd2_q_flintasamt1expy5
+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 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_repurchased_shares_q
+DataFieldDescription: Number of shares that have been repurchased during the period.
+DataField: fnd2_a_gwllimrml
+DataFieldDescription: Amount of loss from the write-down of an asset representing the future economic benefits arising from other assets acquired in a business combination that are not individually identified and separately recognized.
+DataField: 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: fnd2_a_sbcpnargmsptawervl
+DataFieldDescription: Amount of accumulated difference between fair value of underlying shares on dates of exercise and exercise price on options exercised (or share units converted) into shares.
+DataField: fn_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_interest_payable_q
+DataFieldDescription: Carrying value as of the balance sheet date of accrued interest payable on all forms of debt, including trade payables, that has been incurred and is unpaid. 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: fnd2_a_eplsbvdcpcstnrgsbaoo
+DataFieldDescription: Unrecognized cost of unvested other share-based compensation awards.
+DataField: fn_proceeds_from_issuance_of_debt_a
+DataFieldDescription: The cash inflow during the period from additional borrowings in aggregate debt. Includes proceeds from short-term and long-term debt.
+DataField: fn_finite_lived_intangible_assets_net_q
+DataFieldDescription: Finite Lived Intangible Assets, Net
+DataField: fnd2_a_stkdrgprdvalnewissues
+DataFieldDescription: Equity impact of the value of new stock issued during the period. Includes shares issued in an initial public offering or a secondary public offering.
+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 =======================================
+

manual_prompt/manual_prompt_20251211175551.txt

@@ -0,0 +1,922 @@
+任务指令
+你是一个WorldQuant WebSim因子工程师。你的任务是生成100个用于行业轮动策略的复合型Alpha因子表达式。
+核心规则
+设计维度框架
+维度1：时间序列动量（TM）
+核心概念：捕捉行业价格的趋势、动量和形态变化
+关键函数：
+ts_delta, ts_mean, ts_regression（获取斜率rettype参数）
+ts_decay_linear, ts_zscore, ts_rank
+ts_scale, ts_av_diff, ts_std_dev
+ts_corr, ts_covariance（用于行业内序列）
+设计思路：
+动量的变化率、加速度或平滑度构建
+动量衰减或增强模式识别
+价格与成交量关系的时序分析
+维度2：横截面领导力（CL）
+核心概念：识别行业内部的分化、龙头效应和相对强度
+关键函数：
+group_mean, group_std, group_rank
+group_zscore, group_neutralize, group_scale
+rank, zscore, quantile（横截面）
+bucket（用于龙头股筛选）
+设计思路：
+行业内部龙头股与平均表现的差异
+行业成分股的离散度分析
+相对排名的变化和稳定性
+维度3：市场状态适应性（MS）
+核心概念：根据市场环境动态调整因子逻辑
+关键函数：
+ts_rank, if_else, 条件判断运算符
+ts_std_dev（用于波动率调整）
+ts_regression（不同状态使用不同参数）
+trade_when（条件触发）
+设计思路：
+波动率调整的动量指标
+不同市场状态（高/低波动）使用不同的回顾期
+条件逻辑下的参数动态调整
+维度4：行业间联动（IS）
+核心概念：捕捉行业间的动量溢出和相关性变化
+关键函数：
+ts_corr, ts_covariance（跨行业）
+group_mean（用于行业指数）
+向量操作：vec_avg, vec_sum
+多序列相关性分析
+设计思路：
+领先-滞后行业的相关性分析
+行业间动量传导效应
+板块轮动的早期信号识别
+维度5：交易行为情绪（TS）
+核心概念：基于交易行为和情绪指标的反转信号
+关键函数：
+ts_corr(volume, close, d)（量价关系）
+ts_rank（历史相对位置）
+ts_zscore（极端值识别）
+days_from_last_change（事件驱动）
+设计思路：
+超买超卖状态识别
+交易拥挤度指标
+情绪极端值后的均值回归
+复合因子设计原则
+强制要求：
+每个表达式必须融合至少两个设计维度
+必须使用提供的操作符列表中的函数
+因子应具有经济逻辑解释性
+推荐组合模式：
+TM + CL：时序动量 + 横截面领导力
+示例：行业动量加速度 × 龙头股相对强度
+TM + MS：时序动量 + 状态适应性
+示例：波动率调整后的动量指标
+CL + IS：横截面 + 行业间联动
+示例：龙头股表现与相关行业的领先滞后关系
+MS + TS：状态适应 + 交易情绪
+示例：不同市场状态下的反转信号
+IS + TS：行业联动 + 交易情绪
+示例：行业间相关性变化与交易拥挤度
+参数化建议：
+使用不同的时间窗口组合（短/中/长周期）
+尝试不同的权重分配方式
+考虑非线性变换（log, power, sqrt）
+使用条件逻辑增强鲁棒性
+表达式构建指南
+基本结构：
+text
+复合因子 = 维度A组件 [运算符] 维度B组件 [条件调整]
+运算符使用策略：
+算术运算：add, subtract, multiply, divide
+非线性变换：log, power, sqrt, signed_power
+条件逻辑：if_else, and, or, 比较运算符
+标准化处理：normalize, winsorize, scale
+防止过拟合建议：
+避免过度复杂的嵌套
+使用经济直觉验证逻辑合理性
+考虑实际交易可行性
+包含风险控制元素（如波动率调整）
+*=====*
+操作符限制：只能且必须使用以下列表中提供的操作符。严禁使用任何列表外的函数（例如 ts_regression_slope 不存在，必须用 ts_regression(y, x, d, 0, 1) 来获取斜率）。
+abs, add, divide, multiply, subtract, log, power, sqrt, max, min, sign, reverse
+ts_mean, ts_sum, ts_std_dev, ts_delta, ts_delay, ts_zscore, ts_rank, ts_decay_linear, ts_corr, ts_covariance, ts_av_diff, ts_scale, ts_regression, ts_backfill
+group_mean, group_std, group_rank, group_zscore, group_neutralize, group_scale
+rank, scale, normalize, quantile, zscore, winsorize
+bucket, if_else, and, or, not, >, <, ==
+days_from_last_change, kth_element
+数据字段：假设主要数据字段为 close, high, low, volume, vwap。可安全使用。
+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。
+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。
+输出格式：
+输出必须是且仅是 100行纯文本。
+每一行是一个完整、独立、语法正确的WebSim表达式。
+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。
+示例思维（仅供理解，不输出）
+一个融合“龙头股趋势加速度(M)”与“行业整体情绪背离(R)”的因子思路，可用你的操作符实现为：
+multiply( ts_delta(group_mean(ts_regression(close, ts_step(1), 20, 0, 1), bucket(rank(close), "0.7,1")), 5), reverse(ts_corr(ts_zscore(volume, 20), ts_zscore(close, 20), 10)) )
+这里，ts_regression(..., rettype=1)获取斜率代替动量，bucket(rank(close), "0.7,1")近似选取市值前30%的龙头股，ts_corr(...)衡量价量情绪，reverse将其转化为背离信号。
+现在，请严格遵守以上所有规则，开始生成100行可立即在WebSim中运行的复合因子表达式。
+**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 不需要序号, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+请提供具体的WQ表达式。
+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。
+
+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
+
+========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
+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
+
+Operator: abs(x)
+Description: Absolute value of x
+Operator: add(x, y, filter = false), x + y
+Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
+Operator: densify(x)
+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
+Operator: divide(x, y), x / y
+Description: x / y
+Operator: inverse(x)
+Description: 1 / x
+Operator: log(x)
+Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
+Operator: max(x, y, ..)
+Description: Maximum value of all inputs. At least 2 inputs are required
+Operator: min(x, y ..)
+Description: Minimum value of all inputs. At least 2 inputs are required
+Operator: multiply(x ,y, ... , filter=false), x * y
+Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
+Operator: power(x, y)
+Description: x ^ y
+Operator: reverse(x)
+Description: - x
+Operator: sign(x)
+Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
+Operator: signed_power(x, y)
+Description: x raised to the power of y such that final result preserves sign of x
+Operator: sqrt(x)
+Description: Square root of x
+Operator: subtract(x, y, filter=false), x - y
+Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
+Operator: and(input1, input2)
+Description: Logical AND operator, returns true if both operands are true and returns false otherwise
+Operator: if_else(input1, input2, input 3)
+Description: If input1 is true then return input2 else return input3.
+Operator: input1 < input2
+Description: If input1 < input2 return true, else return false
+Operator: input1 <= input2
+Description: Returns true if input1 <= input2, return false otherwise
+Operator: input1 == input2
+Description: Returns true if both inputs are same and returns false otherwise
+Operator: input1 > input2
+Description: Logic comparison operators to compares two inputs
+Operator: input1 >= input2
+Description: Returns true if input1 >= input2, return false otherwise
+Operator: input1!= input2
+Description: Returns true if both inputs are NOT the same and returns false otherwise
+Operator: is_nan(input)
+Description: If (input == NaN) return 1 else return 0
+Operator: not(x)
+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).
+Operator: or(input1, input2)
+Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
+Operator: days_from_last_change(x)
+Description: Amount of days since last change of x
+Operator: hump(x, hump = 0.01)
+Description: Limits amount and magnitude of changes in input (thus reducing turnover)
+Operator: kth_element(x, d, k)
+Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
+Operator: last_diff_value(x, d)
+Description: Returns last x value not equal to current x value from last d days
+Operator: ts_arg_max(x, d)
+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
+Operator: ts_arg_min(x, d)
+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.
+Operator: ts_av_diff(x, d)
+Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
+Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
+Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
+Operator: ts_corr(x, y, d)
+Description: Returns correlation of x and y for the past d days
+Operator: ts_count_nans(x ,d)
+Description: Returns the number of NaN values in x for the past d days
+Operator: ts_covariance(y, x, d)
+Description: Returns covariance of y and x for the past d days
+Operator: ts_decay_linear(x, d, dense = false)
+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.
+Operator: ts_delay(x, d)
+Description: Returns x value d days ago
+Operator: ts_delta(x, d)
+Description: Returns x - ts_delay(x, d)
+Operator: ts_mean(x, d)
+Description: Returns average value of x for the past d days.
+Operator: ts_product(x, d)
+Description: Returns product of x for the past d days
+Operator: ts_quantile(x,d, driver="gaussian" )
+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.
+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.========================= 操作符结束 =======================================
+
+========================= 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
+
+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: forward_price_10
+DataFieldDescription: Forward price at 10 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: call_breakeven_1080
+DataFieldDescription: Price at which a stock's call options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+DataField: 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: call_breakeven_10
+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.
+DataField: forward_price_60
+DataFieldDescription: Forward price at 60 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: pcr_oi_30
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 30 days in the future.
+DataField: forward_price_270
+DataFieldDescription: Forward price at 270 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: put_breakeven_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: pcr_oi_1080
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 1080 days in the future.
+DataField: put_breakeven_270
+DataFieldDescription: Price at which a stock's put options with expiration 270 days in the future break even based on its recent bid/ask mean.
+DataField: option_breakeven_360
+DataFieldDescription: Price at which a stock's options with expiration 360 days in the future break even based on its recent bid/ask mean.
+DataField: forward_price_20
+DataFieldDescription: Forward price at 20 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.
+DataField: 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_10
+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.
+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: call_breakeven_90
+DataFieldDescription: Price at which a stock's call options with expiration 90 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.
+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: option_breakeven_30
+DataFieldDescription: Price at which a stock's options with expiration 30 days in the future break even based on its recent bid/ask mean.
+DataField: pcr_oi_150
+DataFieldDescription: Ratio of put open interest to call open interest on a stock's options with expiration 150 days in the future.
+DataField: pcr_vol_90
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 90 days in the future.
+DataField: option_breakeven_1080
+DataFieldDescription: Price at which a stock's options with expiration 1080 days in the future break even based on its recent bid/ask mean.
+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: 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: pcr_vol_10
+DataFieldDescription: Ratio of put volume to call volume on a stock's options with expiration 10 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: forward_price_360
+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.
+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: 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: fnd6_prstkc
+DataFieldDescription: Purchase of Common and Preferred Stock
+DataField: fnd6_lqpl1
+DataFieldDescription: Liabilities Level 1 (Quoted Prices)
+DataField: fnd6_incorp
+DataFieldDescription: Incorporated
+DataField: fnd6_pnrsho
+DataFieldDescription: Nonred Pfd Shares Outs (000)
+DataField: fnd6_newqv1300_ivstq
+DataFieldDescription: Short-Term Investments - Total
+DataField: fnd6_newa2v1300_tstkn
+DataFieldDescription: Treasury Stock - Number of Common Shares
+DataField: bookvalue_ps
+DataFieldDescription: Book Value Per Share
+DataField: fnd6_newqeventv110_pncwipq
+DataFieldDescription: Core Pension Without Interest Adjustment Pretax
+DataField: fnd6_mfma1_csho
+DataFieldDescription: Common Shares Outstanding
+DataField: inventory_turnover
+DataFieldDescription: Inventory Turnover
+DataField: fnd6_newqv1300_loq
+DataFieldDescription: Liabilities - Other
+DataField: fnd6_newqeventv110_xopteps12
+DataFieldDescription: Implied Option EPS Basic 12MM
+DataField: fnd6_newqv1300_glced12
+DataFieldDescription: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS Effect 12MM
+DataField: cogs
+DataFieldDescription: Cost of Goods Sold
+DataField: fnd6_dudd
+DataFieldDescription: Debt - Unamortized Debt Discount and Other
+DataField: fnd6_newqeventv110_dvpq
+DataFieldDescription: Dividends - Preferred/Preference
+DataField: fnd6_beta
+DataFieldDescription: beta
+DataField: fnd6_dxd5
+DataFieldDescription: Debt (excl Capitalized Leases) - Due in 5th Year
+DataField: fnd6_cik
+DataFieldDescription: nonimportant technical code
+DataField: fnd6_newqeventv110_txdbq
+DataFieldDescription: Deferred Taxes - Balance Sheet
+DataField: fnd6_optrfr
+DataFieldDescription: Risk-Free Rate - Assumption (%)
+DataField: fnd6_newa1v1300_aoloch
+DataFieldDescription: Assets and Liabilities - Other - Net Change
+DataField: fnd6_newqeventv110_prcd12
+DataFieldDescription: Core Post Retirement Adjustment Diluted EPS Effect 12 MM
+DataField: fnd6_newqeventv110_epsfiq
+DataFieldDescription: Earnings Per Share (Diluted) - Including Extraordinary Items
+DataField: fnd6_newqv1300_ciotherq
+DataFieldDescription: Comp Inc - Other Adj
+DataField: equity
+DataFieldDescription: Common/Ordinary Equity - Total
+DataField: pretax_income
+DataFieldDescription: Pretax Income
+DataField: debt_st
+DataFieldDescription: Debt in Current Liabilities
+DataField: fnd6_cptmfmq_opepsq
+DataFieldDescription: Earnings Per Share from Operations
+DataField: fnd6_newqeventv110_dilavq
+DataFieldDescription: Dilution Available - Excluding Extraordinary Items
+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_eaz1laf_bk
+DataFieldDescription: Broker name (int)
+DataField: anl4_basicdetaillt_prevval
+DataFieldDescription: The Previous Estimation of Financial Item
+DataField: cash_flow_from_investing
+DataFieldDescription: Cash Flow from Investing - Value
+DataField: max_adjusted_eps_guidance
+DataFieldDescription: The maximum guidance value for adjusted earnings per share.
+DataField: min_adjusted_funds_from_operations_guidance
+DataFieldDescription: Funds from operation - minimum guidance value
+DataField: sales_estimate_standard_deviation
+DataFieldDescription: Sales - standard deviation of estimations
+DataField: anl4_fcf_median
+DataFieldDescription: Free cash flow - aggregation on estimations, 50th percentile
+DataField: funds_from_operations_max_guidance
+DataFieldDescription: The maximum guidance value for Funds from operation - annual
+DataField: eps_reported_min_guidance_qtr
+DataFieldDescription: Reported Earnings Per Share - Minimum guidance value
+DataField: selling_general_admin_expense
+DataFieldDescription: Selling, General & Administrative Expense Value
+DataField: anl4_capex_high
+DataFieldDescription: Capital Expenditures - The highest estimation
+DataField: min_total_assets_guidance
+DataFieldDescription: Minimum guidance value for Total Assets
+DataField: anl4_median_capexp
+DataFieldDescription: Capital Expenditures - median of estimations
+DataField: anl4_qf_az_wol_spe
+DataFieldDescription: Earnings per share - The lowest estimation
+DataField: anl4_qf_az_hgih_spe
+DataFieldDescription: Earnings per share - The highest estimation
+DataField: anl4_ads1detailafv110_person
+DataFieldDescription: Broker Id
+DataField: anl4_ebitda_flag
+DataFieldDescription: Earnings before interest, taxes, depreciation and amortization - forecast type (revision/new/...)
+DataField: anl4_bac1detailafv110_item
+DataFieldDescription: Financial item
+DataField: anl4_fcf_mean
+DataFieldDescription: Free Cash Flow - mean of estimations
+DataField: anl4_epsr_value
+DataFieldDescription: GAAP Earnings per share - announced financial value
+DataField: anl4_fsdtlestmtqfv4_item
+DataFieldDescription: Financial item
+DataField: sales_estimate_value
+DataFieldDescription: Sales - Estimated value
+DataField: capital_expenditure_guidance_value
+DataFieldDescription: Capital Expenditures - Total value for the annual guidance
+DataField: max_net_profit_guidance
+DataFieldDescription: The maximum guidance value for net profit on an annual basis.
+DataField: anl4_fsgdncbscv4_minguidance
+DataFieldDescription: Minimum guidance value
+DataField: max_investing_cashflow_guidance
+DataFieldDescription: The maximum guidance value for Cash Flow from Investing.
+DataField: earnings_per_share_min_guidance
+DataFieldDescription: Minimum guidance value for Earnings Per Share on an annual basis.
+DataField: anl4_basicdetaillt_estvalue
+DataFieldDescription: Estimation value
+DataField: anl4_basicdetailqfv110_prevval
+DataFieldDescription: The previous estimation of financial item
+DataField: est_sales
+DataFieldDescription: Sales - mean of estimations
+DataField: pv13_hierarchy_min100_corr21_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_2k_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_region
+DataFieldDescription: Unique code of the region
+DataField: pv13_r2_min2_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_r2_min10_3000_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f2_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min10_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f1_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min100_corr21_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_1l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min51_f3_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_3l_scibr
+DataFieldDescription: grouping fields
+DataField: pv13_revere_term_sector_total
+DataFieldDescription: Number of terminal sectors for the company
+DataField: pv13_revere_comproduct_company
+DataFieldDescription: Company product
+DataField: pv13_rha2_min2_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_min22_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_hierarchy_f4_sector
+DataFieldDescription: grouping fields
+DataField: pv13_ustomergraphrank_page_rank
+DataFieldDescription: the PageRank of customers
+DataField: pv13_revere_zipcode
+DataFieldDescription: Zip code
+DataField: pv13_hierarchys32_sector
+DataFieldDescription: grouping fields
+DataField: rel_ret_all
+DataFieldDescription: Averaged one-day return of the companies whose product overlapped with the instrument
+DataField: pv13_h_min2_focused_sector
+DataFieldDescription: Grouping fields for top 200
+DataField: pv13_hierarchy_min40_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min2_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_rha2_min40_3000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_h_min5_500_sector
+DataFieldDescription: Grouping fields
+DataField: pv13_hierarchy_min2_1000_513_sector
+DataFieldDescription: grouping fields
+DataField: pv13_r2_liquid_min2_sector
+DataFieldDescription: grouping fields
+DataField: implied_volatility_put_270
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 270 days
+DataField: implied_volatility_mean_270
+DataFieldDescription: At-the-money option-implied volatility mean for 270 days
+DataField: historical_volatility_150
+DataFieldDescription: Close-to-close Historical volatility over 150 days
+DataField: historical_volatility_20
+DataFieldDescription: Close-to-close Historical volatility over 20 days
+DataField: implied_volatility_mean_skew_30
+DataFieldDescription: At-the-money option-implied volatility mean skew for 30 days
+DataField: implied_volatility_call_60
+DataFieldDescription: At-the-money option-implied volatility for call Option for 60 days
+DataField: parkinson_volatility_60
+DataFieldDescription: Parkinson model's historical volatility over 60 days
+DataField: implied_volatility_put_30
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 30 days
+DataField: historical_volatility_90
+DataFieldDescription: Close-to-close Historical volatility over 90 days
+DataField: parkinson_volatility_120
+DataFieldDescription: Parkinson model's historical volatility over 120 days
+DataField: implied_volatility_put_120
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 120 days
+DataField: parkinson_volatility_10
+DataFieldDescription: Parkinson model's historical volatility over 2 weeks
+DataField: implied_volatility_put_180
+DataFieldDescription: At-the-money option-implied volatility for put option for 180 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: implied_volatility_call_20
+DataFieldDescription: At-the-money option-implied volatility for call Option for 20 days
+DataField: implied_volatility_mean_skew_180
+DataFieldDescription: At-the-money option-implied volatility mean skew for 180 days
+DataField: implied_volatility_put_10
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 10 days
+DataField: implied_volatility_mean_30
+DataFieldDescription: At-the-money option-implied volatility mean for 30 days
+DataField: implied_volatility_call_360
+DataFieldDescription: At-the-money option-implied volatility for call Option for 360 days
+DataField: implied_volatility_mean_skew_20
+DataFieldDescription: At-the-money option-implied volatility mean skew for 20 days
+DataField: implied_volatility_put_360
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 360 days
+DataField: implied_volatility_call_30
+DataFieldDescription: At-the-money option-implied volatility for call Option for 30 days
+DataField: implied_volatility_call_10
+DataFieldDescription: At-the-money option-implied volatility for call Option for 10 days
+DataField: implied_volatility_put_1080
+DataFieldDescription: At-the-money option-implied volatility for Put Option for 3 years
+DataField: historical_volatility_60
+DataFieldDescription: Close-to-close Historical volatility over 60 days
+DataField: parkinson_volatility_30
+DataFieldDescription: Parkinson model's historical volatility over 30 days
+DataField: implied_volatility_mean_90
+DataFieldDescription: At-the-money option-implied volatility mean for 90 days
+DataField: parkinson_volatility_90
+DataFieldDescription: Parkinson model's historical volatility over 90 days
+DataField: implied_volatility_call_1080
+DataFieldDescription: At-the-money option-implied volatility for call option for 1080 days
+DataField: nws12_afterhsz_01s
+DataFieldDescription: Number of minutes that elapsed before price went down 10 percentage points
+DataField: nws12_prez_01p
+DataFieldDescription: The minimum of L or S above for 10-minute bucket
+DataField: nws12_mainz_open_vol
+DataFieldDescription: Main open volume
+DataField: nws12_mainz_mov_vol
+DataFieldDescription: 30-day moving average session volume
+DataField: nws12_prez_1s
+DataFieldDescription: Number of minutes that elapsed before price went down 1 percentage point
+DataField: nws12_afterhsz_tonlow
+DataFieldDescription: Lowest price reached during the session before the time of the news
+DataField: nws12_prez_57p
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: nws12_afterhsz_allticks
+DataFieldDescription: Total number of ticks for the trading day
+DataField: nws12_afterhsz_opengap
+DataFieldDescription: (DayOpen - PrevClose) / PrevClose.
+DataField: nws12_prez_lowexcstddev
+DataFieldDescription: (TONLast - EODLow)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
+DataField: nws12_mainz_57p
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: nws12_afterhsz_rangeamt
+DataFieldDescription: Session High Price - Session Low Price
+DataField: nws12_prez_newssess
+DataFieldDescription: Index of session in which the news was reported
+DataField: news_mins_7_5_chg
+DataFieldDescription: The minimum of L or S above for 7.5-minute bucket
+DataField: nws12_afterhsz_spylast
+DataFieldDescription: Last Price of the SPY at the time of the news
+DataField: nws12_afterhsz_3s
+DataFieldDescription: Number of minutes that elapsed before price went down 3 percentage points
+DataField: nws12_prez_div_y
+DataFieldDescription: Annual yield
+DataField: nws12_mainz_maxupamt
+DataFieldDescription: The after-the-news high minus the price at the time of the news
+DataField: nws12_prez_57l
+DataFieldDescription: Number of minutes that elapsed before price went up 7.5 percentage points
+DataField: nws12_afterhsz_range
+DataFieldDescription: Session High Price - Session Low Price) / Session Low Price.
+DataField: news_vol_stddev
+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: news_open_vol
+DataFieldDescription: Main open volume
+DataField: nws12_afterhsz_tonlast
+DataFieldDescription: Price at the time of news
+DataField: nws12_prez_4s
+DataFieldDescription: Number of minutes that elapsed before price went down 4 percentage points
+DataField: news_atr14
+DataFieldDescription: 14-day Average True Range
+DataField: news_eod_vwap
+DataFieldDescription: Volume weighted average price between the time of news and the end of the session
+DataField: news_eod_high
+DataFieldDescription: Highest price reached between the time of news and the end of the session
+DataField: nws12_mainz_1p
+DataFieldDescription: The minimum of L or S above for 1-minute bucket
+DataField: news_low_exc_stddev
+DataFieldDescription: (TONLast - EODLow) / StdDev, where StdDev is one standard deviation for the close price for 30 calendar days
+DataField: nws12_afterhsz_close_vol
+DataFieldDescription: Main close volume
+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_legal
+DataFieldDescription: News impact projection of legal news
+DataField: rp_css_society
+DataFieldDescription: Composite sentiment score of society-related news
+DataField: rp_css_dividends
+DataFieldDescription: Composite sentiment score of dividends news
+DataField: rp_nip_product
+DataFieldDescription: News impact projection of product and service-related news
+DataField: rp_nip_insider
+DataFieldDescription: News impact projection of insider trading news
+DataField: rp_nip_ptg
+DataFieldDescription: News impact projection of price target news
+DataField: rp_css_mna
+DataFieldDescription: Composite sentiment score of mergers and acquisitions-related news
+DataField: rp_css_technical
+DataFieldDescription: Composite sentiment score based on technical analysis
+DataField: rp_ess_equity
+DataFieldDescription: Event sentiment score of equity action news
+DataField: rp_ess_ptg
+DataFieldDescription: Event sentiment score of price target news
+DataField: rp_css_partner
+DataFieldDescription: Composite sentiment score of partnership news
+DataField: nws18_acb
+DataFieldDescription: News sentiment specializing in corporate action announcements
+DataField: nws18_ghc_lna
+DataFieldDescription: Change in analyst recommendation
+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: rp_css_ptg
+DataFieldDescription: Composite sentiment score of price target news
+DataField: rp_ess_revenue
+DataFieldDescription: Event sentiment score of revenue news
+DataField: rp_ess_partner
+DataFieldDescription: Event sentiment score of partnership news
+DataField: rp_ess_credit_ratings
+DataFieldDescription: Event sentiment score of credit ratings news
+DataField: rp_css_inverstor
+DataFieldDescription: Composite sentiment score of investor relations news
+DataField: rp_nip_equity
+DataFieldDescription: News impact projection of equity action news
+DataField: nws18_nip
+DataFieldDescription: Degree of impact of the news
+DataField: rp_ess_business
+DataFieldDescription: Event sentiment score of business-related news
+DataField: nws18_event_similarity_days
+DataFieldDescription: Days since a similar event was detected
+DataField: rp_ess_technical
+DataFieldDescription: Event sentiment score based on technical analysis
+DataField: rp_nip_price
+DataFieldDescription: News impact projection of stock price news
+DataField: nws18_ssc
+DataFieldDescription: Sentiment of the news calculated using multiple techniques
+DataField: nws18_event_relevance
+DataFieldDescription: Relevance of the event to the story
+DataField: nws18_ber
+DataFieldDescription: News sentiment specializing in earnings result
+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: fn_accum_oth_income_loss_net_of_tax_a
+DataFieldDescription: Accumulated change in equity from transactions and other events and circumstances from non-owner sources, net of tax effect, at period end. Excludes Net Income (Loss), and accumulated changes in equity from transactions resulting from investments by owners and distributions to owners. Includes foreign currency translation items, certain pension adjustments, unrealized gains and losses on certain investments in debt and equity securities, other than temporary impairment (OTTI) losses related to factors other than credit losses on available-for-sale and held-to-maturity debt securities that an entity does not intend to sell and it is not more likely than not that the entity will be required to sell before recovery of the amortized cost basis, as well as changes in the fair value of derivatives related to the effective portion of a designated cash flow hedge.
+DataField: fn_debt_instrument_face_amount_q
+DataFieldDescription: Debt face amount
+DataField: fn_op_lease_min_pay_due_after_5y_a
+DataFieldDescription: Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of one year due after 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_comp_options_exercises_weighted_avg_a
+DataFieldDescription: Share-Based Compensation, Options Assumed, Weighted Average Exercise Price
+DataField: fnd2_a_fedstyitxrt
+DataFieldDescription: Effective Income Tax Rate Reconciliation - Federal Statutory Income Tax Rate %
+DataField: fn_allowance_for_doubtful_accounts_receivable_a
+DataFieldDescription: For an unclassified balance sheet, a valuation allowance for receivables due a company that are expected to be uncollectible.
+DataField: fn_comp_non_opt_forfeited_q
+DataFieldDescription: The number of equity-based payment instruments, excluding stock (or unit) options, that were forfeited during the reporting period.
+DataField: fn_assets_fair_val_l3_q
+DataFieldDescription: Asset Fair Value, Recurring, Level 3
+DataField: fnd2_a_ltrmdmrepopliny5
+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 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_allowance_for_doubtful_accounts_receivable_q
+DataFieldDescription: For an unclassified balance sheet, a valuation allowance for receivables due a company that are expected to be uncollectible.
+DataField: fnd2_a_restructuringcharges
+DataFieldDescription: Amount of expenses associated with exit or disposal activities pursuant to an authorized plan. Excludes expenses related to a discontinued operation or an asset retirement obligation.
+DataField: fn_liab_fair_val_l3_a
+DataFieldDescription: Liabilities Fair Value, Recurring, Level 3
+DataField: fn_profit_loss_q
+DataFieldDescription: The consolidated profit or loss for the period, net of income taxes, including the portion attributable to the noncontrolling interest.
+DataField: fnd2_dbplanchgbnfolintcst
+DataFieldDescription: Defined Benefit Plan Change In Benefit Obligation Interest Cost
+DataField: fn_oth_comp_grants_weighted_avg_grant_date_fair_value_q
+DataFieldDescription: Quarterly Share-Based Compensation Equity Instruments Other Than Options Nonvested Weighted Average Grant Date Fair Value
+DataField: fn_payments_to_acquire_businesses_net_of_cash_acquired_a
+DataFieldDescription: The cash outflow associated with the acquisition of a business, net of the cash acquired from the purchase.
+DataField: fn_comp_options_grants_fair_value_a
+DataFieldDescription: Annual Share-Based Compensation Arrangement by Share-Based Payment Award Options Grants in Period Weighted Average Grant Date Fair Value
+DataField: fnd2_a_gsles1xtinguishmentofd
+DataFieldDescription: Difference between the fair value of payments made and the carrying amount of debt which is extinguished prior to maturity.
+DataField: fn_avg_diluted_sharesout_adj_a
+DataFieldDescription: The sum of dilutive potential common shares or units used in the calculation of the diluted per-share or per-unit computation.
+DataField: fnd2_dbplanbnfol
+DataFieldDescription: 1) For defined benefit pension plans, the benefit obligation is the projected benefit obligation, which is the actuarial present value as of a date of all benefits attributed by the pension benefit formula to employee service rendered prior to that date. 2) For other postretirement defined benefit plans, the benefit obligation is the accumulated postretirement benefit obligation, which is the actuarial present value of benefits attributed to employee service rendered to a particular date.
+DataField: fn_derivative_fair_value_of_derivative_asset_q
+DataFieldDescription: Fair value, before effects of master netting arrangements, of a financial asset or other contract with one or more underlyings, notional amount or payment provision or both, and the contract can be net settled by means outside the contract or delivery of an asset. Includes assets elected not to be offset. Excludes assets not subject to a master netting arrangement.
+DataField: fn_proceeds_from_lt_debt_q
+DataFieldDescription: Proceeds From Issuance Of Debt, Long Term
+DataField: fnd2_a_alsbcmpexrsus
+DataFieldDescription: Allocated Share-Based Compensation Expense, Restricted Stock Units
+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: fnd2_a_sbcpnargmtwfsptepddvdrt
+DataFieldDescription: The estimated dividend rate (a percentage of the share price) to be paid (expected dividends) to holders of the underlying shares over the option's term.
+DataField: fn_accrued_liab_a
+DataFieldDescription: Carrying value as of the balance sheet date of obligations incurred and payable, pertaining to costs that are statutory in nature, are incurred on contractual obligations, or accumulate over time and for which invoices have not yet been received or will not be rendered.
+DataField: fn_debt_instrument_face_amount_a
+DataFieldDescription: Debt face amount
+DataField: fnd2_a_ltrmdmrepoplinyfour
+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 4th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.
+DataField: fnd2_a_dbplannpicbnfcst
+DataFieldDescription: The total amount of net periodic benefit cost for defined benefit plans for the period. Periodic benefit costs include the following components: service cost, interest cost, expected return on plan assets, gain (loss), prior service cost or credit, transition asset or obligation, and gain (loss) due to settlements or curtailments.
+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 =======================================
+

prompt_set_ref/alpha_prompt001.txt

@@ -0,0 +1,53 @@
+你作为WorldQuant因子挖掘专家，请基于以下多维度框架生成alpha因子。请严格遵循WebSim语法规范：
+
+【重要平台约束】
+平台：WorldQuant WebSim
+可用函数：ts_*(时间序列), group_*(横截面), 基础数学运算
+可用字段：open, high, low, close, volume, vwap, sector, industry, country, market_cap
+禁用函数：pandas, numpy, 自定义Python函数，机器学习库
+输出格式：严格一行WebSim表达式，使用WQ平台函数
+表达式要求：必须能直接在WebSim中运行
+
+我正在WorldQuant研究行业轮动策略，请设计行业选择因子：
+
+**需要创新因子-因子设计维度**：(也可以你自己设计维度)：
+请从以下五个核心维度构思因子，确保每个因子都有独特的市场逻辑和经济学解释：
+趋势动能形态因子：超越简单的价格动量。关注动量的加速度、收敛/发散以及平滑度（例如，使用动量的一阶/二阶导数、趋势线的拟合优度）。逻辑：识别趋势是健康加速、乏力钝化还是即将衰竭。
+行业质量与市场环境适配度因子：结合行业自身的财务质量（如盈利稳定性、杠杆率）与宏观市场状态（如波动率环境、利率周期）。逻辑：找到在特定宏观环境下（如“避险”或“增长”期）基本面更具韧性的行业。
+行业内部分化与领导力因子：度量行业内部个股表现的离散度与龙头股的带动效应（例如，行业前三大市值公司收益率与行业平均收益率的差异及相关性）。逻辑：捕捉“龙头领涨”的聚集效应或“普涨轮动”的扩散信号。
+超买超卖与情绪反转因子：基于多维度标准化偏移（如价格相对于自身历史布林带的位置、换手率分位数、分析师情绪极端值）。逻辑：综合估值、交易拥挤度和情绪，识别物极必反的均值回归契机。
+行业间关联与动量溢出因子：构建行业关联网络（基于产业链、基本面相关性或资金流），分析核心行业的动量如何向其关联行业传导的强度与速度。逻辑：利用行业间的传导滞后或相关性结构变化，进行前瞻性布局。
+
+请以以上 5 个维度, 分别设计 20 个因子, 共 100 个因子
+
+**策略背景**：
+投资目标：构建一个基于多因子的行业轮动模型，旨在穿越不同的经济周期（扩张、衰退、复苏、滞胀），持续选择未来一期（月度）预期表现强势的行业。
+标的范围：使用清晰、稳定、覆盖面广的行业分类体系（例如，GICS行业板块或细分行业）。请明确你选取的分类级别。
+组合频率：月度调仓。所有因子计算需使用截至调仓日前可获取的数据，避免未来函数。
+风险控制：
+-行业集中度限制：单行业配置不超过20%。
+-考虑行业市值中性化或流动性筛选，确保实操性。
+-因子需考虑不同市场周期（牛、熊、震荡）下的表现稳定性。
+
+**具体要求**：
+对于你设计的每一个因子，请务必按以下结构化格式进行阐述：
+1,因子名称：简洁明了。
+2,核心逻辑：阐述该因子捕捉的具体市场异象或经济机制（为什么它应该能预测行业表现）。
+3,具体计算方法：分步描述计算步骤、所用数据（价格、成交量、基本面数据等）与关键参数（给出参数选择的逻辑，例如回看期为什么是12个月而非6个月）。
+4,经济周期适应性分析：
+ - 在何种周期下预计最有效？（例如：趋势加速度因子在“经济复苏期”可能最有效，因为主导行业趋势明确且强化）
+ - 在何种周期下可能失效或需要谨慎？（例如：反转因子在“趋势强烈的泡沫期”可能持续失效）
+5,与已有经典因子的区别：明确指出你的因子与简单动量、波动率、估值等传统因子的不同之处与改进点。
+6,可能的实施挑战与注意事项：如数据可获得性、对参数敏感度、极端市场环境下的行为等。
+
+
+**输出格式**(一行一个表达式, 只要表达式本身, 不要解释, 也不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+
+请提供具体的WQ表达式。
+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。

prompt_set_ref/基础提示词.txt

@@ -0,0 +1,40 @@
+你作为WorldQuant因子挖掘专家，请基于以下多维度框架生成 20 个原创alpha因子。请严格遵循WebSim语法规范：
+
+【重要平台约束】
+平台：WorldQuant WebSim
+可用函数：ts_*(时间序列), group_*(横截面), 基础数学运算
+可用字段：open, high, low, close, volume, vwap, sector, industry, country, market_cap
+禁用函数：pandas, numpy, 自定义Python函数，机器学习库
+输出格式：严格一行WebSim表达式，使用WQ平台函数
+表达式要求：必须能直接在WebSim中运行
+
+我正在WorldQuant研究行业轮动策略，请设计行业选择因子：
+
+**需要创新因子-因子设计维度**：(也可以你自己设计维度)：
+行业趋势加速度因子 - 不仅看动量，更看动量的变化率，捕捉趋势的强化或弱化。
+行业质量与防御性因子 - 结合波动率与市场相关性，识别在不同市场环境（风险偏好）下表现稳健的行业。
+行业资金集中度因子 - 分析行业内龙头股与整体行业表现的差异，捕捉“强者恒强”或“内部轮动”的信号。
+行业超买超卖反转因子 - 基于布林带或标准化位置，捕捉极端估值或情绪后的均值回归机会。
+行业间动量扩散因子 - 分析一个行业的动量是否会向相关行业（如同产业链上下游）扩散。
+
+**策略背景**：
+- 目标：在不同经济周期中选择强势行业
+- 数据：使用sector, industry分类字段(或者你自己随意选择)
+- 频率：月度调仓
+- 风险：控制行业集中度
+
+**具体要求**：
+- 必须使用group函数处理行业分组
+- 每个因子包含清晰的行业轮动逻辑
+- 考虑行业特性的持久性
+- 提供经济周期适应的解释
+
+**输出格式**(一行一个, 不要输出多余的东西)：
+表达式
+表达式
+表达式
+...
+表达式
+
+请提供具体的WQ表达式。
+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。

siliconflow_detail.py

@@ -2,12 +2,8 @@
 import json
 import httpx
 
-with open('config.json', 'r') as f:
-    config = json.load(f)
-    siliconflow = config['siliconflow']
-    token = siliconflow['api_keys']
+headers = {"Authorization": f"Bearer sk-pvdiisdowmuwkrpnxsrlhxaovicqibmlljwrwwvbbdjaitdl"}
 
-headers = {"Authorization": f"Bearer {token}"}
 
 def get_user_info():
     # 获取用户信息
@@ -30,8 +26,15 @@ def get_models_info():
 
     data = response.json()['data']
 
+    model_list = []
+
     for d in data:
-        print(d)
+        if d.get('object') and d.get('object') == 'model':
+            model_name = d.get('id')
+            # print(model_name)
+            model_list.append(model_name)
+
+    print(model_list)
 
 # get_models_info()
-# get_user_info()
+get_user_info()