1、策略更新

2、新增qmt

main/factor/industry_factors.py

@@ -0,0 +1,90 @@
+"""
+行业/横截面因子模块
+包含基于日期截面的行业/横截面因子实现
+"""
+
+import numpy as np
+import polars as pl
+from main.factor.operator_framework import DateWiseFactor
+
+
+class IndustryMomentumFactor(DateWiseFactor):
+    """行业动量因子"""
+    
+    def __init__(self, factor_name: str):
+        super().__init__(
+            name=f"industry_momentum",
+            parameters={"factor_name": factor_name},
+            required_factor_ids=[factor_name, "cat_l2_code"]
+        )
+        self.factor_name = factor_name
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算行业动量基准
+        # 这里需要先计算每个行业的平均值，然后与个股比较
+        if self.factor_name in group_df.columns and "cat_l2_code" in group_df.columns:
+            # 按行业计算平均值
+            industry_means = group_df.group_by("cat_l2_code").agg([
+                pl.col(self.factor_name).mean().alias("industry_mean")
+            ])
+            
+            # 将行业均值合并回原数据
+            result_df = group_df.join(industry_means, on="cat_l2_code", how="left")
+            
+            # 计算与行业均值的偏差
+            deviation = result_df[self.factor_name] - result_df["industry_mean"]
+            return deviation.alias(self.factor_id)
+        else:
+            # 如果缺少必要列，返回全NaN
+            return pl.Series([None] * len(group_df)).alias(self.factor_id)
+
+
+class MarketBreadthFactor(DateWiseFactor):
+    """市场宽度因子"""
+    
+    def __init__(self):
+        super().__init__(
+            name="market_breadth",
+            parameters={},
+            required_factor_ids=["pct_chg"]
+        )
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算市场宽度：上涨股票数 / 总股票数
+        pct_chg = group_df["pct_chg"]
+        positive_count = (pct_chg > 0).sum()
+        total_count = len(group_df)
+        
+        # 避免除零
+        breadth = positive_count / (total_count + 1e-8)
+        return pl.Series([breadth] * len(group_df)).alias(self.factor_id)
+
+
+class SectorRotationFactor(DateWiseFactor):
+    """板块轮动因子"""
+    
+    def __init__(self, sector_factor: str):
+        super().__init__(
+            name=f"sector_rotation_{sector_factor}",
+            parameters={"sector_factor": sector_factor},
+            required_factor_ids=[sector_factor, "cat_l2_code"]
+        )
+        self.sector_factor = sector_factor
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算板块轮动因子
+        if self.sector_factor in group_df.columns and "cat_l2_code" in group_df.columns:
+            # 计算每个板块的因子均值
+            sector_means = group_df.group_by("cat_l2_code").agg([
+                pl.col(self.sector_factor).mean().alias("sector_mean")
+            ])
+            
+            # 将板块均值合并回原数据
+            result_df = group_df.join(sector_means, on="cat_l2_code", how="left")
+            
+            # 计算个股与板块均值的偏差
+            deviation = result_df[self.sector_factor] - result_df["sector_mean"]
+            return deviation.alias(self.factor_id)
+        else:
+            # 如果缺少必要列，返回全NaN
+            return pl.Series([None] * len(group_df)).alias(self.factor_id)