1、策略更新

2、新增qmt

main/factor/special_factors.py

@@ -0,0 +1,134 @@
+"""
+特殊因子模块
+包含基于股票截面的特殊因子实现
+"""
+
+import numpy as np
+import polars as pl
+from main.factor.operator_framework import StockWiseFactor
+
+
+class LimitFactor(StockWiseFactor):
+    """涨跌停因子"""
+    
+    def __init__(self):
+        super().__init__(
+            name="limit",
+            parameters={},
+            required_factor_ids=["close", "up_limit", "down_limit"]
+        )
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算是否涨停或跌停
+        close = group_df["close"]
+        up_limit = group_df["up_limit"]
+        down_limit = group_df["down_limit"]
+        
+        # 是否涨停
+        is_up_limit = (close == up_limit).cast(pl.Int32)
+        # 是否跌停
+        is_down_limit = (close == down_limit).cast(pl.Int32)
+        
+        # 合并为一个因子
+        limit_factor = is_up_limit - is_down_limit
+        return limit_factor.alias(self.factor_id)
+
+
+class VolumeRatioFactor(StockWiseFactor):
+    """量比因子"""
+    
+    def __init__(self):
+        super().__init__(
+            name="volume_ratio",
+            parameters={},
+            required_factor_ids=["vol"]
+        )
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算量比：当日成交量 / 5日平均成交量
+        vol = group_df["vol"]
+        avg_vol_5d = vol.rolling_mean(5)
+        # 避免除零
+        volume_ratio = vol / (avg_vol_5d + 1e-8)
+        return volume_ratio.alias(self.factor_id)
+
+
+class BBI_RATIO_FACTOR(StockWiseFactor):
+    """BBI比率因子"""
+    
+    def __init__(self):
+        super().__init__(
+            name="bbi_ratio",
+            parameters={},
+            required_factor_ids=["close"]
+        )
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算BBI比率
+        close = group_df["close"]
+        
+        # 计算不同周期的SMA
+        sma3 = close.rolling_mean(3)
+        sma6 = close.rolling_mean(6)
+        sma12 = close.rolling_mean(12)
+        sma24 = close.rolling_mean(24)
+        
+        # 计算BBI
+        bbi = (sma3 + sma6 + sma12 + sma24) / 4
+        
+        # 计算BBI比率
+        bbi_ratio = bbi / (close + 1e-8)  # 避免除零
+        
+        return bbi_ratio.alias(self.factor_id)
+
+
+class VolatilitySlopeFactor(StockWiseFactor):
+    """波动率斜率因子"""
+    
+    def __init__(self, window_vol: int = 20, window_slope: int = 5):
+        super().__init__(
+            name="volatility_slope",
+            parameters={"window_vol": window_vol, "window_slope": window_slope},
+            required_factor_ids=["pct_chg"]
+        )
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        window_vol = self.parameters["window_vol"]
+        window_slope = self.parameters["window_slope"]
+        
+        # 计算滚动标准差
+        volatility = group_df["pct_chg"].rolling_std(window_vol)
+        
+        # 计算斜率
+        # 这里简化处理，直接计算最后一个窗口的斜率
+        # 实际应用中可能需要更复杂的线性回归计算
+        volatility_slope = volatility.diff().alias(self.factor_id)
+        
+        return volatility_slope
+
+
+class PriceVolumeTrendFactor(StockWiseFactor):
+    """价格量能趋势因子"""
+    
+    def __init__(self):
+        super().__init__(
+            name="price_volume_trend",
+            parameters={},
+            required_factor_ids=["close", "vol"]
+        )
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算价格量能趋势
+        close = group_df["close"]
+        vol = group_df["vol"]
+        
+        # 计算价格变化
+        price_change = close.diff()
+        
+        # 计算成交量变化
+        vol_change = vol.diff()
+        
+        # 计算趋势因子
+        trend_factor = price_change * vol_change
+        
+        return trend_factor.alias(self.factor_id)