1、策略更新

2、新增qmt

main/factor/momentum_factors.py

@@ -0,0 +1,174 @@
+"""
+动量因子模块
+包含基于股票截面和日期截面的动量因子实现
+"""
+
+import numpy as np
+import polars as pl
+from main.factor.operator_framework import StockWiseFactor, DateWiseFactor
+
+
+# -------------------- 股票截面因子：基于时间序列的动量因子 --------------------
+class ReturnFactor(StockWiseFactor):
+    """N日收益率因子"""
+    
+    def __init__(self, period: int = 20):
+        super().__init__(
+            name="return",
+            parameters={"period": period},
+            required_factor_ids=["close"]
+        )
+        self.period = period
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算N日收益率（时间序列操作）
+        return group_df["close"].pct_change(self.period).alias(self.factor_id)
+
+
+class VolatilityFactor(StockWiseFactor):
+    """N日波动率因子"""
+    
+    def __init__(self, period: int = 20):
+        super().__init__(
+            name="volatility",
+            parameters={"period": period},
+            required_factor_ids=["pct_chg"]
+        )
+        self.period = period
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算N日波动率（时间序列操作）
+        return group_df["pct_chg"].rolling_std(self.period).alias(self.factor_id)
+
+
+class MomentumFactor(StockWiseFactor):
+    """动量因子：过去N日累计收益率"""
+    
+    def __init__(self, period: int = 20):
+        super().__init__(
+            name="momentum",
+            parameters={"period": period},
+            required_factor_ids=["pct_chg"]
+        )
+        self.period = period
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 计算N日累计动量（时间序列操作）
+        return group_df["pct_chg"].rolling_sum(self.period).alias(self.factor_id)
+
+class MomentumAcceleration(StockWiseFactor):
+    """
+    动量加速因子：
+    (短期波动率调整后动量 - 长期波动率调整后动量)
+    用于捕捉趋势正在形成或加强的股票
+    """
+    
+    def __init__(self, short_period: int = 20, long_period: int = 60):
+        super().__init__(
+            name="momentum_acceleration",
+            parameters={"short_period": short_period, "long_period": long_period},
+            required_factor_ids=["pct_chg"]
+        )
+        self.short_period = short_period
+        self.long_period = long_period
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        epsilon = 1e-9
+        
+        # 计算短期波动率调整后动量
+        short_momentum = group_df["pct_chg"].rolling_sum(self.short_period)
+        short_vol = group_df["pct_chg"].rolling_std(self.short_period)
+        short_adj_momentum = short_momentum / (short_vol + epsilon)
+        
+        # 计算长期波动率调整后动量
+        long_momentum = group_df["pct_chg"].rolling_sum(self.long_period)
+        long_vol = group_df["pct_chg"].rolling_std(self.long_period)
+        long_adj_momentum = long_momentum / (long_vol + epsilon)
+        
+        # 计算加速因子
+        acceleration = (short_adj_momentum - long_adj_momentum).alias(self.factor_id)
+        
+        return acceleration
+    
+
+class TrendEfficiency(StockWiseFactor):
+    """
+    趋势效率因子：
+    过去N日价格净变化 / 过去N日每日价格变化的绝对值之和
+    衡量趋势的信噪比，值越接近1，趋势越清晰、噪声越小
+    """
+    
+    def __init__(self, period: int = 20):
+        super().__init__(
+            name="trend_efficiency",
+            parameters={"period": period},
+            # 此因子需要收盘价来计算
+            required_factor_ids=["close"] 
+        )
+        self.period = period
+
+    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
+        # 1. 计算N日内的净价格变动（信号）
+        # 使用 diff(n) 计算当前价格与n天前价格的差值
+        net_change = group_df["close"].diff(self.period).abs()
+        
+        # 2. 计算N日内每日价格变动的绝对值之和（总路径/噪声）
+        # 先计算每日变动 diff(1)，取绝对值，再滚动求和
+        total_path = group_df["close"].diff(1).abs().rolling_sum(self.period)
+        
+        # 3. 计算效率比率
+        epsilon = 1e-9
+        efficiency_ratio = (net_change / (total_path + epsilon)).alias(self.factor_id)
+        
+        return efficiency_ratio
+
+# -------------------- 统一计算函数 --------------------
+def calculate_momentum_factors(df: pl.DataFrame) -> pl.DataFrame:
+    """
+    统一计算动量因子的函数
+    
+    Parameters:
+    df (pl.DataFrame): 输入的股票数据表，必须包含以下列：
+                      ts_code, trade_date, close, pct_chg, high, low, vol
+    
+    Returns:
+    pl.DataFrame: 包含所有动量因子的DataFrame
+    """
+    # 初始化结果DataFrame
+    result_df = df.clone()
+    
+    # 定义要计算的因子列表
+    # 先计算股票截面因子（时间序列因子）
+    stock_operators = [
+        ReturnFactor(5),
+        ReturnFactor(20),
+        VolatilityFactor(10),
+        VolatilityFactor(30),
+        MomentumFactor(10),
+        MomentumFactor(30),
+        RSI_Factor(14)
+    ]
+    
+    # 依次应用股票截面因子算子
+    for operator in stock_operators:
+        try:
+            result_df = operator.apply(result_df)
+        except Exception as e:
+            print(f"计算股票截面因子 {operator.factor_id} 时出错: {e}")
+    
+    # 再计算日期截面因子（横截面排序因子）
+    date_operators = [
+        CrossSectionalRanking("return_5d"),
+        CrossSectionalRanking("return_20d"),
+        CrossSectionalRanking("volatility_10d"),
+        CrossSectionalRanking("momentum_10d")
+    ]
+    
+    # 依次应用日期截面因子算子
+    for operator in date_operators:
+        try:
+            result_df = operator.apply(result_df)
+        except Exception as e:
+            print(f"计算日期截面因子 {operator.factor_id} 时出错: {e}")
+    
+    return result_df