第一版流程

combination.py

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+"""
+因子组合模块：多因子模型
+"""
+import numpy as np
+import pandas as pd
+from typing import Dict, List, Optional
+from statsmodels.regression.linear_model import OLS
+
+
+def risk_parity_weights(factors: pd.DataFrame, window: int = 30) -> pd.Series:
+    """
+    风险平价加权：使每个因子的风险贡献相等
+    
+    Parameters:
+    -----------
+    factors : DataFrame
+        因子数据框
+    window : int
+        计算波动率的滚动窗口
+    """
+    # 计算各因子的滚动波动率
+    volatilities = factors.rolling(window).std().iloc[-1]
+    
+    # 风险平价权重：w_i = 1/σ_i / Σ(1/σ_j)
+    inv_vol = 1.0 / (volatilities + 1e-8)
+    weights = inv_vol / inv_vol.sum()
+    
+    return weights
+
+
+def regression_weights(
+    factors: pd.DataFrame,
+    forward_return: pd.Series,
+    window: Optional[int] = None
+) -> pd.Series:
+    """
+    回归系数加权：用回归系数作为权重
+    
+    Parameters:
+    -----------
+    factors : DataFrame
+        因子数据框
+    forward_return : Series
+        未来收益率
+    window : int, optional
+        滚动窗口，None表示使用全样本
+    """
+    # 对齐数据
+    data = pd.concat([factors, forward_return], axis=1).dropna()
+    
+    if window is not None and len(data) > window:
+        data = data.iloc[-window:]
+    
+    if len(data) < 30:
+        # 如果数据不足，返回等权重
+        return pd.Series(1.0 / len(factors.columns), index=factors.columns)
+    
+    y = data.iloc[:, -1].values
+    X = data.iloc[:, :-1].values
+    
+    try:
+        model = OLS(y, X).fit(cov_type='HAC', cov_kwds={'maxlags': 6})
+        betas = pd.Series(model.params, index=factors.columns)
+        
+        # 归一化权重（取绝对值后归一化）
+        weights = np.abs(betas) / (np.abs(betas).sum() + 1e-8)
+        return weights
+    except Exception as e:
+        print(f"回归权重计算出错: {e}")
+        # 返回等权重
+        return pd.Series(1.0 / len(factors.columns), index=factors.columns)
+
+
+def equal_weights(factors: pd.DataFrame) -> pd.Series:
+    """等权重"""
+    n = len(factors.columns)
+    return pd.Series(1.0 / n, index=factors.columns)
+
+
+class MultiFactorModel:
+    """多因子模型"""
+    
+    def __init__(self, weight_method: str = 'risk_parity'):
+        """
+        Parameters:
+        -----------
+        weight_method : str
+            权重方法：'risk_parity', 'regression', 'equal'
+        """
+        self.weight_method = weight_method
+        self.weights: Optional[pd.Series] = None
+        self.factor_names: List[str] = []
+    
+    def fit(
+        self,
+        factors: pd.DataFrame,
+        forward_return: Optional[pd.Series] = None,
+        window: Optional[int] = None
+    ):
+        """
+        拟合多因子模型
+        
+        Parameters:
+        -----------
+        factors : DataFrame
+            因子数据框
+        forward_return : Series, optional
+            未来收益率（回归权重需要）
+        window : int, optional
+            滚动窗口
+        """
+        self.factor_names = list(factors.columns)
+        
+        if self.weight_method == 'risk_parity':
+            self.weights = risk_parity_weights(factors, window=window or 30)
+        elif self.weight_method == 'regression':
+            if forward_return is None:
+                raise ValueError("回归权重需要提供forward_return")
+            self.weights = regression_weights(factors, forward_return, window=window)
+        elif self.weight_method == 'equal':
+            self.weights = equal_weights(factors)
+        else:
+            raise ValueError(f"未知的权重方法: {self.weight_method}")
+    
+    def predict(self, factors: pd.DataFrame) -> pd.Series:
+        """
+        计算多因子综合得分
+        
+        Parameters:
+        -----------
+        factors : DataFrame
+            因子数据框
+        
+        Returns:
+        --------
+        Series: 综合得分
+        """
+        if self.weights is None:
+            raise ValueError("模型尚未拟合，请先调用fit()")
+        
+        # 确保因子顺序一致
+        factors_aligned = factors[self.factor_names]
+        
+        # 计算加权得分
+        score = (factors_aligned * self.weights).sum(axis=1)
+        
+        return score
+    
+    def get_weights(self) -> pd.Series:
+        """获取因子权重"""
+        return self.weights.copy() if self.weights is not None else pd.Series()
+