"""
因子合成工具
将多个因子按权重合成最终得分
"""
import pandas as pd
import numpy as np
from typing import Dict, List, Optional
from factors.base_factor import BaseFactor


class FactorCombiner:
    """因子合成器 - 把多个因子按权重合成最终得分"""
    
    def __init__(self, factors: Dict[str, BaseFactor], weights: Optional[Dict[str, float]] = None):
        """
        参数:
            factors: 因子字典 {因子名称: 因子实例}
            weights: 因子权重 {因子名称: 权重}, 如果None，等权重
        """
        self.factors = factors
        
        # 如果没给权重，用等权重
        if weights is None:
            total = len(factors)
            self.weights = {name: 1.0 / total for name in factors}
        else:
            # 标准化权重，让总和为1
            total = sum(weights.values())
            self.weights = {k: v / total for k, v in weights.items()}
            
    def combine(self, data: pd.DataFrame) -> pd.Series:
        """
        合成因子得分
        参数:
            data: 原始行情财务数据DataFrame
        返回:
            最终合并得分，index和data一致
        """
        combined = None
        
        for name, factor in self.factors.items():
            weight = self.weights[name]
            
            # 计算因子值（已经包含了标准化和rank）
            factor_score = factor.process(data)
            
            # 加权
            weighted = factor_score * weight
            
            if combined is None:
                combined = weighted
            else:
                # 对齐索引相加
                combined = combined.add(weighted, fill_value=0)
        
        return combined
    
    def get_factors(self) -> List[str]:
        """获取所有因子名称"""
        return list(self.factors.keys())
    
    def update_weights(self, new_weights: Dict[str, float]) -> None:
        """更新因子权重（用于动态加权）"""
        # 标准化权重总和为1
        total = sum(new_weights.values())
        self.weights = {k: v / total for k, v in new_weights.items()}
    
    def get_weights(self) -> Dict[str, float]:
        """获取当前权重"""
        return self.weights.copy()