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