cfdaily
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
"""
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动态加权 - 根据滚动IC自动调整因子权重
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IC信息系数越高，因子权重越大
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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
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from scipy.stats import spearmanr
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class DynamicICWeightAdjuster:
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"""
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动态IC权重调整器
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每月更新因子IC，根据IC均值调整权重
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"""
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def __init__(
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self,
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factor_names: List[str],
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window_size: int = 12, # 滚动窗口月数
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min_ic: float = -0.02,
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base_weight: float = 0.02 # 基础权重，保证每个因子都有暴露
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):
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self.factor_names = factor_names
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self.window_size = window_size
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self.min_ic = min_ic
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self.base_weight = base_weight
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# 历史IC记录
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self.ic_history: Dict[str, List[float]] = {n: [] for n in factor_names}
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def update_monthly_ic(
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self,
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factor_df: pd.DataFrame,
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forward_returns: pd.Series
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) -> Dict[str, float]:
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"""
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更新月度IC
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参数:
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factor_df: 当期因子得分，每列一个因子
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forward_returns: 下期收益率（要预测的目标）
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"""
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current_ic = {}
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# 对齐数据
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combined = pd.concat([factor_df, forward_returns], axis=1).dropna()
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for name in self.factor_names:
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if name not in combined.columns:
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continue
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# 计算Spearman秩相关系数IC
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ic, _ = spearmanr(combined[name], combined[forward_returns.name])
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current_ic[name] = ic
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self.ic_history[name].append(ic)
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# 保持窗口大小
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if len(self.ic_history[name]) > self.window_size:
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self.ic_history[name].pop(0)
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return current_ic
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def calculate_weights(self) -> Dict[str, float]:
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"""根据滚动IC计算新权重"""
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# 计算每个因子平均IC
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avg_ic = {}
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for name in self.factor_names:
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ics = self.ic_history[name]
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if len(ics) > 0:
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avg_ic[name] = np.mean(ics)
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else:
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avg_ic[name] = 0
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# IC转权重: IC越大权重越大，IC小于最小值只保留基础权重
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weights = {}
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for name in self.factor_names:
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ic = avg_ic[name]
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if ic < self.min_ic:
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weights[name] = self.base_weight
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else:
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weights[name] = self.base_weight + max(0, ic)
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# 归一化总和为1
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total = sum(weights.values())
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if total > 0:
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weights = {k: v / total for k, v in weights.items()}
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else:
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# 全不好就等权重
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n = len(self.factor_names)
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weights = {k: 1.0 / n for k in self.factor_names}
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return weights
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def get_avg_ic(self) -> Dict[str, float]:
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"""获取当前滚动平均IC"""
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avg = {}
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for name, ics in self.ic_history.items():
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if len(ics) > 0:
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avg[name] = np.mean(ics)
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else:
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avg[name] = 0
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return avg
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def get_ic_history(self) -> Dict[str, List[float]]:
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return self.ic_history.copy()
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