233 lines
7.6 KiB
Python
233 lines
7.6 KiB
Python
"""Factor performance evaluation utilities."""
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from datetime import date, timedelta
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from typing import Dict, List, Optional, Sequence, Tuple
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import numpy as np
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from scipy import stats
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from app.features.factors import (
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DEFAULT_FACTORS,
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FactorResult,
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FactorSpec,
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compute_factor_range
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)
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from app.utils.data_access import DataBroker
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from app.utils.logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "factor_evaluation"}
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class FactorPerformance:
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"""因子表现评估结果。"""
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def __init__(self, factor_name: str) -> None:
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self.factor_name = factor_name
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self.ic_series: List[float] = []
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self.rank_ic_series: List[float] = []
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self.return_spreads: List[float] = []
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self.sharpe_ratio: Optional[float] = None
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self.turnover_rate: Optional[float] = None
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@property
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def ic_mean(self) -> float:
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"""平均IC。"""
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return np.mean(self.ic_series) if self.ic_series else 0.0
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@property
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def ic_std(self) -> float:
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"""IC标准差。"""
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return np.std(self.ic_series) if self.ic_series else 0.0
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@property
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def ic_ir(self) -> float:
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"""信息比率。"""
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return self.ic_mean / self.ic_std if self.ic_std > 0 else 0.0
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@property
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def rank_ic_mean(self) -> float:
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"""平均RankIC。"""
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return np.mean(self.rank_ic_series) if self.rank_ic_series else 0.0
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def to_dict(self) -> Dict[str, float]:
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"""转换为字典格式。"""
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return {
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"ic_mean": self.ic_mean,
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"ic_std": self.ic_std,
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"ic_ir": self.ic_ir,
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"rank_ic_mean": self.rank_ic_mean,
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"sharpe_ratio": self.sharpe_ratio or 0.0,
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"turnover_rate": self.turnover_rate or 0.0
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}
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def evaluate_factor(
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factor_name: str,
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start_date: date,
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end_date: date,
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universe: Optional[List[str]] = None,
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) -> FactorPerformance:
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"""评估单个因子的预测能力。
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Args:
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factor_name: 因子名称
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start_date: 起始日期
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end_date: 结束日期
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universe: 可选的股票池
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Returns:
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因子表现评估结果
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"""
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performance = FactorPerformance(factor_name)
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# 导入进度状态模块
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from app.ui.progress_state import factor_progress
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# 开始因子计算进度（在异步线程中不直接访问factor_progress）
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# factor_progress.start_calculation(
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# total_securities=len(universe) if universe else 0,
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# message=f"开始评估因子 {factor_name}"
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# )
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try:
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# 计算因子值
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factor_results = compute_factor_range(
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start_date,
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end_date,
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factors=[FactorSpec(factor_name, 0)],
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ts_codes=universe
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)
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# 因子计算完成（在异步线程中不直接访问factor_progress）
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# factor_progress.complete_calculation(
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# message=f"因子 {factor_name} 评估完成"
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# )
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except Exception as e:
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# 因子计算失败（在异步线程中不直接访问factor_progress）
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# factor_progress.complete_calculation(
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# message=f"因子 {factor_name} 评估失败: {str(e)}",
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# success=False
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# )
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raise
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# 按日期分组
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date_groups: Dict[date, List[FactorResult]] = {}
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for result in factor_results:
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if result.trade_date not in date_groups:
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date_groups[result.trade_date] = []
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date_groups[result.trade_date].append(result)
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# 计算每日IC值和RankIC值
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broker = DataBroker()
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for curr_date, results in sorted(date_groups.items()):
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next_date = curr_date + timedelta(days=1)
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# 获取因子值和次日收益率
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factor_values = []
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next_returns = []
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for result in results:
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factor_val = result.values.get(factor_name)
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if factor_val is None:
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continue
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# 获取次日收益率
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next_close = broker.fetch_latest(
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result.ts_code,
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next_date.strftime("%Y%m%d"),
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["daily.close"]
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).get("daily.close")
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curr_close = broker.fetch_latest(
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result.ts_code,
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curr_date.strftime("%Y%m%d"),
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["daily.close"]
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).get("daily.close")
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if next_close and curr_close and curr_close > 0:
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ret = (next_close - curr_close) / curr_close
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factor_values.append(factor_val)
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next_returns.append(ret)
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if len(factor_values) >= 20: # 需要足够多的样本
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# 计算IC
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ic, _ = stats.pearsonr(factor_values, next_returns)
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performance.ic_series.append(ic)
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# 计算RankIC
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rank_ic, _ = stats.spearmanr(factor_values, next_returns)
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performance.rank_ic_series.append(rank_ic)
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# 计算多空组合收益
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sorted_pairs = sorted(zip(factor_values, next_returns),
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key=lambda x: x[0])
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n = len(sorted_pairs) // 5 # 五分位
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if n > 0:
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top_returns = [r for _, r in sorted_pairs[-n:]]
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bottom_returns = [r for _, r in sorted_pairs[:n]]
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spread = np.mean(top_returns) - np.mean(bottom_returns)
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performance.return_spreads.append(spread)
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# 计算Sharpe比率
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if performance.return_spreads:
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annual_factor = np.sqrt(252) # 交易日数
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returns_mean = np.mean(performance.return_spreads)
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returns_std = np.std(performance.return_spreads)
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if returns_std > 0:
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performance.sharpe_ratio = returns_mean / returns_std * annual_factor
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# 估算换手率
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if factor_results:
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dates = sorted(date_groups.keys())
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turnovers = []
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for i in range(1, len(dates)):
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prev_results = date_groups[dates[i-1]]
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curr_results = date_groups[dates[i]]
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# 计算组合变化
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prev_top = {r.ts_code for r in prev_results
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if r.values.get(factor_name, float('-inf')) > np.percentile(
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[res.values.get(factor_name, float('-inf'))
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for res in prev_results], 80)}
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curr_top = {r.ts_code for r in curr_results
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if r.values.get(factor_name, float('-inf')) > np.percentile(
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[res.values.get(factor_name, float('-inf'))
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for res in curr_results], 80)}
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# 计算换手率
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if prev_top and curr_top:
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turnover = len(prev_top ^ curr_top) / len(prev_top | curr_top)
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turnovers.append(turnover)
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if turnovers:
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performance.turnover_rate = np.mean(turnovers)
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return performance
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def combine_factors(
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factor_names: Sequence[str],
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weights: Optional[Sequence[float]] = None
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) -> FactorSpec:
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"""组合多个因子。
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Args:
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factor_names: 因子名称列表
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weights: 可选的权重列表，默认等权重
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Returns:
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组合因子的规格
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"""
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if not weights:
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weights = [1.0 / len(factor_names)] * len(factor_names)
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name = "combined_" + "_".join(factor_names)
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window = max(
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spec.window
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for spec in DEFAULT_FACTORS
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if spec.name in factor_names
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)
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return FactorSpec(name, window)
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