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