227 lines
6.8 KiB
Python
227 lines
6.8 KiB
Python
"""
|
||
因子检验模块：IC检验、分组回测、因子跨度回归
|
||
"""
|
||
import numpy as np
|
||
import pandas as pd
|
||
from typing import Dict, List, Tuple
|
||
from statsmodels.regression.linear_model import OLS
|
||
|
||
|
||
def compute_ic(factor: pd.Series, forward_return: pd.Series, method: str = 'spearman') -> pd.Series:
|
||
"""
|
||
计算IC（信息系数）
|
||
|
||
Parameters:
|
||
-----------
|
||
factor : Series
|
||
因子值
|
||
forward_return : Series
|
||
未来收益率
|
||
method : str
|
||
相关性计算方法：'spearman' 或 'pearson'
|
||
"""
|
||
aligned = pd.concat([factor, forward_return], axis=1).dropna()
|
||
if len(aligned) < 10:
|
||
return pd.Series(dtype=float)
|
||
|
||
if method == 'spearman':
|
||
ic = aligned.iloc[:, 0].rank().corr(aligned.iloc[:, 1].rank())
|
||
else:
|
||
ic = aligned.iloc[:, 0].corr(aligned.iloc[:, 1])
|
||
|
||
return pd.Series([ic], index=[aligned.index[-1]])
|
||
|
||
|
||
def compute_rolling_ic(
|
||
factor: pd.Series,
|
||
forward_return: pd.Series,
|
||
window: int = 30,
|
||
method: str = 'spearman'
|
||
) -> pd.Series:
|
||
"""计算滚动IC（向量化优化）"""
|
||
# 对齐数据
|
||
aligned = pd.concat([factor, forward_return], axis=1).dropna()
|
||
if len(aligned) < window:
|
||
return pd.Series(dtype=float, index=factor.index[window:])
|
||
|
||
aligned.columns = ['factor', 'return']
|
||
|
||
if method == 'spearman':
|
||
# 使用rank计算Spearman相关性
|
||
factor_rank = aligned['factor'].rank()
|
||
return_rank = aligned['return'].rank()
|
||
# 使用DataFrame的rolling().corr()方法
|
||
df_rank = pd.DataFrame({'factor': factor_rank, 'return': return_rank})
|
||
ic_series = df_rank['factor'].rolling(window, min_periods=window).corr(df_rank['return'])
|
||
else:
|
||
# Pearson相关性
|
||
df = pd.DataFrame({'factor': aligned['factor'], 'return': aligned['return']})
|
||
ic_series = df['factor'].rolling(window, min_periods=window).corr(df['return'])
|
||
|
||
return ic_series
|
||
|
||
|
||
def group_backtest(
|
||
factor: pd.Series,
|
||
forward_return: pd.Series,
|
||
n_groups: int = 3,
|
||
group_period: int = 180
|
||
) -> Dict:
|
||
"""
|
||
分组回测：将数据按因子值分组，计算各组收益
|
||
|
||
Returns:
|
||
--------
|
||
dict: 包含各组收益、H-L收益差、t统计量等
|
||
"""
|
||
aligned = pd.concat([factor, forward_return], axis=1).dropna()
|
||
aligned.columns = ['factor', 'return']
|
||
|
||
results = {
|
||
'group_returns': [],
|
||
'h_l_return': [],
|
||
'h_l_tstat': [],
|
||
'periods': []
|
||
}
|
||
|
||
# 按月分组（每180个4h周期）- 使用更高效的步长
|
||
step = max(group_period // 2, 90) # 减少重叠计算
|
||
for start in range(0, len(aligned) - group_period, step):
|
||
end = start + group_period
|
||
period_data = aligned.iloc[start:end]
|
||
|
||
if len(period_data) < 30:
|
||
continue
|
||
|
||
# 按因子值分组（向量化）
|
||
try:
|
||
period_data = period_data.copy()
|
||
period_data['group'] = pd.qcut(
|
||
period_data['factor'],
|
||
q=n_groups,
|
||
labels=False,
|
||
duplicates='drop'
|
||
)
|
||
|
||
# 计算各组收益（向量化）
|
||
group_returns = period_data.groupby('group')['return'].mean()
|
||
results['group_returns'].append(group_returns)
|
||
|
||
# H-L收益差
|
||
if len(group_returns) >= 2:
|
||
h_return = group_returns.iloc[-1] # 高因子组
|
||
l_return = group_returns.iloc[0] # 低因子组
|
||
h_l_diff = h_return - l_return
|
||
|
||
results['h_l_return'].append(h_l_diff)
|
||
results['periods'].append(period_data.index[-1])
|
||
except (ValueError, KeyError):
|
||
# qcut失败时跳过
|
||
continue
|
||
|
||
# 计算平均H-L收益和t统计量
|
||
if results['h_l_return']:
|
||
h_l_series = pd.Series(results['h_l_return'], index=results['periods'])
|
||
mean_h_l = h_l_series.mean()
|
||
std_h_l = h_l_series.std()
|
||
t_stat = mean_h_l / (std_h_l / np.sqrt(len(h_l_series)) + 1e-8)
|
||
|
||
results['mean_h_l_return'] = mean_h_l
|
||
results['mean_h_l_tstat'] = t_stat
|
||
results['h_l_series'] = h_l_series
|
||
else:
|
||
results['mean_h_l_return'] = 0
|
||
results['mean_h_l_tstat'] = 0
|
||
|
||
return results
|
||
|
||
|
||
def factor_span_regression(
|
||
factors: pd.DataFrame,
|
||
forward_return: pd.Series,
|
||
target_factor: str
|
||
) -> Dict:
|
||
"""
|
||
因子跨度回归：检验因子的边际解释力
|
||
|
||
Parameters:
|
||
-----------
|
||
factors : DataFrame
|
||
所有因子数据框
|
||
forward_return : Series
|
||
未来收益率
|
||
target_factor : str
|
||
目标因子名称
|
||
|
||
Returns:
|
||
--------
|
||
dict: 包含回归系数、t统计量、R²等
|
||
"""
|
||
# 对齐数据
|
||
data = pd.concat([factors, forward_return], axis=1).dropna()
|
||
if len(data) < 30:
|
||
return {'beta': 0, 'tstat': 0, 'r2': 0, 'r2_change': 0}
|
||
|
||
y = data.iloc[:, -1].values
|
||
X_all = data.iloc[:, :-1].values
|
||
|
||
# 全模型（包含目标因子）
|
||
try:
|
||
model_all = OLS(y, X_all).fit(cov_type='HAC', cov_kwds={'maxlags': 6})
|
||
r2_all = model_all.rsquared
|
||
|
||
# 目标因子的系数和t统计量
|
||
target_idx = factors.columns.get_loc(target_factor)
|
||
beta = model_all.params[target_idx]
|
||
tstat = model_all.tvalues[target_idx]
|
||
|
||
# 不含目标因子的模型
|
||
X_without = np.delete(X_all, target_idx, axis=1)
|
||
model_without = OLS(y, X_without).fit(cov_type='HAC', cov_kwds={'maxlags': 6})
|
||
r2_without = model_without.rsquared
|
||
|
||
r2_change = r2_all - r2_without
|
||
|
||
return {
|
||
'beta': beta,
|
||
'tstat': tstat,
|
||
'r2': r2_all,
|
||
'r2_change': r2_change,
|
||
'pvalue': model_all.pvalues[target_idx]
|
||
}
|
||
except Exception as e:
|
||
print(f"回归分析出错: {e}")
|
||
return {'beta': 0, 'tstat': 0, 'r2': 0, 'r2_change': 0}
|
||
|
||
|
||
def validate_factor(
|
||
factor: pd.Series,
|
||
forward_return: pd.Series,
|
||
ic_window: int = 30,
|
||
n_groups: int = 3
|
||
) -> Dict:
|
||
"""
|
||
综合因子检验
|
||
|
||
Returns:
|
||
--------
|
||
dict: 包含IC、分组回测、显著性等指标
|
||
"""
|
||
# IC检验
|
||
rolling_ic = compute_rolling_ic(factor, forward_return, window=ic_window)
|
||
mean_ic = rolling_ic.mean()
|
||
ic_ir = mean_ic / (rolling_ic.std() + 1e-8) # IC信息比率
|
||
|
||
# 分组回测
|
||
group_result = group_backtest(factor, forward_return, n_groups=n_groups)
|
||
|
||
return {
|
||
'mean_ic': mean_ic,
|
||
'ic_ir': ic_ir,
|
||
'ic_series': rolling_ic,
|
||
'mean_h_l_return': group_result['mean_h_l_return'],
|
||
'mean_h_l_tstat': group_result['mean_h_l_tstat'],
|
||
'group_returns': group_result['group_returns']
|
||
}
|
||
|