File size: 3,902 Bytes
d4fa2ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | """Factor Decomposition Engine - Break returns into style factors."""
import numpy as np
import pandas as pd
from typing import Dict, Optional
import warnings
warnings.filterwarnings('ignore')
class FactorDecomposition:
"""Decompose returns into style factors."""
def __init__(self):
self.factor_names = ['momentum', 'value', 'size', 'volatility', 'quality', 'market']
self.factor_returns = None
self.exposures = None
def compute_factor_returns(self, returns_df: pd.DataFrame) -> pd.DataFrame:
factors = pd.DataFrame(index=returns_df.index)
factors['market'] = returns_df.mean(axis=1)
momentum_rets = []; value_rets = []; size_rets = []; vol_rets = []; quality_rets = []
for date in returns_df.index:
day_returns = returns_df.loc[date].dropna()
if len(day_returns) < 5:
for lst in [momentum_rets, value_rets, size_rets, vol_rets, quality_rets]:
lst.append(0)
continue
past_returns = returns_df.loc[:date].iloc[-21:-1].mean().reindex(day_returns.index).fillna(0) if date in returns_df.index else pd.Series(0, index=day_returns.index)
value_score = 1.0 / (1 + day_returns.abs())
size_score = 1.0 / (day_returns.rolling(21).std().reindex(day_returns.index).fillna(0.01) + 0.01)
vol_score = -day_returns.rolling(21).std().reindex(day_returns.index).fillna(0.01)
quality_score = (day_returns > 0).astype(float)
X = pd.DataFrame({'momentum': past_returns, 'value': value_score, 'size': size_score,
'volatility': vol_score, 'quality': quality_score}).fillna(0)
X = (X - X.mean()) / (X.std() + 1e-8)
y = day_returns.reindex(X.index).fillna(0)
try:
coefs = np.linalg.lstsq(X.values, y.values, rcond=None)[0]
except:
coefs = np.zeros(5)
momentum_rets.append(coefs[0]); value_rets.append(coefs[1]); size_rets.append(coefs[2])
vol_rets.append(coefs[3]); quality_rets.append(coefs[4])
factors['momentum'] = momentum_rets; factors['value'] = value_rets
factors['size'] = size_rets; factors['volatility'] = vol_rets; factors['quality'] = quality_rets
self.factor_returns = factors
return factors
def compute_exposures(self, asset_returns: pd.Series, factor_returns: pd.DataFrame, window: int = 63) -> pd.DataFrame:
from sklearn.linear_model import Ridge
exposures = pd.DataFrame(index=asset_returns.index, columns=factor_returns.columns)
for factor in factor_returns.columns:
for i in range(len(asset_returns)):
if i < window: exposures.iloc[i][factor] = 0; continue
y = asset_returns.iloc[i-window:i].values
X = factor_returns[factor].iloc[i-window:i].values.reshape(-1, 1)
try:
model = Ridge(alpha=1.0).fit(X, y)
exposures.iloc[i][factor] = model.coef_[0]
except:
exposures.iloc[i][factor] = 0
self.exposures = exposures
return exposures
def attribution(self, portfolio_returns: pd.Series, factor_returns: pd.DataFrame, exposures: pd.DataFrame) -> Dict:
factor_contrib = exposures.multiply(factor_returns.reindex(exposures.index).fillna(0)).sum(axis=1)
residual = portfolio_returns - factor_contrib
return {
'total_return': portfolio_returns.sum(),
'factor_return': factor_contrib.sum(),
'residual_return': residual.sum(),
'r_squared': 1 - residual.var() / portfolio_returns.var() if portfolio_returns.var() > 0 else 0
}
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