A comprehensive research infrastructure with:
- Backtesting Framework
- Event-driven architecture
- Transaction cost modeling
- Performance analytics
- Risk management integration
- Factor Research Platform
- Factor return calculation
- Statistical significance testing
- Cross-sectional analysis
- Factor decay modeling
- Signal Generation
- Technical indicators
- Factor-based signals
- Machine learning integration
- Signal combination methods
- Machine Learning Integration
- Time series cross-validation
- Feature engineering
- Model selection and validation
- Ensemble methods
- Alternative Data Processing
- Flexible data source integration
- Custom processing pipelines
- Data quality checks
- Market Regime Detection
- Hidden Markov Models
- Regime characteristics analysis
- Regime-aware strategy adjustment
"""
Backtesting and Research Infrastructure Module for Index Solutions
This module provides comprehensive research capabilities including:
- Backtesting framework
- Factor research and analysis
- Signal generation and testing
- Machine learning model integration
- Alternative data processing
- Market regime detection
"""
import numpy as np
import pandas as pd
from typing import Dict, List, Optional, Union, Callable, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import TimeSeriesSplit
from scipy.stats import norm
import statsmodels.api as sm
from risk_analytics import RiskAnalytics
from trading_execution import TradingExecution, TradeInstruction
@dataclass
class BacktestConfig:
start_date: datetime
end_date: datetime
initial_capital: float
transaction_costs: Dict[str, float] # Different types of costs
rebalance_frequency: str # 'daily', 'weekly', 'monthly'
risk_limits: Dict[str, float]
benchmark: str
@dataclass
class SignalConfig:
lookback_period: int
decay_factor: float
update_frequency: str
combination_method: str # 'equal_weight', 'risk_parity', 'optimization'
class MarketRegime:
def __init__(self, returns: pd.DataFrame, window_size: int = 252):
self.returns = returns
self.window_size = window_size
self.regimes = pd.Series(index=returns.index)
self.regime_stats = {}
def detect_regimes(self, n_regimes: int = 3) -> pd.Series:
"""Detect market regimes using Hidden Markov Model"""
from hmmlearn import hmm
# Calculate features for regime detection
rolling_vol = self.returns.rolling(window=21).std() * np.sqrt(252)
rolling_corr = self.returns.rolling(window=63).corr().mean()
# Combine features
features = np.column_stack([rolling_vol, rolling_corr])
# Fit HMM
model = hmm.GaussianHMM(n_components=n_regimes, covariance_type="full")
self.regimes = pd.Series(
model.fit_predict(features),
index=self.returns.index
)
# Calculate regime characteristics
for regime in range(n_regimes):
mask = self.regimes == regime
self.regime_stats[regime] = {
'volatility': self.returns[mask].std() * np.sqrt(252),
'sharpe': self.returns[mask].mean() / self.returns[mask].std() * np.sqrt(252),
'frequency': mask.mean()
}
return self.regimes
class FactorResearch:
def __init__(self, price_data: pd.DataFrame, factor_data: pd.DataFrame):
self.price_data = price_data
self.factor_data = factor_data
self.factor_returns = pd.DataFrame()
self.factor_exposures = pd.DataFrame()
def calculate_factor_returns(self, method: str = 'regression') -> pd.DataFrame:
"""Calculate factor returns using specified method"""
if method == 'regression':
# Fama-MacBeth regression
self.factor_returns = self._fama_macbeth_regression()
elif method == 'portfolio_sort':
# Portfolio sort method
self.factor_returns = self._portfolio_sort()
return self.factor_returns
def analyze_factor_significance(self) -> pd.DataFrame:
"""Analyze statistical significance of factors"""
factor_stats = pd.DataFrame()
for factor in self.factor_returns.columns:
returns = self.factor_returns[factor]
t_stat = returns.mean() / (returns.std() / np.sqrt(len(returns)))
factor_stats.loc[factor, 'T-Statistic'] = t_stat
factor_stats.loc[factor, 'P-Value'] = 2 * (1 - norm.cdf(abs(t_stat)))
factor_stats.loc[factor, 'Sharpe'] = returns.mean() / returns.std() * np.sqrt(252)
factor_stats.loc[factor, 'IR'] = self._calculate_information_ratio(returns)
return factor_stats
def _fama_macbeth_regression(self) -> pd.DataFrame:
"""Implement Fama-MacBeth regression"""
factor_returns = pd.DataFrame(index=self.price_data.index)
for date in self.price_data.index:
# Cross-sectional regression
y = self.price_data.loc[date]
X = self.factor_data.loc[date]
model = sm.OLS(y, sm.add_constant(X))
results = model.fit()
factor_returns.loc[date] = results.params[1:]
return factor_returns
class SignalGeneration:
def __init__(self, price_data: pd.DataFrame, factor_data: pd.DataFrame,
config: SignalConfig):
self.price_data = price_data
self.factor_data = factor_data
self.config = config
self.signals = pd.DataFrame()
def generate_signals(self, method: str = 'combined') -> pd.DataFrame:
"""Generate trading signals using specified method"""
if method == 'combined':
technical_signals = self._generate_technical_signals()
factor_signals = self._generate_factor_signals()
ml_signals = self._generate_ml_signals()
# Combine signals based on configuration
if self.config.combination_method == 'equal_weight':
self.signals = (technical_signals + factor_signals + ml_signals) / 3
elif self.config.combination_method == 'risk_parity':
self.signals = self._risk_parity_combination([
technical_signals, factor_signals, ml_signals
])
return self.signals
def _generate_technical_signals(self) -> pd.DataFrame:
"""Generate technical analysis signals"""
signals = pd.DataFrame(index=self.price_data.index,
columns=self.price_data.columns)
# Moving average crossover
short_ma = self.price_data.rolling(window=50).mean()
long_ma = self.price_data.rolling(window=200).mean()
signals = (short_ma > long_ma).astype(float)
# Add momentum
momentum = self.price_data.pct_change(20)
signals = signals * (1 + momentum)
return signals
def _generate_ml_signals(self) -> pd.DataFrame:
"""Generate machine learning based signals"""
signals = pd.DataFrame(index=self.price_data.index,
columns=self.price_data.columns)
for asset in self.price_data.columns:
# Prepare features
X = self._prepare_ml_features(asset)
y = self.price_data[asset].pct_change().shift(-1)
# Train model using expanding window
model = RandomForestRegressor(n_estimators=100)
predictions = []
for train_idx, test_idx in TimeSeriesSplit(n_splits=5).split(X):
X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
y_train = y.iloc[train_idx]
model.fit(X_train, y_train)
pred = model.predict(X_test)
predictions.extend(pred)
signals[asset] = predictions
return signals
class AlternativeDataProcessor:
def __init__(self):
self.data_sources = {}
self.processed_data = {}
def add_data_source(self, name: str, data: pd.DataFrame,
processing_func: Callable):
"""Add alternative data source with processing function"""
self.data_sources[name] = {
'data': data,
'processing_func': processing_func
}
def process_all_sources(self) -> Dict[str, pd.DataFrame]:
"""Process all alternative data sources"""
for name, source in self.data_sources.items():
self.processed_data[name] = source['processing_func'](source['data'])
return self.processed_data
class BacktestEngine:
def __init__(self, config: BacktestConfig, trading_execution: TradingExecution,
risk_analytics: RiskAnalytics):
self.config = config
self.trading_execution = trading_execution
self.risk_analytics = risk_analytics
self.portfolio = pd.DataFrame()
self.performance_metrics = {}
def run_backtest(self, signals: pd.DataFrame) -> Dict:
"""Run backtest using generated signals"""
portfolio_value = self.config.initial_capital
positions = {}
trades = []
for date in signals.index:
if self._should_rebalance(date):
# Generate target portfolio
target_positions = self._generate_target_positions(
signals.loc[date], portfolio_value
)
# Generate trades
trades_list = self._generate_trades(positions, target_positions)
# Execute trades
for trade in trades_list:
execution_details = self.trading_execution.execute_trade(trade)
trades.append(execution_details)
# Update positions
positions = target_positions
# Update portfolio value
portfolio_value = self._calculate_portfolio_value(positions, date)
self.portfolio.loc[date, 'value'] = portfolio_value
# Calculate performance metrics
self.performance_metrics = self._calculate_performance_metrics()
return {
'portfolio': self.portfolio,
'trades': trades,
'metrics': self.performance_metrics
}
def _calculate_performance_metrics(self) -> Dict:
"""Calculate comprehensive performance metrics"""
returns = self.portfolio['value'].pct_change()
metrics = {
'total_return': (self.portfolio['value'].iloc[-1] /
self.config.iitial_capital - 1),
'sharpe_ratio': returns.mean() / returns.std() * np.sqrt(252),
'max_drawdown': self._calculate_max_drawdown(),
'turnover': self._calculate_turnover(),
'tracking_error': self._calculate_tracking_error()
}
return metrics
def _calculate_max_drawdown(self) -> float:
"""Calculate maximum drawdown"""
portfolio_value = self.portfolio['value']
rolling_max = portfolio_value.expanding().max()
drawdowns = portfolio_value / rolling_max - 1
return drawdowns.min()
class ResearchInfrastructure:
def __init__(self, price_data: pd.DataFrame, factor_data: pd.DataFrame,
config: BacktestConfig):
self.price_data = price_data
self.factor_data = factor_data
self.config = config
# Initialize components
self.market_regime = MarketRegime(price_data)
self.factor_research = FactorResearch(price_data, factor_data)
self.signal_generation = SignalGeneration(
price_data, factor_data,
SignalConfig(
lookback_period=252,
decay_factor=0.94,
update_frequency='daily',
combination_method='risk_parity'
)
)
self.alternative_data = AlternativeDataProcessor()
self.backtest_engine = BacktestEngine(
config,
TradingExecution(RiskAnalytics(price_data)),
RiskAnalytics(price_data)
)
def run_research_pipeline(self) -> Dict:
"""Run complete research pipeline"""
# Detect market regimes
regimes = self.market_regime.detect_regimes()
# Analyze factors
factor_analysis = self.factor_research.analyze_factor_significance()
# Generate signals
signals = self.signal_generation.generate_signals()
# Run backtest
backtest_results = self.backtest_engine.run_backtest(signals)
return {
'market_regimes': regimes,
'factor_analysis': factor_analysis,
'signals': signals,
'backtest_results': backtest_results
}
Naixian Zhang 