Kalman Filter Pairs Trading System — full production-grade implementation
Core engine (pure NumPy, zero external deps beyond NumPy):
- kalman_filter.py: KalmanFilter + KalmanPairsTrader
- Time-varying observation matrix H_t = [1, X_t]
- RTS smoother for offline analysis
- Properties: alpha, beta, spread = Y - (alpha + beta*X)
- Signal: z-score crossing z_entry/z_exit/z_stop thresholds
Pair discovery (pure NumPy):
- pair_discovery.py: Engle-Granger cointegration + OU half-life
- ADF test with MacKinnon critical values (no statsmodels)
- Half-life estimation via OLS on AR(1) residuals
- Pair screening: cointegrated + 1-20 period half-life
- Rolling OLS hedge ratio for baseline comparison
Production system:
- trading_system.py: KalmanPairsTradingSystem
- Multi-pair orchestration with risk overlay
- Capital allocation, stop-loss, drawdown controls
- KalmanPairsConfig dataclass (YAML-compatible)
Backtesting:
- backtest.py: Walk-forward backtest with realistic execution
- Transaction costs, capital tracking, per-trade PnL
- Side-by-side Kalman vs rolling OLS comparison
- Metrics: CAGR, Sharpe, Sortino, max DD, win rate, turnover
Tuning:
- tuning.py: Grid search over transition_covariance
- Train/validation split (chronological)
- Objective: maximize Sharpe - penalty * max_drawdown
Regime-shift test results:
Kalman: Sharpe 2.17, beta adapts from 2.0 -> 0.5 in ~50 bars
OLS 60d: Sharpe 0.17 (stuck on old beta)
OLS 120d: Sharpe 0.66 (even slower adaptation)
Integration: Added to historical_runner.py as kalman_pairs strategy
ramseshk
f4c8bca15a