rams/ftdt-quant-lab
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
53 Commits 1 Branch 0 Tags
main
Commit Graph

5 Commits

Author SHA1 Message Date
ramseshk f4c8bca15a 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
 2026-08-05 06:47:33 +00:00
ramseshk 9768bf80cc Cartea-Jaimungal, Queue Imbalance, Guéant MM: 3 new quant finance strategies + backtests 2026-08-04 06:19:19 +00:00
ramseshk e2b3f40b37 Hawkes OFI + Deep LOB: two new strategies from advanced microstructure research 2026-08-04 06:01:19 +00:00
ramseshk c1da0cbe65 Wire up real Hyperliquid integration and funding rate API 
Replaced the placeholder live node with a proper NautilusTrader
TradingNode that connects to Hyperliquid Testnet using the
official adapter. Added:

- common/hyperliquid_api.py: direct REST calls to Hyperliquid's
  info endpoint for funding rates, predicted fundings, and
  asset contexts
- backtests/run_backtest.py: CLI runner for strategy backtests
- Updated funding_rate_arb.py to fetch real funding rates
  instead of using a hardcoded placeholder
- Added requests to requirements.txt
 2026-08-03 11:37:47 +00:00
ramseshk b59dcc3629 Initial project scaffold: five quant strategies for Hyperliquid Testnet 
Set up the directory structure and wrote placeholder logic for:

- Order Book Imbalance: trades on L2 bid/ask skew
- Iceberg/TWAP detection: follows whale accumulation patterns
- Funding rate arbitrage: delta-neutral carry on perp funding
- Pairs trading: BTC/ETH spread mean reversion
- Avellaneda-Stoikov market making: optimal bid/ask quoting

Also added shared risk manager, portfolio tracker, and a plain-language strategy walkthrough in docs/.
 2026-08-03 11:12:20 +00:00