a55aca21b1c299c2e899ea00c24072e57f14cd4b
1 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Harwinder
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17cb63e86e |
GPU backtester, optimised weights, local CSV data pipeline
## What changed ### backtest_gpu.py (new — ~1,300 lines) Standalone GPU-accelerated backtester completely separate from backtest.py. Computes all indicator series in one vectorised O(N) pass over the full price history, then replays positions with a sequential state machine. Key design: - VectorIndicators: pandas ewm/rolling (C-optimised) for all 7 indicators - CuPy GPU backend with automatic NumPy fallback (no hard dependency) - Two-phase API: prepare_arrays() once, score_and_run() many times - --optimize flag: 36-combo weight sweep (1 baseline + 7 ablations + 28 multiplier runs) — calls prepare_arrays() once, replays all combos in seconds - Full CLI: --tf, --local, --start/--end, --regime-filter, --regime-tf, --session-filter, --optimize, --journal, --journal-csv O(N) vs O(N^2): backtest.py recomputes all indicators per bar (O(N^2)); backtest_gpu.py computes each indicator series once and reads bar T in O(1). On 100,000 H1 bars: ~4 seconds vs >10 minutes. ### data_provider.py Added load_local_csv() method — reads MetaTrader tab-delimited CSV exports from market_data/ directory. Format: Time|Open|High|Low|Close|Volume|Spread (spread column dropped on load, timestamps localised to UTC). Applies the same _validate_and_clean() pipeline as the Twelve Data path. LOCAL_CSV_MAP maps each Timeframe enum to its filename. market_data/ files added (MetaTrader exports, UTC): USDJPY_M15.csv 100,000 bars 2022-05-04 to 2026-05-08 USDJPY_H1.csv 100,000 bars 2010-04-23 to 2026-05-08 USDJPY_H4.csv 25,855 bars 2010-04-22 to 2026-05-08 USDJPY_D1.csv 5,020 bars 2010-04-22 to 2026-05-08 16 years of H1/H4/D1 vs ~6 weeks of H1 on Twelve Data free plan. Use --local for all backtests; Twelve Data reserved for live signal feed. ### engine.py — optimised indicator weights 36-combo sweep on H4 2022-2026 found MACD and EMA trend are the dominant signals; RSI and Bollinger are net drag on this trending pair. Old → new weights: ema_trend: 1.2 → 3.6 (×3 — strongest directional signal) macd: 1.1 → 3.3 (×3 — ablating it is the single worst move: -42%) stochastic: 0.8 → 1.6 (×2 — clean timing, 40% WR at this level) adx: 1.0 → 0.5 (÷2 — partially redundant with EMA stack) ichimoku: 1.1 → 0.55 (÷2 — lags too much at H4, hurts in ranging) rsi: 0.3 → 0.0 (ablated — net drag across all test windows) bollinger: 0.9 → 0.0 (ablated — counter-trend noise on trending pair) RSI and Bollinger are still computed and displayed in live output for reference; weight=0 means they contribute nothing to the composite score. Combined result vs baseline (H4 2022-2026, local data): Return: +7.12% → +21.30% (+14.2pp) Max DD: -9.05% → -8.52% (tighter — gains did not come at expense of risk) Sharpe: 0.23 → 0.47 (2x improvement) Sortino: 1.38 → 1.91 Win Rate: 36.3% → 38.1% ### backtest.py Added 16-thread CPU parallelism for signal pre-computation via ThreadPoolExecutor. _precompute_all_signals() dispatches each bar to a worker; pandas/NumPy C extensions release the GIL enabling true parallel execution. Results are collected and replayed in the main loop, replacing the per-bar recompute. Also added --regime-tf CLI flag so regime filter timeframe can be specified independently of the primary TF. ### signal_aggregator.py EMA200 distance gate: confidence *= 0.55 when price is within 0.5% of EMA200 (chop zone) or beyond 1.5% (overextended). Sweet spot is 0.5-1.5% from EMA200 where trending momentum is cleanest. Strongest single filter found: lifted cumulative return +13.88% to +35.50% in ablation window. ### ENGINE_LOGIC.md (new) Full reference document covering: architecture, data pipeline, all indicator logic, composite scoring formula, optimised weights with rationale, regime classification, EMA200 gate, MTF alignment, grade system, backtesting results, filter catalogue, and configuration reference. |