## 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.
data_provider now uses TDClient.time_series().as_pandas() instead of
hand-rolled REST calls. Throttle, validation, and parquet caching are
unchanged. Adds twelvedata>=1.2.0 to requirements.txt.
Harwinder
harry