A-grade trades entered exhausted moves with TP too far (0.75R), causing
57.7% WR and +0.25R over 30 days. Store exhaustion_count on SignalReport
and scale TP to B-level (1.0×) when A-grade has 2+ exhaustion tags.
Backtest: A-grade 73.1% WR / +3.25R, B/C unchanged.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signal daemon (signal_web.py): candle-boundary scanner mirroring
telegram_bot.py, publishes serialized SignalReport JSON to Redis.
Web API (web/app.py): thin FastAPI reader behind Apache, serves
/api/signals REST + /ws WebSocket from Redis pub/sub.
Frontend (web/static/index.html): 3-pair grid, color-coded signals,
indicator breakdown per timeframe, trade setup display.
Also: XAU-specific penalty scoping in signal_aggregator (softer
confidence penalties for gold, original strict values for forex),
pair-aware session filter routing XAU to its own blocked hours.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The general session filter ran after analyze_from_dataframes() and
wiped out XAU oversold bounce signals at most hours. Now skips the
session filter when the XAU strategy has already fired — the strategy
has its own backtested hour filter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
BUY-only strategy for gold: enters when M15 RSI drops below oversold
threshold while price remains above EMA200 on all three timeframes
(M15, H1, H4). Uses ATR-based TP/SL (0.7x/2.0x) sized from M15 ATR.
Isolated to XAU/USD only — other pairs completely unaffected.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Drop USD/JPY from scan pairs (zero signals across all timeframes)
- Disable divergence indicator (zero impact in 14-config lab test)
- Parallelize evaluate.py with ProcessPoolExecutor (16 cores)
- Add --pair CLI filter to evaluate.py for single-pair backtests
- Add data freshness guard: stale cache auto-invalidated, primary TF
stale data suppresses signals to prevent trading on old data
- Add /cache command: shows fetch age and last bar per pair/TF
- Add /clearcache command: wipes in-memory cache for fresh API fetch
- Remove BB from WebSocket breach alerts (keep structural levels only)
- Show live prices only for WS-monitored pairs (EUR/USD, XAU/USD)
- Replace arrow emojis with text tags in indicator lines
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Trade setup TP now scales to SL distance ensuring min RR is met when
no viable swing target exists; skip swing targets with poor RR
- Add float tolerance to RR gate to prevent edge-case rejection
- M5 auto-scan runs for level cache only, no Telegram broadcast
- WebSocket limited to EUR/USD + XAU/USD (Grow plan constraint)
- Register bot command menu via set_my_commands on startup
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Expand from 2 to 4 pairs (EUR/USD, USD/JPY, GBP/USD, XAU/USD) with
automatic scanning at M15 and H1 candle close boundaries. Scheduler
staggers H1 by 60s to stay within 8 credits/min; daily budget is
480 of 800 allowed. Cache TTL is now candle-duration-aware so cross-TF
confirmation data is reused without extra API calls. Spread estimate
uses primary-TF data instead of fetching M5 separately.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove backtest.py, backtest_gpu.py, metrics.py, local CSV data,
Dukascopy loader, and all backtest CLI modes from main.py. Rebalance
engine weights: leading indicators (divergence, RSI, stochastic, BB)
now carry 66% of signal weight vs 34% for lagging (EMA, MACD). Add
ws_twelvedata.py for live tick streaming. Drop pyarrow and tqdm deps.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
## 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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Across 2024 trend / 2023 chop / 2026 hostile windows, distance from the
H1 EMA200 was empirically the most discriminative variable. Sweet spot
0.5-1.5% from EMA200; closer = false-breakout chop, farther = chase.
Outside the band, confidence is multiplied by 0.55 so the trade falls
below the B-grade threshold and is rejected.
Cumulative return across the 3 windows: +13.88% baseline -> +35.50%.
Win rate improved in every regime; 2023 chop turned positive (-2.56% to
+14.94%).
Also extends the trade journal with per-timeframe MTF scores, RSI at
entry, and EMA200 distance for future filter mining; surfaces ema_trend
in key_levels so the gate can read it.
Session filter restricts trades to a UTC hour window (default 07-16,
London + NY) — opt-in via --session-filter, with --session-start /
--session-end overrides. Wired into both the backtester (cheap hour
check next to the regime gate) and live mode (downgrades signal to
NEUTRAL/F when current UTC hour is outside the window).
Auto-toggle (--auto-session-filter) tracks rolling mean pnl_pct over
the last N closed trades; when it falls below threshold, the session
filter activates, releasing once expectancy recovers. Default lookback
10 trades, threshold -0.3%.
Trade dataclass extended with diagnostic fields captured at entry:
entry_hour, entry_dow, ADX, ATR%, market_regime, volatility_regime,
mtf_agreement. These let --journal-csv exports surface loss-pattern
clusters when a window underperforms.
Validated on 2024 trend / 2023 chop / 2026 recent windows. Auto-toggle
preserves ~50% of trend edge while still cutting ~25% of recent-regime
losses — pure regime filter is still best on cumulative, so both
session flags ship default-off.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
--regime-filter previously affected only backtests. Live mode now also
checks higher-TF ADX after analysis: signals are downgraded to NEUTRAL
with a warning when ADX < threshold, and confirming when above.
This brings live behavior into parity with what the backtester measures.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Strip multi-pair plumbing; drop synthetic-data fallback
- Add BacktestConfig + walk-forward replay through the live signal pipeline
- Add metrics module (PF, Sharpe, Sortino, max DD, trade journal)
- Add parallel grid optimizer with disk parquet cache
- Add H4 ADX regime gate (--regime-filter, --regime-min-adx) — cuts chop
losses ~84% in 2023 H1 while preserving trend edge
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
harwinder
harry