Harwinder
636f0304ae
- 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>
170 lines
6.5 KiB
Markdown
170 lines
6.5 KiB
Markdown
# ForexBot2 — USD/JPY Signal Intelligence
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A focused USD/JPY signal generator + walk-forward backtester. Live signals
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and backtests run through the **same** technical engine, so what you measure
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in a backtest is exactly what you'd execute live.
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## Setup
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```bash
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pip install -r requirements.txt
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```
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API key (optional — a default key is in `config.py`):
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```bash
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export TWELVE_DATA_API_KEY=your_key_here # Linux/Mac
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$env:TWELVE_DATA_API_KEY="your_key_here" # PowerShell
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```
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---
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## Live Signal
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```bash
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python main.py # H1, summary view
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python main.py --detail # Full indicator breakdown
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python main.py --timeframe H4 --detail # H4 instead of H1
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python main.py --json # JSON output (for piping)
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```
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| Option | Default | Description |
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|---------------|---------|--------------------------------------------|
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| `--timeframe` | `H1` | `M5 M15 M30 H1 H4 D1` |
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| `--detail` | off | Indicator breakdown, key levels, reasons |
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| `--json` | off | Machine-readable output |
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| `--verbose` | off | Debug logging |
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### Output
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```
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USD/JPY │ Price: 149.230 │ Regime: trending_up │ Vol: normal
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SIGNAL: BUY │ Score: +28.4 │ Grade: B │ Confidence: 62%
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Action: BUY — Decent setup, wait for confirmation
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Multi-Timeframe: ✓ ALIGNED
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M15: BUY (+18.2)
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H1: BUY (+28.4)
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H4: STRONG BUY (+45.1)
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Trade Setup:
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Direction: BUY
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Entry: 149.230
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Stop Loss: 148.960 (27.0 pips)
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Take Profit 1: 149.770 (54.0 pips)
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Take Profit 2: 150.040
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Risk:Reward: 1:2.0
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```
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---
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## Backtest (Walk-Forward)
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The backtester replays USD/JPY history bar-by-bar through the live signal
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pipeline. No lookahead, no parallel implementation — what passes in
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backtest passes live.
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```bash
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python main.py --backtest # Full available history
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python main.py --backtest --bars 2000 # Last 2000 H1 bars
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python main.py --backtest --start 2024-01-01 --end 2024-12-31
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python main.py --backtest --timeframe H4 # Backtest on H4
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python main.py --backtest --min-grade A # Only A/A+ entries
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python main.py --backtest --journal # Last 10 trades printed
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python main.py --backtest --journal-csv trades.csv # Full journal to CSV
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python main.py --backtest --regime-filter # Skip choppy/ranging bars
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python main.py --backtest --regime-filter --regime-min-adx 35
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```
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### Regime filter
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The engine is a confluence-based trend-follower — it makes money in trending
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regimes and loses in chop. `--regime-filter` gates entries on H4 ADX:
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signals only fire when ADX ≥ `--regime-min-adx` (default 30). Sweet spot is
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ADX 30–35; tighter cuts trades faster than it improves quality.
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### Execution model
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| Aspect | Behavior |
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|--------------------|-------------------------------------------------------------------|
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| Entry | Next bar's open (no current-bar lookahead) |
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| Stop Loss | 1.5 × ATR from entry (configurable) |
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| Take Profit | RR × stop distance (default 1:2) |
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| Same-bar SL & TP | Conservative: SL fills first |
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| Spread cost | Half-spread on entry + half-spread on exit (full round-trip) |
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| Slippage | 0.5 pips per fill (entry + exit) |
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| Position size | 1% of current equity risked per trade |
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| Concurrency | One open position at a time |
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| News in backtest | Disabled — RSS/calendar can't be replayed without lookahead |
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### Output
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```
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Backtest Summary — USD/JPY
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─────────────────────────────────────────────────
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Trades: 87 (52W / 35L)
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Win Rate: 59.8%
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Profit Factor: 1.84
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Expectancy: +12.4 pips/trade (+12.42 per trade)
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Initial Balance: $10,000.00
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Final Balance: $11,084.00
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Total Return: +10.84%
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CAGR: +18.92%
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Max Drawdown: -4.21%
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Sharpe Ratio: 1.42
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Sortino Ratio: 2.18
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Avg Duration: 14.3 bars
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Win Streak: 7 | Loss Streak: 4
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─────────────────────────────────────────────────
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```
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---
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## Parameter Optimization (parallel)
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```bash
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python main.py --backtest --optimize
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```
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Runs a 3×3×3 grid (27 combos) over `atr_sl_multiplier`, `default_rr_ratio`,
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and `min_score` (signal-score threshold). Combos run in parallel across all
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CPU cores via `multiprocessing.Pool`. Twelve Data is hit once — workers
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share an on-disk parquet cache (`.cache/usdjpy_<tf>.parquet`).
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Output ranks combos by total return:
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```
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Top 10 Parameter Combinations (by total return):
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# Return% PF WinR Trades MaxDD% Sharpe Params
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──────────────────────────────────────────────────────────────────────
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1 +14.21% 2.10 62.5% 64 -3.84 1.78 atr_sl_multiplier=1.5, default_rr_ratio=2.5, min_score=25.0
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2 +11.93% 1.92 58.7% 71 -4.11 1.55 atr_sl_multiplier=1.0, default_rr_ratio=2.0, min_score=15.0
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...
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```
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Customize the grid in `config.py → BacktestConfig.param_grid`.
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---
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## Files
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| File | Role |
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|-----------------------|---------------------------------------------------|
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| `main.py` | CLI entry point (live + backtest + optimize) |
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| `config.py` | All parameters in one place |
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| `data_provider.py` | Twelve Data fetcher + parquet cache |
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| `engine.py` | Technical indicators (8 + divergence + patterns) |
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| `signal_aggregator.py`| Multi-TF aggregation, grading, trade setup |
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| `news_engine.py` | RSS sentiment + economic calendar (live only) |
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| `backtest.py` | Walk-forward replay + parallel optimizer |
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| `metrics.py` | Win rate, PF, Sharpe, max DD, trade journal |
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---
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## Disclaimer
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For educational and research use. Past performance does not guarantee
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future results. Always use proper risk management.
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