Refocus on USD/JPY and add walk-forward backtester with regime filter
- 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
This commit is contained in:
Harwinder
@@ -1,4 +1,8 @@
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# ForexBot2 — Usage Guide
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# 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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@@ -6,159 +10,160 @@
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pip install -r requirements.txt
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```
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Set your Twelve Data API key (or it falls back to the key in `config.py`):
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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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set TWELVE_DATA_API_KEY=your_key_here # Windows CMD
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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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## Basic Syntax
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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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python main.py [OPTIONS]
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```
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---
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## Options
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| Option | Default | Description |
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|---|---|---|
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| `--pairs PAIR [PAIR ...]` | all major pairs | Specific pairs to analyze |
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| `--timeframe TF` | `H1` | Primary timeframe: `M5 M15 M30 H1 H4 D1` |
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| `--all` | off | Add EUR/GBP, EUR/JPY, GBP/JPY and other cross pairs |
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| `--detail` | off | Full indicator breakdown per pair |
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| `--top N` | all | Show only the top N signals by strength |
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| `--min-grade GRADE` | `F` (show all) | Filter by minimum quality grade: `A+ A B C D F` |
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| `--json` | off | Output results as JSON instead of the terminal UI |
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| `--watch` | off | Continuous monitoring — refreshes every 5 minutes |
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| `--verbose` / `-v` | off | Enable debug logging |
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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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---
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## Common Examples
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### Analyze all major Forex pairs (default)
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```bash
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python main.py
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```
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Analyzes EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD, USD/CAD, NZD/USD on H1.
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---
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### Analyze Gold (XAU/USD)
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```bash
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python main.py --pairs XAUUSD
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```
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Pass the symbol without the slash. The engine converts it to Twelve Data format automatically.
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---
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### Gold on the 4-hour chart with full detail
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```bash
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python main.py --pairs XAUUSD --timeframe H4 --detail
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```
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---
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### Multiple pairs at once
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```bash
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python main.py --pairs XAUUSD EURUSD GBPUSD
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```
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---
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### Only show high-conviction signals (Grade B or above)
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```bash
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python main.py --min-grade B
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```
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---
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### Top 3 signals across all pairs including crosses
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```bash
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python main.py --all --top 3
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```
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---
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### Continuous monitoring — updates every 5 minutes
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```bash
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python main.py --watch
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```
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Add `--pairs XAUUSD` to watch a single instrument, or `--all` for everything.
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```bash
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python main.py --pairs XAUUSD --watch --detail
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```
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---
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### JSON output (for piping or logging)
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```bash
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python main.py --pairs XAUUSD --json
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python main.py --all --json > signals.json
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```
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---
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## Signal Grades
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| Grade | Meaning |
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|---|---|
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| A+ | Strongest — all timeframes aligned, high confidence |
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| A | Strong signal, minor divergence |
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| B | Good setup, watch for confirmation |
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| C | Weak — for reference only |
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| D/F | Noise — best ignored |
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---
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## Supported Symbols
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Pass any symbol without slashes. Examples:
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| You type | Pair |
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|---|---|
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| `XAUUSD` | Gold vs USD |
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| `EURUSD` | Euro vs USD |
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| `GBPUSD` | British Pound vs USD |
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| `USDJPY` | USD vs Japanese Yen |
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| `GBPJPY` | GBP vs Japanese Yen |
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| `USDCAD` | USD vs Canadian Dollar |
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| `AUDUSD` | Australian Dollar vs USD |
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Any pair supported by Twelve Data can be used.
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---
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## Output Explained
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### Output
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```
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EUR/USD │ Price: 1.08423 │ Regime: trending_up │ Vol: normal
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USD/JPY │ Price: 149.230 │ Regime: trending_up │ Vol: normal
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SIGNAL: STRONG BUY │ Score: +62.4 │ Grade: A │ Confidence: 74%
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Action: Strong buy signal with multi-timeframe alignment...
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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 (+28.1)
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H1: STRONG BUY (+62.4)
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H4: BUY (+41.0)
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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: 1.08423
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Stop Loss: 1.08150 (27.3 pips)
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Take Profit 1: 1.08970 (54.7 pips)
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Take Profit 2: 1.09517
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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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- **Score** — ranges from -100 (strong sell) to +100 (strong buy)
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- **Grade** — summary quality of the signal setup
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- **Regime** — `trending_up`, `trending_down`, or `ranging`
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- **Trade Setup** — auto-calculated entry, SL, and TP based on ATR
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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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+514
@@ -0,0 +1,514 @@
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"""
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ForexBot2 — Walk-Forward Backtester + Parameter Optimizer
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==========================================================
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Replays Twelve Data USD/JPY history bar-by-bar through the same
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SignalAggregator that drives live mode. The same code path that
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trades USD/JPY in production is what produces these metrics — so
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the backtest validates the engine, not a parallel implementation.
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"""
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from __future__ import annotations
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import copy
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import itertools
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import logging
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import multiprocessing as mp
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import os
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from dataclasses import replace
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from typing import Dict, List, Optional, Tuple
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import pandas as pd
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from config import PlatformConfig, Timeframe, USDJPY, PIP_VALUE
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from data_provider import ForexDataProvider
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from metrics import BacktestResult, Trade, compute_metrics, trades_to_dataframe
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from signal_aggregator import SignalAggregator, SignalReport, TradeSetup
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logger = logging.getLogger(__name__)
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# Period in minutes for each timeframe (used for close-time computation)
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TF_MINUTES: Dict[Timeframe, int] = {
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Timeframe.M1: 1,
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Timeframe.M5: 5,
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Timeframe.M15: 15,
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Timeframe.M30: 30,
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Timeframe.H1: 60,
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Timeframe.H4: 240,
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Timeframe.D1: 1440,
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Timeframe.W1: 10080,
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}
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GRADE_ORDER = ["A+", "A", "B", "C", "D", "F"]
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def _grade_passes(grade: str, min_grade: str) -> bool:
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"""True if `grade` is at least as good as `min_grade`."""
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if grade not in GRADE_ORDER or min_grade not in GRADE_ORDER:
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return False
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return GRADE_ORDER.index(grade) <= GRADE_ORDER.index(min_grade)
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# ══════════════════════════════════════════════════════════════════
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# WALK-FORWARD BACKTESTER
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# ══════════════════════════════════════════════════════════════════
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class WalkForwardBacktester:
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"""Single-pair, single-position event-driven backtester."""
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def __init__(self, config: PlatformConfig):
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self.config = config
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self.aggregator = SignalAggregator(config)
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self.data_provider = self.aggregator.data_provider
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self._regime_adx: Optional[pd.Series] = None
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self._regime_tf: Optional[Timeframe] = None
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# ── Public ─────────────────────────────────────────────────────
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def load_data(self) -> Dict[Timeframe, pd.DataFrame]:
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"""Pull (or read from disk cache) all needed timeframes."""
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timeframes = [self.config.primary_timeframe] + list(
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self.config.confirmation_timeframes
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)
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bt = self.config.backtest
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if bt.regime_filter and bt.regime_filter_timeframe not in timeframes:
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timeframes.append(bt.regime_filter_timeframe)
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dfs: Dict[Timeframe, pd.DataFrame] = {}
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for tf in timeframes:
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df = self.data_provider.fetch_full_history(
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symbol=self.config.symbol,
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timeframe=tf,
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max_bars=bt.max_bars,
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start_date=bt.start_date,
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end_date=bt.end_date,
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)
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df = self._apply_window(df)
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dfs[tf] = df
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return dfs
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def run(
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self,
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dfs: Optional[Dict[Timeframe, pd.DataFrame]] = None,
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progress: bool = True,
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) -> BacktestResult:
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if dfs is None:
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dfs = self.load_data()
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self._precompute_regime_filter(dfs)
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primary_tf = self.config.primary_timeframe
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primary_df = dfs.get(primary_tf)
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if primary_df is None or primary_df.empty:
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return BacktestResult(initial_balance=self.config.backtest.initial_balance)
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# Warmup: enough bars for ema_trend & ichimoku_senkou_b on the primary TF
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warmup = max(
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self.config.technical.ema_trend,
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self.config.technical.ichimoku_senkou_b,
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200,
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) + 30
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if len(primary_df) <= warmup + 1:
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logger.warning(
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f"Insufficient data: {len(primary_df)} primary bars, "
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f"need >{warmup}."
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)
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return BacktestResult(initial_balance=self.config.backtest.initial_balance)
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bt = self.config.backtest
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balance = bt.initial_balance
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equity_points: List[Tuple[pd.Timestamp, float]] = [
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(primary_df.index[warmup], balance)
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]
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trades: List[Trade] = []
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open_position: Optional[Dict] = None
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# Optional progress bar
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iterator = range(warmup, len(primary_df) - 1)
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if progress:
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try:
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from tqdm import tqdm # type: ignore
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iterator = tqdm(iterator, desc="Backtesting", unit="bar", leave=False)
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except ImportError:
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pass
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for t in iterator:
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decision_time = self._bar_close_time(primary_df.index[t], primary_tf)
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# ── Step 1: manage any open position ───────────────────
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if open_position is not None:
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next_bar = primary_df.iloc[t]
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exit_info = self._check_exit(open_position, next_bar)
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if exit_info is not None:
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closed = self._close_trade(
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open_position, next_bar.name, exit_info, balance
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)
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trades.append(closed)
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balance += closed.pnl_currency
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equity_points.append((next_bar.name, balance))
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open_position = None
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# ── Step 2: maybe open a new position ──────────────────
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if open_position is None or not bt.one_position_only:
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# Slice each TF's df up to (and including) bars closed by decision_time
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sliced = self._slice_dfs(dfs, decision_time)
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report = self.aggregator.analyze_from_dataframes(
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symbol=self.config.symbol,
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dfs=sliced,
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use_news=False,
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)
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if (
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_grade_passes(report.signal_quality, bt.min_trade_grade)
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and abs(float(report.final_score)) >= bt.min_abs_score
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and report.trade_setup is not None
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and t + 1 < len(primary_df)
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and self._regime_passes(decision_time)
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):
|
||||
next_bar = primary_df.iloc[t + 1]
|
||||
open_position = self._open_trade(report, next_bar, balance)
|
||||
|
||||
# ── Force-close any still-open position at end of data ────
|
||||
if open_position is not None:
|
||||
last_bar = primary_df.iloc[-1]
|
||||
closed = self._close_trade(
|
||||
open_position,
|
||||
last_bar.name,
|
||||
{"price": float(last_bar["Close"]), "reason": "end_of_data"},
|
||||
balance,
|
||||
)
|
||||
trades.append(closed)
|
||||
balance += closed.pnl_currency
|
||||
equity_points.append((last_bar.name, balance))
|
||||
|
||||
# Build equity curve
|
||||
eq_index = [p[0] for p in equity_points]
|
||||
eq_values = [p[1] for p in equity_points]
|
||||
equity_curve = pd.Series(eq_values, index=pd.DatetimeIndex(eq_index))
|
||||
|
||||
elapsed_days: Optional[float] = None
|
||||
if len(equity_curve) >= 2:
|
||||
elapsed_days = (equity_curve.index[-1] - equity_curve.index[0]).total_seconds() / 86400
|
||||
|
||||
metrics = compute_metrics(
|
||||
trades, equity_curve, bt.initial_balance, elapsed_days=elapsed_days
|
||||
)
|
||||
|
||||
return BacktestResult(
|
||||
trades=trades,
|
||||
equity_curve=equity_curve,
|
||||
initial_balance=bt.initial_balance,
|
||||
final_balance=balance,
|
||||
metrics=metrics,
|
||||
)
|
||||
|
||||
# ── Private: regime gate ───────────────────────────────────────
|
||||
|
||||
def _precompute_regime_filter(self, dfs: Dict[Timeframe, pd.DataFrame]) -> None:
|
||||
"""Pre-compute ADX on the regime-filter TF; cached for the whole run."""
|
||||
bt = self.config.backtest
|
||||
if not bt.regime_filter:
|
||||
self._regime_adx = None
|
||||
self._regime_tf = None
|
||||
return
|
||||
|
||||
tf = bt.regime_filter_timeframe
|
||||
df = dfs.get(tf)
|
||||
if df is None or df.empty:
|
||||
logger.warning(
|
||||
f"Regime filter requested ({tf.value}) but no data available; "
|
||||
f"all signals will be skipped."
|
||||
)
|
||||
self._regime_adx = pd.Series(dtype=float)
|
||||
self._regime_tf = tf
|
||||
return
|
||||
|
||||
self._regime_tf = tf
|
||||
self._regime_adx = self._compute_adx_series(df, self.config.technical.adx_period)
|
||||
|
||||
def _regime_passes(self, decision_time: pd.Timestamp) -> bool:
|
||||
bt = self.config.backtest
|
||||
if not bt.regime_filter:
|
||||
return True
|
||||
if self._regime_adx is None or self._regime_adx.empty:
|
||||
return False
|
||||
period_min = TF_MINUTES[self._regime_tf]
|
||||
close_times = self._regime_adx.index + pd.Timedelta(minutes=period_min)
|
||||
valid = self._regime_adx[close_times <= decision_time]
|
||||
if valid.empty:
|
||||
return False
|
||||
last = valid.iloc[-1]
|
||||
if pd.isna(last):
|
||||
return False
|
||||
return float(last) >= bt.regime_filter_min_adx
|
||||
|
||||
@staticmethod
|
||||
def _compute_adx_series(df: pd.DataFrame, period: int = 14) -> pd.Series:
|
||||
"""Wilder's ADX series — same algorithm as engine._compute_adx."""
|
||||
import numpy as np
|
||||
high, low, close = df["High"], df["Low"], df["Close"]
|
||||
tr = pd.concat([
|
||||
high - low,
|
||||
(high - close.shift()).abs(),
|
||||
(low - close.shift()).abs(),
|
||||
], axis=1).max(axis=1)
|
||||
plus_dm = high.diff()
|
||||
minus_dm = -low.diff()
|
||||
plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
|
||||
minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
|
||||
atr_s = tr.ewm(alpha=1/period, min_periods=period).mean()
|
||||
plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
|
||||
minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
|
||||
dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
|
||||
return dx.ewm(alpha=1/period, min_periods=period).mean()
|
||||
|
||||
# ── Private: data slicing ──────────────────────────────────────
|
||||
|
||||
def _apply_window(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
bt = self.config.backtest
|
||||
if bt.start_date:
|
||||
start = pd.Timestamp(bt.start_date, tz="UTC")
|
||||
df = df[df.index >= start]
|
||||
if bt.end_date:
|
||||
end = pd.Timestamp(bt.end_date, tz="UTC")
|
||||
df = df[df.index <= end]
|
||||
return df
|
||||
|
||||
@staticmethod
|
||||
def _bar_close_time(open_time: pd.Timestamp, tf: Timeframe) -> pd.Timestamp:
|
||||
return open_time + pd.Timedelta(minutes=TF_MINUTES[tf])
|
||||
|
||||
def _slice_dfs(
|
||||
self, dfs: Dict[Timeframe, pd.DataFrame], decision_time: pd.Timestamp
|
||||
) -> Dict[Timeframe, pd.DataFrame]:
|
||||
"""Return only bars whose close time ≤ decision_time (no lookahead)."""
|
||||
sliced: Dict[Timeframe, pd.DataFrame] = {}
|
||||
for tf, df in dfs.items():
|
||||
close_times = df.index + pd.Timedelta(minutes=TF_MINUTES[tf])
|
||||
sliced[tf] = df[close_times <= decision_time]
|
||||
return sliced
|
||||
|
||||
# ── Private: trade execution ───────────────────────────────────
|
||||
|
||||
def _open_trade(
|
||||
self, report: SignalReport, next_bar: pd.Series, balance: float
|
||||
) -> Dict:
|
||||
"""Open at next bar's open, applying spread + slippage."""
|
||||
ts: TradeSetup = report.trade_setup # type: ignore[assignment]
|
||||
bt = self.config.backtest
|
||||
|
||||
raw_entry = float(next_bar["Open"])
|
||||
|
||||
# Sanity-test inversion: flip direction and mirror SL/TP around entry
|
||||
direction = ts.direction
|
||||
sl = ts.stop_loss
|
||||
tp = ts.take_profit_1
|
||||
if bt.invert_signal:
|
||||
direction = "SELL" if direction == "BUY" else "BUY"
|
||||
sl, tp = (2 * raw_entry - sl), (2 * raw_entry - tp)
|
||||
|
||||
# Cost in price units = (half-spread + slippage) × pip_value
|
||||
half_spread_pips = 0.5 if bt.use_spread_cost else 0.0
|
||||
cost_pips = half_spread_pips + bt.slippage_pips
|
||||
cost_price = cost_pips * PIP_VALUE
|
||||
|
||||
if direction == "BUY":
|
||||
entry_price = raw_entry + cost_price # buy higher (worse)
|
||||
else:
|
||||
entry_price = raw_entry - cost_price # sell lower (worse)
|
||||
|
||||
# Position sizing — risk a fixed % of current balance
|
||||
risk_pct = bt.risk_per_trade_pct / 100.0
|
||||
risk_amount = balance * risk_pct
|
||||
risk_pips = max(ts.risk_pips, 1e-9)
|
||||
pip_currency_value = risk_amount / risk_pips # $ per pip
|
||||
|
||||
return {
|
||||
"direction": direction,
|
||||
"entry_time": next_bar.name,
|
||||
"entry_price": entry_price,
|
||||
"stop_loss": sl,
|
||||
"take_profit": tp,
|
||||
"risk_pips": risk_pips,
|
||||
"pip_currency_value": pip_currency_value,
|
||||
"balance_at_open": balance,
|
||||
"grade": report.signal_quality,
|
||||
"score_at_entry": float(report.final_score),
|
||||
"confidence_at_entry": float(report.final_confidence),
|
||||
"bar_index_open": next_bar.name,
|
||||
}
|
||||
|
||||
def _check_exit(
|
||||
self, position: Dict, bar: pd.Series
|
||||
) -> Optional[Dict]:
|
||||
"""Return {price, reason} if SL or TP hit, else None.
|
||||
|
||||
On the entry bar (bar with same timestamp as position's entry_time),
|
||||
we already filled at the open — only check subsequent bars' OHLC.
|
||||
"""
|
||||
if bar.name == position["bar_index_open"]:
|
||||
return None # entry bar — no exit check
|
||||
|
||||
bt = self.config.backtest
|
||||
high = float(bar["High"])
|
||||
low = float(bar["Low"])
|
||||
|
||||
sl = position["stop_loss"]
|
||||
tp = position["take_profit"]
|
||||
slip = bt.slippage_pips * PIP_VALUE
|
||||
half_spread = (0.5 if bt.use_spread_cost else 0.0) * PIP_VALUE
|
||||
|
||||
if position["direction"] == "BUY":
|
||||
sl_hit = low <= sl
|
||||
tp_hit = high >= tp
|
||||
if sl_hit and tp_hit:
|
||||
# Conservative: SL first
|
||||
return {"price": sl - slip - half_spread, "reason": "sl"} if bt.sl_first_on_tie else \
|
||||
{"price": tp - slip - half_spread, "reason": "tp"}
|
||||
if sl_hit:
|
||||
return {"price": sl - slip - half_spread, "reason": "sl"}
|
||||
if tp_hit:
|
||||
return {"price": tp - slip - half_spread, "reason": "tp"}
|
||||
else: # SELL
|
||||
sl_hit = high >= sl
|
||||
tp_hit = low <= tp
|
||||
if sl_hit and tp_hit:
|
||||
return {"price": sl + slip + half_spread, "reason": "sl"} if bt.sl_first_on_tie else \
|
||||
{"price": tp + slip + half_spread, "reason": "tp"}
|
||||
if sl_hit:
|
||||
return {"price": sl + slip + half_spread, "reason": "sl"}
|
||||
if tp_hit:
|
||||
return {"price": tp + slip + half_spread, "reason": "tp"}
|
||||
return None
|
||||
|
||||
def _close_trade(
|
||||
self, position: Dict, exit_time: pd.Timestamp, exit_info: Dict, balance: float
|
||||
) -> Trade:
|
||||
exit_price = exit_info["price"]
|
||||
reason = exit_info["reason"]
|
||||
|
||||
if position["direction"] == "BUY":
|
||||
pnl_pips = (exit_price - position["entry_price"]) / PIP_VALUE
|
||||
else:
|
||||
pnl_pips = (position["entry_price"] - exit_price) / PIP_VALUE
|
||||
|
||||
pnl_currency = pnl_pips * position["pip_currency_value"]
|
||||
pnl_pct = pnl_currency / position["balance_at_open"] * 100
|
||||
|
||||
# Duration in primary-TF bars (timestamp diff / period)
|
||||
period_min = TF_MINUTES[self.config.primary_timeframe]
|
||||
duration_minutes = (exit_time - position["entry_time"]).total_seconds() / 60
|
||||
duration_bars = int(round(duration_minutes / period_min))
|
||||
|
||||
return Trade(
|
||||
entry_time=position["entry_time"],
|
||||
exit_time=exit_time,
|
||||
direction=position["direction"],
|
||||
entry_price=position["entry_price"],
|
||||
exit_price=exit_price,
|
||||
stop_loss=position["stop_loss"],
|
||||
take_profit=position["take_profit"],
|
||||
exit_reason=reason,
|
||||
risk_pips=position["risk_pips"],
|
||||
pnl_pips=pnl_pips,
|
||||
pnl_pct=pnl_pct,
|
||||
pnl_currency=pnl_currency,
|
||||
duration_bars=duration_bars,
|
||||
grade=position["grade"],
|
||||
score_at_entry=position["score_at_entry"],
|
||||
confidence_at_entry=position["confidence_at_entry"],
|
||||
)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
# PARAMETER OPTIMIZER (PARALLEL)
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
|
||||
def _apply_overrides(config: PlatformConfig, overrides: Dict[str, float]) -> PlatformConfig:
|
||||
"""Return a deep-copied config with parameter overrides applied."""
|
||||
new_config = copy.deepcopy(config)
|
||||
|
||||
if "atr_sl_multiplier" in overrides:
|
||||
new_config.signals.atr_sl_multiplier = float(overrides["atr_sl_multiplier"])
|
||||
if "default_rr_ratio" in overrides:
|
||||
new_config.signals.default_rr_ratio = float(overrides["default_rr_ratio"])
|
||||
if "min_trade_grade" in overrides:
|
||||
new_config.backtest.min_trade_grade = str(overrides["min_trade_grade"])
|
||||
if "min_abs_score" in overrides:
|
||||
new_config.backtest.min_abs_score = float(overrides["min_abs_score"])
|
||||
if "invert_signal" in overrides:
|
||||
new_config.backtest.invert_signal = bool(overrides["invert_signal"])
|
||||
if "regime_filter" in overrides:
|
||||
new_config.backtest.regime_filter = bool(overrides["regime_filter"])
|
||||
if "regime_filter_min_adx" in overrides:
|
||||
new_config.backtest.regime_filter_min_adx = float(overrides["regime_filter_min_adx"])
|
||||
if "risk_per_trade_pct" in overrides:
|
||||
new_config.backtest.risk_per_trade_pct = float(overrides["risk_per_trade_pct"])
|
||||
|
||||
return new_config
|
||||
|
||||
|
||||
def _run_single_combo(args: Tuple[PlatformConfig, Dict[str, float]]) -> BacktestResult:
|
||||
"""Worker function — must be top-level for multiprocessing.Pool."""
|
||||
base_config, overrides = args
|
||||
config = _apply_overrides(base_config, overrides)
|
||||
backtester = WalkForwardBacktester(config)
|
||||
# Workers reuse the disk parquet cache, so no Twelve Data fetch needed
|
||||
result = backtester.run(progress=False)
|
||||
result.params = overrides
|
||||
return result
|
||||
|
||||
|
||||
class Optimizer:
|
||||
"""Embarrassingly parallel grid search over BacktestConfig.param_grid."""
|
||||
|
||||
def __init__(self, config: PlatformConfig):
|
||||
self.config = config
|
||||
|
||||
def run_grid(
|
||||
self,
|
||||
param_grid: Optional[Dict[str, List[float]]] = None,
|
||||
prewarm_cache: bool = True,
|
||||
) -> List[BacktestResult]:
|
||||
if param_grid is None:
|
||||
param_grid = self.config.backtest.param_grid
|
||||
|
||||
# Prewarm the disk cache once before fanning out — avoids each worker
|
||||
# re-fetching from Twelve Data.
|
||||
if prewarm_cache:
|
||||
logger.info("Prewarming disk cache for backtest data...")
|
||||
warmer = WalkForwardBacktester(self.config)
|
||||
warmer.load_data()
|
||||
|
||||
keys = list(param_grid.keys())
|
||||
value_lists = [param_grid[k] for k in keys]
|
||||
combos = [
|
||||
dict(zip(keys, vals))
|
||||
for vals in itertools.product(*value_lists)
|
||||
]
|
||||
|
||||
n_workers = min(
|
||||
self.config.backtest.parallel_workers,
|
||||
len(combos),
|
||||
os.cpu_count() or 1,
|
||||
)
|
||||
logger.info(
|
||||
f"Optimizer: {len(combos)} combos × {n_workers} workers"
|
||||
)
|
||||
|
||||
tasks = [(self.config, c) for c in combos]
|
||||
|
||||
if n_workers <= 1:
|
||||
results = [_run_single_combo(t) for t in tasks]
|
||||
else:
|
||||
with mp.Pool(processes=n_workers) as pool:
|
||||
results = pool.map(_run_single_combo, tasks)
|
||||
|
||||
# Sort by total return descending
|
||||
results.sort(
|
||||
key=lambda r: r.metrics.get("total_return_pct", -float("inf")),
|
||||
reverse=True,
|
||||
)
|
||||
return results
|
||||
@@ -1,13 +1,15 @@
|
||||
"""
|
||||
Forex Signal Intelligence Platform - Configuration
|
||||
===================================================
|
||||
Central configuration for all signal analysis parameters.
|
||||
Tuned by market regime and validated against historical performance.
|
||||
ForexBot2 — Configuration (USD/JPY focus)
|
||||
==========================================
|
||||
Single-pair signal & backtest configuration.
|
||||
All knobs that govern indicator behavior, signal scoring, news handling,
|
||||
and the walk-forward backtester live here.
|
||||
"""
|
||||
|
||||
import os
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import Dict, List
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
|
||||
class Timeframe(Enum):
|
||||
@@ -37,160 +39,160 @@ class MarketSession(Enum):
|
||||
OVERLAP_LONDON_NY = "london_ny_overlap"
|
||||
|
||||
|
||||
# ── Major Forex Pairs ──────────────────────────────────────────────
|
||||
MAJOR_PAIRS = [
|
||||
"EURUSD=X", "GBPUSD=X", "USDJPY=X", "USDCHF=X",
|
||||
"AUDUSD=X", "USDCAD=X", "NZDUSD=X",
|
||||
]
|
||||
|
||||
CROSS_PAIRS = [
|
||||
"EURGBP=X", "EURJPY=X", "GBPJPY=X", "AUDJPY=X",
|
||||
"EURCHF=X", "CADJPY=X",
|
||||
]
|
||||
|
||||
# Yahoo Finance symbol mapping for display
|
||||
SYMBOL_DISPLAY = {
|
||||
"EURUSD=X": "EUR/USD", "GBPUSD=X": "GBP/USD",
|
||||
"USDJPY=X": "USD/JPY", "USDCHF=X": "USD/CHF",
|
||||
"AUDUSD=X": "AUD/USD", "USDCAD=X": "USD/CAD",
|
||||
"NZDUSD=X": "NZD/USD", "EURGBP=X": "EUR/GBP",
|
||||
"EURJPY=X": "EUR/JPY", "GBPJPY=X": "GBP/JPY",
|
||||
"AUDJPY=X": "AUD/JPY", "EURCHF=X": "EUR/CHF",
|
||||
"CADJPY=X": "CAD/JPY",
|
||||
}
|
||||
# ── Single Focus: USD/JPY ─────────────────────────────────────────
|
||||
USDJPY = "USDJPY=X"
|
||||
DISPLAY = "USD/JPY"
|
||||
PIP_VALUE = 0.01 # JPY pairs use 0.01 per pip
|
||||
|
||||
|
||||
@dataclass
|
||||
class TechnicalConfig:
|
||||
"""Technical indicator parameters - carefully tuned for Forex."""
|
||||
# EMA periods (fast/medium/slow for trend detection)
|
||||
"""Technical indicator parameters."""
|
||||
ema_fast: int = 9
|
||||
ema_medium: int = 21
|
||||
ema_slow: int = 50
|
||||
ema_trend: int = 200
|
||||
|
||||
# RSI
|
||||
rsi_period: int = 14
|
||||
rsi_overbought: float = 70.0
|
||||
rsi_oversold: float = 30.0
|
||||
rsi_neutral_upper: float = 60.0
|
||||
rsi_neutral_lower: float = 40.0
|
||||
|
||||
# MACD (12, 26, 9 is standard)
|
||||
macd_fast: int = 12
|
||||
macd_slow: int = 26
|
||||
macd_signal: int = 9
|
||||
|
||||
# Bollinger Bands
|
||||
bb_period: int = 20
|
||||
bb_std: float = 2.0
|
||||
|
||||
# ATR for volatility measurement
|
||||
atr_period: int = 14
|
||||
|
||||
# Stochastic
|
||||
stoch_k: int = 14
|
||||
stoch_d: int = 3
|
||||
stoch_overbought: float = 80.0
|
||||
stoch_oversold: float = 20.0
|
||||
|
||||
# ADX for trend strength
|
||||
adx_period: int = 14
|
||||
adx_strong_trend: float = 25.0
|
||||
adx_very_strong: float = 50.0
|
||||
|
||||
# Ichimoku
|
||||
ichimoku_tenkan: int = 9
|
||||
ichimoku_kijun: int = 26
|
||||
ichimoku_senkou_b: int = 52
|
||||
|
||||
# Volume profile
|
||||
volume_ma_period: int = 20
|
||||
|
||||
# Pivot points lookback
|
||||
pivot_lookback: int = 1
|
||||
|
||||
# Divergence detection — how many bars back to scan for swing points
|
||||
divergence_lookback: int = 14
|
||||
|
||||
|
||||
@dataclass
|
||||
class SignalConfig:
|
||||
"""Signal scoring and filtering parameters."""
|
||||
# Minimum confidence to emit a signal (0-100)
|
||||
"""Signal scoring & filtering."""
|
||||
min_confidence: float = 55.0
|
||||
|
||||
# Weight allocation for composite score
|
||||
weights: Dict[str, float] = field(default_factory=lambda: {
|
||||
"trend": 0.25, # EMA alignment, ADX
|
||||
"momentum": 0.20, # RSI, Stochastic, MACD
|
||||
"volatility": 0.15, # BB position, ATR regime
|
||||
"price_action": 0.15, # Candlestick patterns, S/R
|
||||
"multi_timeframe": 0.15,# Higher TF confirmation
|
||||
"sentiment": 0.10, # News/calendar events
|
||||
"trend": 0.25,
|
||||
"momentum": 0.20,
|
||||
"volatility": 0.15,
|
||||
"price_action": 0.15,
|
||||
"multi_timeframe": 0.15,
|
||||
"sentiment": 0.10,
|
||||
})
|
||||
|
||||
# Multi-timeframe confirmation requirements
|
||||
mtf_agreement_threshold: int = 2 # out of 3 timeframes must agree
|
||||
|
||||
# Signal cooldown (prevent over-signalling same direction)
|
||||
mtf_agreement_threshold: int = 2 # of 3 timeframes
|
||||
cooldown_bars: int = 5
|
||||
|
||||
# Risk management defaults
|
||||
default_risk_pct: float = 1.0 # risk 1% per trade
|
||||
default_rr_ratio: float = 2.0 # 1:2 risk-reward minimum
|
||||
default_risk_pct: float = 1.0
|
||||
default_rr_ratio: float = 2.0
|
||||
atr_sl_multiplier: float = 1.5 # SL distance = 1.5 × ATR
|
||||
|
||||
|
||||
@dataclass
|
||||
class NewsConfig:
|
||||
"""Economic calendar and sentiment configuration."""
|
||||
# High-impact event currencies
|
||||
tracked_currencies: List[str] = field(default_factory=lambda: [
|
||||
"USD", "EUR", "GBP", "JPY", "CHF", "AUD", "CAD", "NZD"
|
||||
])
|
||||
|
||||
# Event impact levels to monitor
|
||||
min_impact: str = "medium" # "low", "medium", "high"
|
||||
|
||||
# Pre-event buffer (minutes before high-impact event = no new signals)
|
||||
"""Economic calendar & sentiment."""
|
||||
tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "JPY"])
|
||||
min_impact: str = "medium"
|
||||
pre_event_buffer_minutes: int = 30
|
||||
|
||||
# Post-event volatility window (minutes after event = cautious signals)
|
||||
post_event_buffer_minutes: int = 15
|
||||
|
||||
# News sentiment sources
|
||||
rss_feeds: List[str] = field(default_factory=lambda: [
|
||||
"https://www.forexlive.com/feed/",
|
||||
"https://www.fxstreet.com/rss",
|
||||
])
|
||||
|
||||
|
||||
@dataclass
|
||||
class BacktestConfig:
|
||||
"""Walk-forward backtest configuration."""
|
||||
# Data window — None means "all available history"
|
||||
start_date: Optional[str] = None # "YYYY-MM-DD"
|
||||
end_date: Optional[str] = None # "YYYY-MM-DD"
|
||||
max_bars: int = 5000 # Twelve Data ceiling per request
|
||||
|
||||
# Account & sizing
|
||||
initial_balance: float = 10_000.0
|
||||
risk_per_trade_pct: float = 1.0 # % of equity risked per trade
|
||||
|
||||
# Execution realism
|
||||
use_spread_cost: bool = True
|
||||
slippage_pips: float = 0.5 # added to spread on entry & exit
|
||||
one_position_only: bool = True
|
||||
sl_first_on_tie: bool = True # if SL & TP both inside bar, SL hits first
|
||||
|
||||
# Signal gates
|
||||
min_trade_grade: str = "B" # "A+", "A", "B", "C", "D", "F"
|
||||
min_abs_score: float = 0.0 # |final_score| must exceed this to enter
|
||||
|
||||
# Sanity-test knob — flip BUY↔SELL to see if the engine has reversed edge
|
||||
invert_signal: bool = False
|
||||
|
||||
# Regime gate — only trade when a higher-TF trend is confirmed
|
||||
regime_filter: bool = False
|
||||
regime_filter_timeframe: Timeframe = Timeframe.H4
|
||||
regime_filter_min_adx: float = 25.0
|
||||
|
||||
# Parallelism (used by Optimizer; single-run backtest is sequential)
|
||||
parallel_workers: int = field(default_factory=lambda: os.cpu_count() or 4)
|
||||
|
||||
# Optimizer
|
||||
optimize: bool = False
|
||||
param_grid: Dict[str, list] = field(default_factory=lambda: {
|
||||
# 2 × 2 × 3 × 2 × 2 = 48 combos
|
||||
"atr_sl_multiplier": [1.5, 2.5],
|
||||
"default_rr_ratio": [1.5, 2.5],
|
||||
"min_abs_score": [15.0, 35.0, 60.0],
|
||||
"min_trade_grade": ["B", "A"],
|
||||
"invert_signal": [False, True],
|
||||
})
|
||||
|
||||
# Cache directory for fetched OHLCV (parquet)
|
||||
cache_dir: str = ".cache"
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlatformConfig:
|
||||
"""Overall platform configuration."""
|
||||
"""Top-level configuration."""
|
||||
technical: TechnicalConfig = field(default_factory=TechnicalConfig)
|
||||
signals: SignalConfig = field(default_factory=SignalConfig)
|
||||
news: NewsConfig = field(default_factory=NewsConfig)
|
||||
backtest: BacktestConfig = field(default_factory=BacktestConfig)
|
||||
|
||||
# Default pairs to monitor
|
||||
watch_pairs: List[str] = field(default_factory=lambda: MAJOR_PAIRS)
|
||||
symbol: str = USDJPY
|
||||
display_name: str = DISPLAY
|
||||
pip_value: float = PIP_VALUE
|
||||
|
||||
# Analysis timeframes (primary + confirmation)
|
||||
primary_timeframe: Timeframe = Timeframe.H1
|
||||
confirmation_timeframes: List[Timeframe] = field(
|
||||
default_factory=lambda: [Timeframe.M15, Timeframe.H4]
|
||||
)
|
||||
|
||||
# Data source
|
||||
data_lookback_days: int = 60 # enough for 200 EMA on H1
|
||||
|
||||
# Twelve Data API key (overridden by TWELVE_DATA_API_KEY env var)
|
||||
twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
|
||||
|
||||
# Dashboard refresh interval (seconds)
|
||||
refresh_interval: int = 300 # 5 minutes
|
||||
refresh_interval: int = 300 # 5 min cache TTL
|
||||
|
||||
# Logging
|
||||
log_level: str = "INFO"
|
||||
log_signals_to_file: bool = True
|
||||
signal_log_path: str = "signals.log"
|
||||
|
||||
+105
-302
@@ -1,23 +1,22 @@
|
||||
"""
|
||||
Forex Signal Intelligence Platform - Data Provider
|
||||
====================================================
|
||||
Retrieves and validates market data from Twelve Data API.
|
||||
Falls back to synthetic GBM data if the API is unavailable.
|
||||
ForexBot2 — Data Provider (Twelve Data, USD/JPY only)
|
||||
=======================================================
|
||||
Fetches and validates OHLCV from Twelve Data. No synthetic fallback —
|
||||
if the API fails the caller learns about it. Adds a parquet on-disk
|
||||
cache so the backtest worker pool reuses one download instead of
|
||||
hammering Twelve Data 27 times during a parameter sweep.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from collections import deque
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import Dict, List, Optional
|
||||
from typing import Dict, Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from config import PlatformConfig, Timeframe, SYMBOL_DISPLAY
|
||||
from config import PlatformConfig, Timeframe, USDJPY, DISPLAY, PIP_VALUE
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -28,12 +27,7 @@ class DataIntegrityError(Exception):
|
||||
|
||||
|
||||
class ForexDataProvider:
|
||||
"""
|
||||
Forex data provider backed by Twelve Data API.
|
||||
|
||||
Twelve Data has native 4h candles, eliminating the H1→H4 resampling
|
||||
hack needed for Yahoo Finance. Free tier: 800 credits/day, 8 req/min.
|
||||
"""
|
||||
"""Twelve Data REST client + on-disk parquet cache."""
|
||||
|
||||
TD_BASE_URL = "https://api.twelvedata.com"
|
||||
|
||||
@@ -48,7 +42,6 @@ class ForexDataProvider:
|
||||
Timeframe.W1: "1week",
|
||||
}
|
||||
|
||||
# Approximate bars per day for each timeframe (used to size outputsize)
|
||||
BARS_PER_DAY = {
|
||||
Timeframe.M1: 1440,
|
||||
Timeframe.M5: 288,
|
||||
@@ -60,7 +53,7 @@ class ForexDataProvider:
|
||||
Timeframe.W1: 0.143,
|
||||
}
|
||||
|
||||
# Free plan: 8 API credits per minute (each symbol = 1 credit)
|
||||
# Free plan: 8 credits / minute
|
||||
RATE_LIMIT = 8
|
||||
RATE_WINDOW = 60.0
|
||||
|
||||
@@ -72,7 +65,7 @@ class ForexDataProvider:
|
||||
)
|
||||
self._cache: Dict[str, pd.DataFrame] = {}
|
||||
self._cache_timestamps: Dict[str, float] = {}
|
||||
self._request_times: deque = deque() # timestamps of recent API credits used
|
||||
self._request_times: deque = deque()
|
||||
self._session = requests.Session()
|
||||
self._session.headers.update({
|
||||
"User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)"
|
||||
@@ -80,22 +73,18 @@ class ForexDataProvider:
|
||||
|
||||
@staticmethod
|
||||
def _to_td_symbol(symbol: str) -> str:
|
||||
"""Convert internal symbol (EURUSD=X) to Twelve Data format (EUR/USD)."""
|
||||
clean = symbol.replace("=X", "")
|
||||
return f"{clean[:3]}/{clean[3:]}"
|
||||
|
||||
# ── Public API ─────────────────────────────────────────────────
|
||||
|
||||
def fetch_ohlcv(
|
||||
self,
|
||||
symbol: str,
|
||||
timeframe: Timeframe,
|
||||
symbol: str = USDJPY,
|
||||
timeframe: Timeframe = Timeframe.H1,
|
||||
lookback_days: Optional[int] = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Fetch OHLCV data for a Forex pair with full validation.
|
||||
|
||||
Returns a DataFrame with columns: Open, High, Low, Close, Volume
|
||||
Index is a timezone-aware DatetimeIndex in UTC.
|
||||
"""
|
||||
"""Fetch OHLCV with in-memory caching for live mode."""
|
||||
if lookback_days is None:
|
||||
lookback_days = self.config.data_lookback_days
|
||||
|
||||
@@ -111,55 +100,63 @@ class ForexDataProvider:
|
||||
if cached is not None and not cached.empty:
|
||||
return cached
|
||||
|
||||
df = self._fetch_twelve_data(symbol, timeframe, lookback_days)
|
||||
outputsize = max(
|
||||
300,
|
||||
min(int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000),
|
||||
)
|
||||
df = self._fetch_twelve_data(symbol, timeframe, outputsize)
|
||||
df = self._validate_and_clean(df, symbol, timeframe)
|
||||
|
||||
self._cache[cache_key] = df
|
||||
self._cache_timestamps[cache_key] = now
|
||||
return df
|
||||
|
||||
def prefetch_batch(
|
||||
def fetch_full_history(
|
||||
self,
|
||||
symbols: List[str],
|
||||
timeframes: List[Timeframe],
|
||||
lookback_days: Optional[int] = None,
|
||||
) -> None:
|
||||
symbol: str = USDJPY,
|
||||
timeframe: Timeframe = Timeframe.H1,
|
||||
max_bars: int = 5000,
|
||||
use_disk_cache: bool = True,
|
||||
start_date: Optional[str] = None,
|
||||
end_date: Optional[str] = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Batch-fetch all symbols for each timeframe in a single API call per TF.
|
||||
|
||||
Twelve Data accepts comma-separated symbols, so 7 pairs × 3 timeframes
|
||||
= 3 requests instead of 21 — well within the free-tier 8 req/min limit.
|
||||
Results are stored in the local cache; subsequent fetch_ohlcv calls
|
||||
for the same symbol/TF will be served from cache.
|
||||
Pull the largest window Twelve Data allows in one call.
|
||||
On-disk parquet cache prevents re-download across worker subprocesses
|
||||
during an optimizer sweep.
|
||||
"""
|
||||
if lookback_days is None:
|
||||
lookback_days = self.config.data_lookback_days
|
||||
cache_path = self._disk_cache_path(symbol, timeframe, start_date, end_date)
|
||||
|
||||
for tf in timeframes:
|
||||
if use_disk_cache and os.path.exists(cache_path):
|
||||
try:
|
||||
self._batch_fetch_timeframe(symbols, tf, lookback_days)
|
||||
time.sleep(1.0) # brief pause between TF requests
|
||||
df = pd.read_parquet(cache_path)
|
||||
if df.index.tz is None:
|
||||
df.index = df.index.tz_localize("UTC")
|
||||
logger.info(
|
||||
f"Loaded {len(df)} bars from disk cache: {cache_path}"
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
logger.warning(f"Batch prefetch failed for {tf.value}: {e}")
|
||||
logger.warning(f"Disk cache read failed ({e}); refetching.")
|
||||
|
||||
def fetch_multiple(
|
||||
self,
|
||||
symbols: List[str],
|
||||
timeframe: Timeframe,
|
||||
lookback_days: Optional[int] = None,
|
||||
) -> Dict[str, pd.DataFrame]:
|
||||
"""Fetch data for multiple symbols, returning dict of DataFrames."""
|
||||
results = {}
|
||||
for symbol in symbols:
|
||||
outputsize = min(max_bars, 5000)
|
||||
df = self._fetch_twelve_data(
|
||||
symbol, timeframe, outputsize,
|
||||
start_date=start_date, end_date=end_date,
|
||||
)
|
||||
df = self._validate_and_clean(df, symbol, timeframe)
|
||||
|
||||
if use_disk_cache:
|
||||
os.makedirs(os.path.dirname(cache_path), exist_ok=True)
|
||||
try:
|
||||
df = self.fetch_ohlcv(symbol, timeframe, lookback_days)
|
||||
results[symbol] = df
|
||||
df.to_parquet(cache_path)
|
||||
logger.info(f"Cached {len(df)} bars → {cache_path}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to fetch {symbol}: {e}")
|
||||
return results
|
||||
logger.warning(f"Disk cache write failed: {e}")
|
||||
|
||||
def get_latest_price(self, symbol: str) -> Optional[float]:
|
||||
"""Return the most recent close price for a symbol."""
|
||||
return df
|
||||
|
||||
def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
|
||||
try:
|
||||
df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
|
||||
if df is not None and not df.empty:
|
||||
@@ -168,47 +165,47 @@ class ForexDataProvider:
|
||||
pass
|
||||
return None
|
||||
|
||||
def get_spread_estimate(self, symbol: str) -> float:
|
||||
"""Estimate typical spread from recent M5 high-low ranges."""
|
||||
def get_spread_estimate(self, symbol: str = USDJPY) -> float:
|
||||
"""Estimate spread from recent M5 H-L median; fallback to 1 pip on JPY."""
|
||||
try:
|
||||
df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
|
||||
if df is not None and not df.empty:
|
||||
return float((df["High"] - df["Low"]).median())
|
||||
except Exception:
|
||||
pass
|
||||
# Fallback: 1 pip = 0.01 for JPY pairs
|
||||
return PIP_VALUE
|
||||
|
||||
fallback_spreads = {
|
||||
"EURUSD=X": 0.00010, "GBPUSD=X": 0.00015,
|
||||
"USDJPY=X": 0.010, "USDCHF=X": 0.00015,
|
||||
"AUDUSD=X": 0.00015, "USDCAD=X": 0.00018,
|
||||
"NZDUSD=X": 0.00020,
|
||||
}
|
||||
return fallback_spreads.get(symbol, 0.00020)
|
||||
|
||||
def get_pair_info(self, symbol: str) -> Dict:
|
||||
"""Return display name and pip value for a pair."""
|
||||
display = SYMBOL_DISPLAY.get(symbol, symbol)
|
||||
is_jpy = "JPY" in symbol
|
||||
def get_pair_info(self, symbol: str = USDJPY) -> Dict:
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"display": display,
|
||||
"pip_value": 0.01 if is_jpy else 0.0001,
|
||||
"is_jpy_pair": is_jpy,
|
||||
"display": DISPLAY,
|
||||
"pip_value": PIP_VALUE,
|
||||
"is_jpy_pair": True,
|
||||
}
|
||||
|
||||
# ── Private Methods ────────────────────────────────────────────
|
||||
# ── Private ────────────────────────────────────────────────────
|
||||
|
||||
def _disk_cache_path(
|
||||
self,
|
||||
symbol: str,
|
||||
timeframe: Timeframe,
|
||||
start_date: Optional[str] = None,
|
||||
end_date: Optional[str] = None,
|
||||
) -> str:
|
||||
clean = symbol.replace("=X", "").lower()
|
||||
suffix = ""
|
||||
if start_date or end_date:
|
||||
suffix = f"_{start_date or 'min'}_{end_date or 'max'}"
|
||||
return os.path.join(
|
||||
self.config.backtest.cache_dir,
|
||||
f"{clean}_{timeframe.value}{suffix}.parquet",
|
||||
)
|
||||
|
||||
def _throttle(self, credits: int = 1) -> None:
|
||||
"""
|
||||
Block until `credits` API credits are available.
|
||||
|
||||
Twelve Data free tier: 8 credits/minute. Each symbol in any request
|
||||
(single or batch) consumes 1 credit. This method enforces the limit
|
||||
regardless of whether requests are batched or individual.
|
||||
"""
|
||||
"""Free tier: 8 credits / minute. Block until budget allows."""
|
||||
while True:
|
||||
now = time.time()
|
||||
# Evict credits older than the rate window
|
||||
while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
|
||||
self._request_times.popleft()
|
||||
|
||||
@@ -218,120 +215,32 @@ class ForexDataProvider:
|
||||
self._request_times.append(time.time())
|
||||
return
|
||||
|
||||
# Wait until the oldest credit expires
|
||||
wait = (self._request_times[0] + self.RATE_WINDOW) - now + 0.2
|
||||
logger.info(
|
||||
f"Rate limit: {len(self._request_times)}/{self.RATE_LIMIT} credits used "
|
||||
f"— waiting {wait:.1f}s for next window..."
|
||||
f"— waiting {wait:.1f}s..."
|
||||
)
|
||||
time.sleep(wait)
|
||||
|
||||
def _batch_fetch_timeframe(
|
||||
self, symbols: List[str], timeframe: Timeframe, lookback_days: int
|
||||
) -> None:
|
||||
"""
|
||||
Single API call fetching all symbols for one timeframe.
|
||||
Twelve Data returns a dict keyed by symbol when multiple are requested.
|
||||
Parsed DataFrames are validated and stored in the cache.
|
||||
"""
|
||||
if not self._api_key:
|
||||
return
|
||||
|
||||
td_symbols = [self._to_td_symbol(s) for s in symbols]
|
||||
interval = self.TD_INTERVALS[timeframe]
|
||||
outputsize = min(
|
||||
int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000
|
||||
)
|
||||
outputsize = max(outputsize, 300)
|
||||
|
||||
# Each symbol in a batch request costs 1 credit
|
||||
self._throttle(credits=len(symbols))
|
||||
|
||||
resp = self._session.get(
|
||||
f"{self.TD_BASE_URL}/time_series",
|
||||
params={
|
||||
"symbol": ",".join(td_symbols),
|
||||
"interval": interval,
|
||||
"outputsize": outputsize,
|
||||
"apikey": self._api_key,
|
||||
"timezone": "UTC",
|
||||
"format": "json",
|
||||
},
|
||||
timeout=30,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
# If the whole batch request returned a top-level error, raise it
|
||||
if isinstance(data, dict) and data.get("status") == "error":
|
||||
raise DataIntegrityError(
|
||||
f"Batch API error ({timeframe.value}): {data.get('message', 'unknown')}"
|
||||
)
|
||||
|
||||
# Batch response: {"EUR/USD": {...}, "GBP/USD": {...}, ...}
|
||||
now = time.time()
|
||||
for symbol, td_sym in zip(symbols, td_symbols):
|
||||
try:
|
||||
sym_data = data.get(td_sym, {})
|
||||
if sym_data.get("status") == "error":
|
||||
logger.warning(
|
||||
f"Batch fetch error for {td_sym}: {sym_data.get('message')}"
|
||||
)
|
||||
continue
|
||||
|
||||
values = sym_data.get("values")
|
||||
if not values:
|
||||
logger.warning(f"No values in batch response for {td_sym}")
|
||||
continue
|
||||
|
||||
records = [
|
||||
{
|
||||
"Datetime": pd.Timestamp(v["datetime"], tz="UTC"),
|
||||
"Open": float(v["open"]),
|
||||
"High": float(v["high"]),
|
||||
"Low": float(v["low"]),
|
||||
"Close": float(v["close"]),
|
||||
"Volume": float(v.get("volume") or 0),
|
||||
}
|
||||
for v in reversed(values)
|
||||
]
|
||||
df = pd.DataFrame(records).set_index("Datetime")
|
||||
df.index.name = "Datetime"
|
||||
df = self._validate_and_clean(df, symbol, timeframe)
|
||||
|
||||
cache_key = f"{symbol}_{timeframe.value}"
|
||||
self._cache[cache_key] = df
|
||||
self._cache_timestamps[cache_key] = now
|
||||
logger.debug(f"Batch cached {len(df)} bars for {td_sym} @ {interval}")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse batch data for {td_sym}: {e}")
|
||||
|
||||
def _fetch_twelve_data(
|
||||
self, symbol: str, timeframe: Timeframe, lookback_days: int
|
||||
self,
|
||||
symbol: str,
|
||||
timeframe: Timeframe,
|
||||
outputsize: int,
|
||||
start_date: Optional[str] = None,
|
||||
end_date: Optional[str] = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Fetch OHLCV from Twelve Data REST API.
|
||||
Falls back to synthetic GBM data if API is unavailable or key is missing.
|
||||
"""
|
||||
if not self._api_key:
|
||||
logger.warning("No Twelve Data API key — using synthetic data. "
|
||||
"Set TWELVE_DATA_API_KEY env var or config.twelve_data_api_key.")
|
||||
return self._generate_synthetic_data(symbol, timeframe, lookback_days)
|
||||
raise DataIntegrityError(
|
||||
"No Twelve Data API key. Set TWELVE_DATA_API_KEY env var "
|
||||
"or config.twelve_data_api_key."
|
||||
)
|
||||
|
||||
td_symbol = self._to_td_symbol(symbol)
|
||||
interval = self.TD_INTERVALS[timeframe]
|
||||
outputsize = min(
|
||||
int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20,
|
||||
5000,
|
||||
)
|
||||
outputsize = max(outputsize, 300)
|
||||
|
||||
self._throttle(credits=1)
|
||||
|
||||
try:
|
||||
resp = self._session.get(
|
||||
f"{self.TD_BASE_URL}/time_series",
|
||||
params = {
|
||||
"symbol": td_symbol,
|
||||
"interval": interval,
|
||||
@@ -339,22 +248,29 @@ class ForexDataProvider:
|
||||
"apikey": self._api_key,
|
||||
"timezone": "UTC",
|
||||
"format": "json",
|
||||
},
|
||||
timeout=15,
|
||||
}
|
||||
if start_date:
|
||||
params["start_date"] = start_date
|
||||
if end_date:
|
||||
params["end_date"] = end_date
|
||||
|
||||
resp = self._session.get(
|
||||
f"{self.TD_BASE_URL}/time_series",
|
||||
params=params,
|
||||
timeout=30,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
if data.get("status") == "error":
|
||||
raise DataIntegrityError(
|
||||
f"Twelve Data API error for {td_symbol}: {data.get('message', 'unknown')}"
|
||||
f"Twelve Data error for {td_symbol}: {data.get('message', 'unknown')}"
|
||||
)
|
||||
|
||||
values = data.get("values")
|
||||
if not values:
|
||||
raise DataIntegrityError(f"Empty response from Twelve Data for {td_symbol}")
|
||||
|
||||
# Values arrive newest-first; reverse to chronological order
|
||||
records = [
|
||||
{
|
||||
"Datetime": pd.Timestamp(v["datetime"], tz="UTC"),
|
||||
@@ -372,122 +288,9 @@ class ForexDataProvider:
|
||||
logger.debug(f"Fetched {len(df)} bars for {td_symbol} @ {interval}")
|
||||
return df
|
||||
|
||||
except DataIntegrityError:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.info(f"Twelve Data fetch failed for {symbol} ({e}), using synthetic data.")
|
||||
return self._generate_synthetic_data(symbol, timeframe, lookback_days)
|
||||
|
||||
def _generate_synthetic_data(
|
||||
self, symbol: str, timeframe: Timeframe, lookback_days: int
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Generate realistic synthetic Forex OHLCV using GBM with mean-reversion.
|
||||
Used as a fallback when live data is unavailable.
|
||||
"""
|
||||
pair_params = {
|
||||
"EURUSD=X": {"price": 1.1350, "vol": 0.07, "drift": -0.001},
|
||||
"GBPUSD=X": {"price": 1.3200, "vol": 0.08, "drift": 0.002},
|
||||
"USDJPY=X": {"price": 144.50, "vol": 0.09, "drift": 0.003},
|
||||
"USDCHF=X": {"price": 0.8850, "vol": 0.07, "drift": -0.001},
|
||||
"AUDUSD=X": {"price": 0.6550, "vol": 0.10, "drift": -0.002},
|
||||
"USDCAD=X": {"price": 1.3650, "vol": 0.07, "drift": 0.001},
|
||||
"NZDUSD=X": {"price": 0.6100, "vol": 0.10, "drift": -0.001},
|
||||
"EURGBP=X": {"price": 0.8590, "vol": 0.06, "drift": 0.000},
|
||||
"EURJPY=X": {"price": 163.90, "vol": 0.09, "drift": 0.002},
|
||||
"GBPJPY=X": {"price": 190.70, "vol": 0.11, "drift": 0.003},
|
||||
"AUDJPY=X": {"price": 94.60, "vol": 0.11, "drift": 0.001},
|
||||
"EURCHF=X": {"price": 0.9380, "vol": 0.05, "drift": 0.000},
|
||||
"CADJPY=X": {"price": 105.80, "vol": 0.09, "drift": 0.001},
|
||||
}
|
||||
|
||||
params = pair_params.get(symbol, {"price": 1.0000, "vol": 0.08, "drift": 0.000})
|
||||
base_price = params["price"]
|
||||
annual_vol = params["vol"]
|
||||
annual_drift = params["drift"]
|
||||
|
||||
tf_minutes = {
|
||||
Timeframe.M1: 1, Timeframe.M5: 5, Timeframe.M15: 15,
|
||||
Timeframe.M30: 30, Timeframe.H1: 60, Timeframe.H4: 240,
|
||||
Timeframe.D1: 1440, Timeframe.W1: 10080,
|
||||
}
|
||||
bar_minutes = tf_minutes.get(timeframe, 60)
|
||||
bars_per_year = (365.25 * 24 * 60) / bar_minutes
|
||||
dt = 1.0 / bars_per_year
|
||||
|
||||
n_bars = min(int(lookback_days * 24 * 60 / bar_minutes), 2000)
|
||||
n_bars = max(n_bars, 300)
|
||||
|
||||
seed_base = int(hashlib.md5(symbol.encode()).hexdigest()[:8], 16)
|
||||
time_component = int(time.time()) // 3600
|
||||
np.random.seed((seed_base + time_component) % (2**31))
|
||||
|
||||
bar_vol = annual_vol * np.sqrt(dt)
|
||||
bar_drift = annual_drift * dt
|
||||
|
||||
regime_len = np.random.randint(20, 80)
|
||||
trend_strength = np.random.normal(0, 0.3)
|
||||
|
||||
price = base_price
|
||||
prices = [price]
|
||||
|
||||
for i in range(1, n_bars):
|
||||
if i % regime_len == 0:
|
||||
regime_len = np.random.randint(20, 80)
|
||||
trend_strength = np.random.normal(0, 0.3)
|
||||
|
||||
mr_force = -0.001 * (price - base_price) / base_price
|
||||
ret = bar_drift + trend_strength * dt + mr_force + bar_vol * np.random.randn()
|
||||
|
||||
if np.random.random() < 0.02:
|
||||
ret *= np.random.uniform(2.0, 4.0)
|
||||
|
||||
price *= (1 + ret)
|
||||
prices.append(price)
|
||||
|
||||
prices = np.array(prices)
|
||||
opens = np.roll(prices, 1)
|
||||
opens[0] = prices[0]
|
||||
|
||||
typical_range = bar_vol * base_price * 1.5
|
||||
highs = np.maximum(prices, opens) + np.abs(np.random.normal(0, typical_range * 0.6, n_bars))
|
||||
lows = np.minimum(prices, opens) - np.abs(np.random.normal(0, typical_range * 0.6, n_bars))
|
||||
highs = np.maximum(highs, np.maximum(opens, prices))
|
||||
lows = np.minimum(lows, np.minimum(opens, prices))
|
||||
|
||||
volume = np.random.lognormal(mean=10, sigma=0.5, size=n_bars).astype(int)
|
||||
|
||||
freq_map = {
|
||||
Timeframe.M1: "1min", Timeframe.M5: "5min",
|
||||
Timeframe.M15: "15min", Timeframe.M30: "30min",
|
||||
Timeframe.H1: "1h", Timeframe.H4: "4h",
|
||||
Timeframe.D1: "D", Timeframe.W1: "W",
|
||||
}
|
||||
freq = freq_map.get(timeframe, "1h")
|
||||
end_time = pd.Timestamp.now(tz="UTC").floor(f"{bar_minutes}min")
|
||||
index = pd.date_range(end=end_time, periods=n_bars, freq=freq, tz="UTC")
|
||||
|
||||
df = pd.DataFrame({
|
||||
"Open": opens, "High": highs, "Low": lows,
|
||||
"Close": prices, "Volume": volume,
|
||||
}, index=index)
|
||||
df.index.name = "Datetime"
|
||||
|
||||
logger.info(
|
||||
f"Generated {n_bars} synthetic {timeframe.value} bars for "
|
||||
f"{SYMBOL_DISPLAY.get(symbol, symbol)} @ {prices[-1]:.5f}"
|
||||
)
|
||||
return df
|
||||
|
||||
def _validate_and_clean(
|
||||
self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Validate and clean OHLCV data.
|
||||
|
||||
Steps: drop all-NaN rows → ffill small gaps → remove future timestamps
|
||||
→ fix OHLC inconsistencies → remove >10-sigma outliers → sort & deduplicate.
|
||||
"""
|
||||
if df is None or df.empty:
|
||||
raise DataIntegrityError(f"Empty dataset for {symbol}")
|
||||
|
||||
|
||||
@@ -1,35 +1,31 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Forex Signal Intelligence Platform
|
||||
====================================
|
||||
Main entry point. Runs full analysis and outputs formatted signal reports.
|
||||
ForexBot2 — USD/JPY Signal Intelligence (single-pair)
|
||||
=======================================================
|
||||
Entry point for both live signal analysis and walk-forward backtesting.
|
||||
|
||||
Usage:
|
||||
python main.py # Analyze all major pairs
|
||||
python main.py --pairs EURUSD GBPUSD # Specific pairs
|
||||
python main.py --timeframe H4 # Different primary TF
|
||||
python main.py # Live signal on H1
|
||||
python main.py --timeframe H4 --detail # Detailed H4 signal
|
||||
python main.py --json # JSON output
|
||||
python main.py --watch # Continuous monitoring mode
|
||||
python main.py --backtest # Walk-forward backtest
|
||||
python main.py --backtest --optimize # Parallel grid search
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from typing import List
|
||||
from typing import Dict, List
|
||||
|
||||
# Force UTF-8 output on Windows so box-drawing / Unicode chars render correctly
|
||||
# Force UTF-8 output on Windows so box-drawing chars render
|
||||
if hasattr(sys.stdout, "reconfigure"):
|
||||
sys.stdout.reconfigure(encoding="utf-8")
|
||||
if hasattr(sys.stderr, "reconfigure"):
|
||||
sys.stderr.reconfigure(encoding="utf-8")
|
||||
|
||||
from config import (
|
||||
PlatformConfig, Timeframe, SignalStrength,
|
||||
MAJOR_PAIRS, CROSS_PAIRS,
|
||||
)
|
||||
from config import PlatformConfig, Timeframe, SignalStrength, USDJPY, DISPLAY
|
||||
from signal_aggregator import SignalAggregator, SignalReport
|
||||
|
||||
|
||||
@@ -38,12 +34,10 @@ from signal_aggregator import SignalAggregator, SignalReport
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
|
||||
class Colors:
|
||||
"""ANSI color codes for terminal output."""
|
||||
RESET = "\033[0m"
|
||||
BOLD = "\033[1m"
|
||||
DIM = "\033[2m"
|
||||
UNDERLINE = "\033[4m"
|
||||
|
||||
RED = "\033[91m"
|
||||
GREEN = "\033[92m"
|
||||
YELLOW = "\033[93m"
|
||||
@@ -53,16 +47,8 @@ class Colors:
|
||||
WHITE = "\033[97m"
|
||||
GRAY = "\033[90m"
|
||||
|
||||
BG_RED = "\033[41m"
|
||||
BG_GREEN = "\033[42m"
|
||||
BG_YELLOW = "\033[43m"
|
||||
BG_BLUE = "\033[44m"
|
||||
BG_MAGENTA = "\033[45m"
|
||||
BG_CYAN = "\033[46m"
|
||||
|
||||
|
||||
def signal_color(signal: SignalStrength) -> str:
|
||||
"""Get color for a signal strength."""
|
||||
return {
|
||||
SignalStrength.STRONG_BUY: Colors.GREEN + Colors.BOLD,
|
||||
SignalStrength.BUY: Colors.GREEN,
|
||||
@@ -72,8 +58,7 @@ def signal_color(signal: SignalStrength) -> str:
|
||||
}.get(signal, Colors.RESET)
|
||||
|
||||
|
||||
def quality_color(quality: str) -> str:
|
||||
"""Get color for signal quality grade."""
|
||||
def quality_color(q: str) -> str:
|
||||
return {
|
||||
"A+": Colors.GREEN + Colors.BOLD,
|
||||
"A": Colors.GREEN,
|
||||
@@ -81,93 +66,26 @@ def quality_color(quality: str) -> str:
|
||||
"C": Colors.YELLOW,
|
||||
"D": Colors.GRAY,
|
||||
"F": Colors.DIM,
|
||||
}.get(quality, Colors.RESET)
|
||||
}.get(q, Colors.RESET)
|
||||
|
||||
|
||||
def score_bar(score: float, width: int = 30) -> str:
|
||||
"""Create a visual score bar from -100 to +100."""
|
||||
normalized = (score + 100) / 200 # 0 to 1
|
||||
filled = int(normalized * width)
|
||||
center = width // 2
|
||||
|
||||
bar = ""
|
||||
for i in range(width):
|
||||
if i == center:
|
||||
bar += "│"
|
||||
elif (score >= 0 and center < i <= filled) or \
|
||||
(score < 0 and filled <= i < center):
|
||||
bar += "█"
|
||||
else:
|
||||
bar += "░"
|
||||
|
||||
if score >= 0:
|
||||
color = Colors.GREEN if score > 20 else Colors.GRAY
|
||||
else:
|
||||
color = Colors.RED if score < -20 else Colors.GRAY
|
||||
|
||||
return f"{color}{bar}{Colors.RESET}"
|
||||
|
||||
|
||||
def print_header():
|
||||
"""Print the platform header."""
|
||||
def print_header(title: str = "USD/JPY SIGNAL INTELLIGENCE"):
|
||||
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
|
||||
width = 80
|
||||
|
||||
print(f"\n{Colors.CYAN}{'═' * width}{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.CYAN} ╔═══════════════════════════════════════════════════════╗{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.CYAN} ║ FOREX SIGNAL INTELLIGENCE PLATFORM ║{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.CYAN} ║ Multi-Layer Signal Analysis Engine ║{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.CYAN} ╚═══════════════════════════════════════════════════════╝{Colors.RESET}")
|
||||
print(f"{Colors.GRAY} Scan Time: {now}{Colors.RESET}")
|
||||
print(f"{Colors.CYAN}{'═' * width}{Colors.RESET}\n")
|
||||
print(f"\n{Colors.CYAN}{'═' * 80}{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.CYAN} {title}{Colors.RESET}")
|
||||
print(f"{Colors.GRAY} Time: {now}{Colors.RESET}")
|
||||
print(f"{Colors.CYAN}{'═' * 80}{Colors.RESET}\n")
|
||||
|
||||
|
||||
def print_summary_table(reports: List[SignalReport]):
|
||||
"""Print a summary table of all pairs."""
|
||||
print(f"{Colors.BOLD}{Colors.WHITE} ┌─────────────┬───────────┬──────────────────────────────────┬────────┬────────────┐{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.WHITE} │ Pair │ Signal │ Score │ Grade │ Confidence │{Colors.RESET}")
|
||||
print(f"{Colors.BOLD}{Colors.WHITE} ├─────────────┼───────────┼──────────────────────────────────┼────────┼────────────┤{Colors.RESET}")
|
||||
# ── Live signal renderer ───────────────────────────────────────────
|
||||
|
||||
for r in reports:
|
||||
sig = r.final_signal.name.replace("_", " ")
|
||||
sc = signal_color(r.final_signal)
|
||||
qc = quality_color(r.signal_quality)
|
||||
bar = score_bar(r.final_score, 30)
|
||||
|
||||
pair = f"{r.display_name:<11}"
|
||||
signal_str = f"{sc}{sig:<9}{Colors.RESET}"
|
||||
grade_str = f"{qc}{r.signal_quality:<6}{Colors.RESET}"
|
||||
conf_str = f"{r.final_confidence:5.1f}%"
|
||||
|
||||
# Add warning indicator
|
||||
warn = ""
|
||||
if r.event_risk == "danger":
|
||||
warn = f" {Colors.RED}⚠{Colors.RESET}"
|
||||
elif r.event_risk == "caution":
|
||||
warn = f" {Colors.YELLOW}△{Colors.RESET}"
|
||||
|
||||
print(f" │ {pair} │ {signal_str} │ {bar} │ {grade_str} │ {conf_str:>8}{warn} │")
|
||||
|
||||
print(f"{Colors.BOLD}{Colors.WHITE} └─────────────┴───────────┴──────────────────────────────────┴────────┴────────────┘{Colors.RESET}")
|
||||
|
||||
# Legend
|
||||
print(f"\n{Colors.GRAY} Legend: "
|
||||
f"{Colors.GREEN}██ Bullish{Colors.GRAY} | "
|
||||
f"{Colors.RED}██ Bearish{Colors.GRAY} | "
|
||||
f"│ Center (0) | "
|
||||
f"{Colors.RED}⚠ Event Risk{Colors.GRAY} | "
|
||||
f"{Colors.YELLOW}△ Caution{Colors.RESET}")
|
||||
|
||||
|
||||
def print_detailed_report(report: SignalReport):
|
||||
"""Print detailed analysis for a single pair."""
|
||||
def print_signal_report(report: SignalReport, detail: bool = True):
|
||||
sc = signal_color(report.final_signal)
|
||||
qc = quality_color(report.signal_quality)
|
||||
sig_name = report.final_signal.name.replace("_", " ")
|
||||
|
||||
print(f"\n{Colors.CYAN}{'─' * 80}{Colors.RESET}")
|
||||
print(f" {Colors.BOLD}{Colors.WHITE}{report.display_name}{Colors.RESET}"
|
||||
f" │ Price: {Colors.BOLD}{report.current_price:.5f}{Colors.RESET}"
|
||||
f" │ Price: {Colors.BOLD}{report.current_price:.3f}{Colors.RESET}"
|
||||
f" │ Regime: {Colors.CYAN}{report.market_regime}{Colors.RESET}"
|
||||
f" │ Vol: {report.volatility_regime}")
|
||||
|
||||
@@ -178,99 +96,67 @@ def print_detailed_report(report: SignalReport):
|
||||
|
||||
print(f" {Colors.BOLD}Action:{Colors.RESET} {report.action_recommendation}")
|
||||
|
||||
# Multi-timeframe breakdown
|
||||
if report.mtf_signals:
|
||||
print(f"\n {Colors.BOLD}Multi-Timeframe:{Colors.RESET}", end="")
|
||||
mtf_agree = f" {Colors.GREEN}✓ ALIGNED{Colors.RESET}" if report.mtf_agreement else f" {Colors.YELLOW}✗ DIVERGENT{Colors.RESET}"
|
||||
print(mtf_agree)
|
||||
mtf_status = (f" {Colors.GREEN}✓ ALIGNED{Colors.RESET}"
|
||||
if report.mtf_agreement
|
||||
else f" {Colors.YELLOW}✗ DIVERGENT{Colors.RESET}")
|
||||
print(f"\n {Colors.BOLD}Multi-Timeframe:{Colors.RESET}{mtf_status}")
|
||||
for tf, sig in report.mtf_signals.items():
|
||||
tf_sc = signal_color(sig)
|
||||
tf_score = report.mtf_scores.get(tf, 0)
|
||||
print(f" {tf:>5}: {tf_sc}{sig.name.replace('_', ' '):<12}{Colors.RESET} ({tf_score:+.1f})")
|
||||
print(f" {tf:>5}: {tf_sc}{sig.name.replace('_',' '):<12}{Colors.RESET}"
|
||||
f" ({tf_score:+.1f})")
|
||||
|
||||
# Indicators detail
|
||||
if report.primary_analysis:
|
||||
if detail and report.primary_analysis:
|
||||
print(f"\n {Colors.BOLD}Indicators ({report.primary_analysis.timeframe}):{Colors.RESET}")
|
||||
for name, ind in report.primary_analysis.indicators.items():
|
||||
if name.startswith("_") or ind.weight == 0:
|
||||
continue
|
||||
isc = signal_color(ind.signal)
|
||||
ind_name = f"{ind.name:<18}"
|
||||
print(f" {ind_name} {isc}{ind.signal.name.replace('_', ' '):<12}{Colors.RESET}"
|
||||
print(f" {ind.name:<18} {isc}{ind.signal.name.replace('_',' '):<12}{Colors.RESET}"
|
||||
f" ({ind.confidence:.0f}%) │ {Colors.GRAY}{ind.description}{Colors.RESET}")
|
||||
|
||||
# Bullish reasons
|
||||
if report.bullish_reasons:
|
||||
print(f"\n {Colors.GREEN}▲ Bullish Factors:{Colors.RESET}")
|
||||
for reason in report.bullish_reasons[:5]:
|
||||
print(f" {Colors.GREEN}+{Colors.RESET} {reason}")
|
||||
|
||||
# Bearish reasons
|
||||
for r in report.bullish_reasons[:5]:
|
||||
print(f" {Colors.GREEN}+{Colors.RESET} {r}")
|
||||
if report.bearish_reasons:
|
||||
print(f"\n {Colors.RED}▼ Bearish Factors:{Colors.RESET}")
|
||||
for reason in report.bearish_reasons[:5]:
|
||||
print(f" {Colors.RED}-{Colors.RESET} {reason}")
|
||||
for r in report.bearish_reasons[:5]:
|
||||
print(f" {Colors.RED}-{Colors.RESET} {r}")
|
||||
|
||||
# Warnings
|
||||
if report.warnings:
|
||||
print(f"\n {Colors.YELLOW}⚠ Warnings:{Colors.RESET}")
|
||||
for warning in report.warnings:
|
||||
print(f" {Colors.YELLOW}!{Colors.RESET} {warning}")
|
||||
for w in report.warnings:
|
||||
print(f" {Colors.YELLOW}!{Colors.RESET} {w}")
|
||||
|
||||
# News context
|
||||
if report.news_description and report.news_description != "Neutral news sentiment":
|
||||
print(f"\n {Colors.MAGENTA}📰 News:{Colors.RESET} {report.news_description}")
|
||||
if report.event_description and report.event_risk != "clear":
|
||||
print(f" {Colors.YELLOW}📅 Event:{Colors.RESET} {report.event_description}")
|
||||
|
||||
# Trade setup
|
||||
if report.trade_setup:
|
||||
ts = report.trade_setup
|
||||
dir_color = Colors.GREEN if ts.direction == "BUY" else Colors.RED
|
||||
print(f"\n {Colors.BOLD}Trade Setup:{Colors.RESET}")
|
||||
print(f" Direction: {dir_color}{ts.direction}{Colors.RESET}")
|
||||
print(f" Entry: {ts.entry_price:.5f}")
|
||||
print(f" Stop Loss: {Colors.RED}{ts.stop_loss:.5f}{Colors.RESET} ({ts.risk_pips:.1f} pips)")
|
||||
print(f" Take Profit 1: {Colors.GREEN}{ts.take_profit_1:.5f}{Colors.RESET} ({ts.reward_pips:.1f} pips)")
|
||||
print(f" Take Profit 2: {Colors.GREEN}{ts.take_profit_2:.5f}{Colors.RESET}")
|
||||
print(f" Entry: {ts.entry_price:.3f}")
|
||||
print(f" Stop Loss: {Colors.RED}{ts.stop_loss:.3f}{Colors.RESET} ({ts.risk_pips:.1f} pips)")
|
||||
print(f" Take Profit 1: {Colors.GREEN}{ts.take_profit_1:.3f}{Colors.RESET} ({ts.reward_pips:.1f} pips)")
|
||||
print(f" Take Profit 2: {Colors.GREEN}{ts.take_profit_2:.3f}{Colors.RESET}")
|
||||
print(f" Risk:Reward: 1:{ts.risk_reward:.1f}")
|
||||
|
||||
# Key levels
|
||||
if report.primary_analysis and report.primary_analysis.key_levels:
|
||||
levels = report.primary_analysis.key_levels
|
||||
print(f"\n {Colors.BOLD}Key Levels:{Colors.RESET}")
|
||||
level_names = ["r3", "r2", "r1", "pivot", "s1", "s2", "s3"]
|
||||
for lvl in level_names:
|
||||
for lvl in ["r3", "r2", "r1", "pivot", "s1", "s2", "s3"]:
|
||||
if lvl in levels:
|
||||
val = levels[lvl]
|
||||
if val > report.current_price:
|
||||
color = Colors.RED # resistance
|
||||
else:
|
||||
color = Colors.GREEN # support
|
||||
label = lvl.upper()
|
||||
print(f" {label:>6}: {color}{val:.5f}{Colors.RESET}")
|
||||
|
||||
|
||||
def print_event_calendar(reports: List[SignalReport]):
|
||||
"""Print upcoming economic events affecting watched pairs."""
|
||||
events_shown = set()
|
||||
has_events = False
|
||||
|
||||
for report in reports:
|
||||
if report.event_risk != "clear" and report.event_description not in events_shown:
|
||||
if not has_events:
|
||||
print(f"\n{Colors.YELLOW}{Colors.BOLD} 📅 ECONOMIC CALENDAR ALERTS{Colors.RESET}")
|
||||
has_events = True
|
||||
events_shown.add(report.event_description)
|
||||
risk_color = Colors.RED if report.event_risk == "danger" else Colors.YELLOW
|
||||
print(f" {risk_color}● {report.event_description}{Colors.RESET} → affects {report.display_name}")
|
||||
|
||||
if not has_events:
|
||||
print(f"\n{Colors.GRAY} 📅 No high-impact events in the near term{Colors.RESET}")
|
||||
color = Colors.RED if val > report.current_price else Colors.GREEN
|
||||
print(f" {lvl.upper():>6}: {color}{val:.3f}{Colors.RESET}")
|
||||
|
||||
|
||||
def report_to_dict(report: SignalReport) -> dict:
|
||||
"""Convert a SignalReport to a JSON-serializable dict."""
|
||||
d = {
|
||||
"symbol": report.symbol,
|
||||
"display_name": report.display_name,
|
||||
@@ -305,100 +191,134 @@ def report_to_dict(report: SignalReport) -> dict:
|
||||
"reward_pips": round(ts.reward_pips, 1),
|
||||
}
|
||||
if report.primary_analysis:
|
||||
d["indicators"] = {}
|
||||
for name, ind in report.primary_analysis.indicators.items():
|
||||
if not name.startswith("_"):
|
||||
d["indicators"][name] = {
|
||||
d["indicators"] = {
|
||||
name: {
|
||||
"signal": ind.signal.name,
|
||||
"confidence": round(ind.confidence, 1),
|
||||
"description": ind.description,
|
||||
}
|
||||
for name, ind in report.primary_analysis.indicators.items()
|
||||
if not name.startswith("_")
|
||||
}
|
||||
d["key_levels"] = {
|
||||
k: round(v, 5) for k, v in report.primary_analysis.key_levels.items()
|
||||
}
|
||||
return d
|
||||
|
||||
|
||||
# ── Backtest renderer ──────────────────────────────────────────────
|
||||
|
||||
def print_backtest_report(result, params: dict = None, verbose_journal: bool = False):
|
||||
m = result.metrics
|
||||
print(f" {Colors.BOLD}{Colors.WHITE}Backtest Summary — USD/JPY{Colors.RESET}")
|
||||
if params:
|
||||
params_str = ", ".join(f"{k}={v}" for k, v in params.items())
|
||||
print(f" {Colors.GRAY}Params: {params_str}{Colors.RESET}")
|
||||
print(f" {Colors.GRAY}{'─' * 76}{Colors.RESET}")
|
||||
|
||||
initial = result.initial_balance
|
||||
final = result.final_balance
|
||||
ret_pct = m.get("total_return_pct", 0)
|
||||
ret_color = Colors.GREEN if ret_pct >= 0 else Colors.RED
|
||||
|
||||
print(f" Trades: {Colors.BOLD}{m.get('total_trades', 0)}{Colors.RESET}"
|
||||
f" ({m.get('wins', 0)}W / {m.get('losses', 0)}L)")
|
||||
print(f" Win Rate: {m.get('win_rate', 0):.1f}%")
|
||||
print(f" Profit Factor: {m.get('profit_factor', 0):.2f}")
|
||||
pf_inf = m.get('profit_factor', 0) == float('inf')
|
||||
if pf_inf:
|
||||
print(f" (no losing trades — infinite PF)")
|
||||
print(f" Expectancy: {m.get('expectancy_pips', 0):+.2f} pips/trade"
|
||||
f" ({m.get('expectancy_per_trade', 0):+.2f} per trade)")
|
||||
print(f" Initial Balance: ${initial:,.2f}")
|
||||
print(f" Final Balance: {ret_color}${final:,.2f}{Colors.RESET}")
|
||||
print(f" Total Return: {ret_color}{ret_pct:+.2f}%{Colors.RESET}")
|
||||
print(f" CAGR: {m.get('cagr', 0):+.2f}%")
|
||||
print(f" Max Drawdown: {Colors.RED}{m.get('max_drawdown_pct', 0):.2f}%{Colors.RESET}")
|
||||
print(f" Sharpe Ratio: {m.get('sharpe', 0):.2f}")
|
||||
print(f" Sortino Ratio: {m.get('sortino', 0):.2f}")
|
||||
print(f" Avg Duration: {m.get('avg_trade_duration_bars', 0):.1f} bars")
|
||||
print(f" Win Streak: {m.get('longest_winning_streak', 0)} | "
|
||||
f"Loss Streak: {m.get('longest_losing_streak', 0)}")
|
||||
print(f" {Colors.GRAY}{'─' * 76}{Colors.RESET}")
|
||||
|
||||
if verbose_journal and result.trades:
|
||||
print(f"\n {Colors.BOLD}Trade Journal (last 10):{Colors.RESET}")
|
||||
print(f" {'#':<4} {'Entry Time':<22} {'Dir':<5} {'Entry':<9} "
|
||||
f"{'Exit':<9} {'Reason':<6} {'Pips':>8} {'P&L%':>7}")
|
||||
for i, t in enumerate(result.trades[-10:], start=max(1, len(result.trades) - 9)):
|
||||
color = Colors.GREEN if t.pnl_currency > 0 else Colors.RED
|
||||
entry_str = str(t.entry_time)[:19]
|
||||
print(f" {i:<4} {entry_str:<22} {t.direction:<5} {t.entry_price:<9.3f} "
|
||||
f"{t.exit_price:<9.3f} {t.exit_reason:<6} "
|
||||
f"{color}{t.pnl_pips:>+8.1f}{Colors.RESET} "
|
||||
f"{color}{t.pnl_pct:>+6.2f}%{Colors.RESET}")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
# MAIN
|
||||
# CLI
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Forex Signal Intelligence Platform",
|
||||
description="ForexBot2 — USD/JPY Signal Intelligence + Backtester",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
python main.py Analyze all major pairs (H1)
|
||||
python main.py --pairs EURUSD GBPUSD Analyze specific pairs
|
||||
python main.py --timeframe H4 Use 4-hour as primary TF
|
||||
python main.py --all Include cross pairs
|
||||
python main.py --json Output as JSON
|
||||
python main.py --detail Show detailed analysis per pair
|
||||
python main.py --watch Continuous monitoring (5min refresh)
|
||||
python main.py Live signal (H1, summary)
|
||||
python main.py --detail Live signal with full breakdown
|
||||
python main.py --timeframe H4 --detail H4 timeframe
|
||||
python main.py --json JSON output
|
||||
python main.py --backtest Walk-forward backtest (full history)
|
||||
python main.py --backtest --bars 2000 Backtest on last 2000 bars
|
||||
python main.py --backtest --optimize Parallel grid search across CPU cores
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pairs", nargs="+", default=None,
|
||||
help="Specific pairs to analyze (e.g., EURUSD GBPUSD)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--timeframe", "-tf", default="H1",
|
||||
parser.add_argument("--timeframe", "-tf", default="H1",
|
||||
choices=["M5", "M15", "M30", "H1", "H4", "D1"],
|
||||
help="Primary analysis timeframe (default: H1)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--all", action="store_true",
|
||||
help="Include cross pairs in analysis",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json", action="store_true",
|
||||
help="Output results as JSON",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--detail", action="store_true",
|
||||
help="Show detailed analysis for each pair",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--top", type=int, default=None,
|
||||
help="Show only top N signals by strength",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--watch", action="store_true",
|
||||
help="Continuous monitoring mode (refreshes every 5 min)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min-grade", default="F",
|
||||
help="Primary analysis timeframe (default: H1)")
|
||||
parser.add_argument("--detail", action="store_true",
|
||||
help="Show full indicator breakdown (live mode)")
|
||||
parser.add_argument("--json", action="store_true",
|
||||
help="Output as JSON")
|
||||
parser.add_argument("--verbose", "-v", action="store_true",
|
||||
help="Debug logging")
|
||||
|
||||
# Backtest options
|
||||
parser.add_argument("--backtest", action="store_true",
|
||||
help="Run walk-forward backtest instead of live signal")
|
||||
parser.add_argument("--optimize", action="store_true",
|
||||
help="Run parallel parameter grid search (implies --backtest)")
|
||||
parser.add_argument("--bars", type=int, default=5000,
|
||||
help="Max bars of history (default 5000 — TD ceiling)")
|
||||
parser.add_argument("--start", default=None,
|
||||
help="Backtest window start (YYYY-MM-DD)")
|
||||
parser.add_argument("--end", default=None,
|
||||
help="Backtest window end (YYYY-MM-DD)")
|
||||
parser.add_argument("--min-grade", default="B",
|
||||
choices=["A+", "A", "B", "C", "D", "F"],
|
||||
help="Minimum signal quality to display",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verbose", "-v", action="store_true",
|
||||
help="Enable debug logging",
|
||||
)
|
||||
help="Minimum grade to open backtest trade (default B)")
|
||||
parser.add_argument("--journal", action="store_true",
|
||||
help="Print last 10 trades from backtest journal")
|
||||
parser.add_argument("--journal-csv", default=None,
|
||||
help="Write full trade journal to CSV path")
|
||||
parser.add_argument("--regime-filter", action="store_true",
|
||||
help="Gate signals on higher-TF ADX trend confirmation")
|
||||
parser.add_argument("--regime-min-adx", type=float, default=30.0,
|
||||
help="Min ADX for regime filter (default 30, requires --regime-filter)")
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def symbol_from_name(name: str) -> str:
|
||||
"""Convert pair name like 'EURUSD' to Yahoo symbol 'EURUSD=X'."""
|
||||
name = name.upper().replace("/", "").replace("_", "")
|
||||
if not name.endswith("=X"):
|
||||
name += "=X"
|
||||
return name
|
||||
|
||||
|
||||
def run_analysis(args) -> List[SignalReport]:
|
||||
"""Run a single analysis cycle."""
|
||||
def build_config(args) -> PlatformConfig:
|
||||
config = PlatformConfig()
|
||||
|
||||
# Configure timeframe
|
||||
tf_map = {
|
||||
"M5": Timeframe.M5, "M15": Timeframe.M15, "M30": Timeframe.M30,
|
||||
"H1": Timeframe.H1, "H4": Timeframe.H4, "D1": Timeframe.D1,
|
||||
}
|
||||
config.primary_timeframe = tf_map[args.timeframe]
|
||||
|
||||
# Configure confirmation timeframes based on primary
|
||||
tf_confirmations = {
|
||||
"M5": [Timeframe.M1, Timeframe.M15],
|
||||
"M15": [Timeframe.M5, Timeframe.H1],
|
||||
@@ -411,34 +331,111 @@ def run_analysis(args) -> List[SignalReport]:
|
||||
args.timeframe, [Timeframe.M15, Timeframe.H4]
|
||||
)
|
||||
|
||||
# Configure pairs
|
||||
if args.pairs:
|
||||
config.watch_pairs = [symbol_from_name(p) for p in args.pairs]
|
||||
elif args.all:
|
||||
config.watch_pairs = MAJOR_PAIRS + CROSS_PAIRS
|
||||
else:
|
||||
config.watch_pairs = MAJOR_PAIRS
|
||||
# Backtest config
|
||||
config.backtest.start_date = args.start
|
||||
config.backtest.end_date = args.end
|
||||
config.backtest.max_bars = args.bars
|
||||
config.backtest.min_trade_grade = args.min_grade
|
||||
config.backtest.optimize = args.optimize
|
||||
config.backtest.regime_filter = args.regime_filter
|
||||
config.backtest.regime_filter_min_adx = args.regime_min_adx
|
||||
|
||||
# Run analysis
|
||||
return config
|
||||
|
||||
|
||||
def run_live(args, config: PlatformConfig):
|
||||
print(f"{Colors.GRAY} Fetching market data and running analysis...{Colors.RESET}\n")
|
||||
aggregator = SignalAggregator(config)
|
||||
reports = aggregator.analyze_all_pairs()
|
||||
report = aggregator.analyze_pair()
|
||||
|
||||
# Filter by grade
|
||||
grade_order = ["A+", "A", "B", "C", "D", "F"]
|
||||
min_idx = grade_order.index(args.min_grade) if args.min_grade in grade_order else len(grade_order) - 1
|
||||
reports = [r for r in reports if grade_order.index(r.signal_quality) <= min_idx]
|
||||
if args.json:
|
||||
print(json.dumps(report_to_dict(report), indent=2))
|
||||
return
|
||||
|
||||
# Top N
|
||||
if args.top:
|
||||
reports = reports[:args.top]
|
||||
print_header("USD/JPY SIGNAL INTELLIGENCE")
|
||||
print_signal_report(report, detail=args.detail)
|
||||
print(f"\n{Colors.CYAN}{'═' * 80}{Colors.RESET}")
|
||||
print(f"{Colors.GRAY} Disclaimer: For educational/research purposes only.{Colors.RESET}")
|
||||
print(f"{Colors.CYAN}{'═' * 80}{Colors.RESET}\n")
|
||||
|
||||
return reports
|
||||
|
||||
def run_backtest(args, config: PlatformConfig):
|
||||
from backtest import WalkForwardBacktester
|
||||
from metrics import trades_to_dataframe
|
||||
|
||||
print_header("USD/JPY WALK-FORWARD BACKTEST")
|
||||
print(f"{Colors.GRAY} Loading data and replaying bars...{Colors.RESET}\n")
|
||||
|
||||
backtester = WalkForwardBacktester(config)
|
||||
result = backtester.run()
|
||||
|
||||
if args.json:
|
||||
out = {
|
||||
"metrics": result.metrics,
|
||||
"initial_balance": result.initial_balance,
|
||||
"final_balance": result.final_balance,
|
||||
"trades": [
|
||||
{
|
||||
"entry_time": str(t.entry_time),
|
||||
"exit_time": str(t.exit_time),
|
||||
"direction": t.direction,
|
||||
"entry_price": t.entry_price,
|
||||
"exit_price": t.exit_price,
|
||||
"exit_reason": t.exit_reason,
|
||||
"pnl_pips": t.pnl_pips,
|
||||
"pnl_pct": t.pnl_pct,
|
||||
"grade": t.grade,
|
||||
}
|
||||
for t in result.trades
|
||||
],
|
||||
}
|
||||
print(json.dumps(out, indent=2))
|
||||
else:
|
||||
print_backtest_report(result, verbose_journal=args.journal)
|
||||
|
||||
if args.journal_csv and result.trades:
|
||||
df = trades_to_dataframe(result.trades)
|
||||
df.to_csv(args.journal_csv, index=False)
|
||||
print(f"\n {Colors.GREEN}✓ Trade journal written to {args.journal_csv}{Colors.RESET}")
|
||||
|
||||
|
||||
def run_optimize(args, config: PlatformConfig):
|
||||
from backtest import Optimizer
|
||||
|
||||
print_header("USD/JPY BACKTEST PARAMETER OPTIMIZER")
|
||||
grid = config.backtest.param_grid
|
||||
n_combos = 1
|
||||
for v in grid.values():
|
||||
n_combos *= len(v)
|
||||
print(f"{Colors.GRAY} Grid: {n_combos} combos × {config.backtest.parallel_workers} workers{Colors.RESET}\n")
|
||||
|
||||
optimizer = Optimizer(config)
|
||||
results = optimizer.run_grid(grid)
|
||||
|
||||
print(f"\n {Colors.BOLD}Top 10 Parameter Combinations (by total return):{Colors.RESET}\n")
|
||||
print(f" {'#':<3} {'Return%':>8} {'PF':>6} {'WinR':>6} {'Trades':>7} "
|
||||
f"{'MaxDD%':>7} {'Sharpe':>7} Params")
|
||||
print(f" {Colors.GRAY}{'─' * 90}{Colors.RESET}")
|
||||
|
||||
for i, r in enumerate(results[:10], start=1):
|
||||
m = r.metrics
|
||||
ret = m.get("total_return_pct", 0)
|
||||
ret_color = Colors.GREEN if ret >= 0 else Colors.RED
|
||||
params_str = ", ".join(f"{k}={v}" for k, v in r.params.items())
|
||||
pf = m.get("profit_factor", 0)
|
||||
pf_str = "inf" if pf == float("inf") else f"{pf:.2f}"
|
||||
print(f" {i:<3} {ret_color}{ret:>+7.2f}%{Colors.RESET} {pf_str:>6} "
|
||||
f"{m.get('win_rate', 0):>5.1f}% {m.get('total_trades', 0):>7} "
|
||||
f"{m.get('max_drawdown_pct', 0):>+6.2f}% {m.get('sharpe', 0):>7.2f}"
|
||||
f" {Colors.GRAY}{params_str}{Colors.RESET}")
|
||||
|
||||
print(f"\n {Colors.BOLD}Best combination details:{Colors.RESET}\n")
|
||||
print_backtest_report(results[0], params=results[0].params, verbose_journal=False)
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
# Setup logging
|
||||
log_level = logging.DEBUG if args.verbose else logging.WARNING
|
||||
logging.basicConfig(
|
||||
level=log_level,
|
||||
@@ -446,63 +443,14 @@ def main():
|
||||
datefmt="%H:%M:%S",
|
||||
)
|
||||
|
||||
if args.watch:
|
||||
# Continuous monitoring mode
|
||||
print(f"{Colors.BOLD}Starting continuous monitoring mode (Ctrl+C to stop)...{Colors.RESET}")
|
||||
while True:
|
||||
try:
|
||||
reports = run_analysis(args)
|
||||
if args.json:
|
||||
output = [report_to_dict(r) for r in reports]
|
||||
print(json.dumps(output, indent=2))
|
||||
else:
|
||||
print("\033[2J\033[H") # clear screen
|
||||
print_header()
|
||||
print_summary_table(reports)
|
||||
print_event_calendar(reports)
|
||||
if args.detail:
|
||||
for r in reports:
|
||||
print_detailed_report(r)
|
||||
print(f"\n{Colors.GRAY} Next refresh in 5 minutes...{Colors.RESET}")
|
||||
config = build_config(args)
|
||||
|
||||
time.sleep(300)
|
||||
except KeyboardInterrupt:
|
||||
print(f"\n{Colors.YELLOW}Monitoring stopped.{Colors.RESET}")
|
||||
break
|
||||
if args.optimize:
|
||||
run_optimize(args, config)
|
||||
elif args.backtest:
|
||||
run_backtest(args, config)
|
||||
else:
|
||||
# Single run
|
||||
print(f"{Colors.GRAY} Fetching market data and running analysis...{Colors.RESET}\n")
|
||||
reports = run_analysis(args)
|
||||
|
||||
if args.json:
|
||||
output = [report_to_dict(r) for r in reports]
|
||||
print(json.dumps(output, indent=2))
|
||||
else:
|
||||
print_header()
|
||||
if reports:
|
||||
print_summary_table(reports)
|
||||
print_event_calendar(reports)
|
||||
|
||||
if args.detail:
|
||||
for r in reports:
|
||||
print_detailed_report(r)
|
||||
else:
|
||||
# Show detail for top signals only
|
||||
actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B")]
|
||||
if actionable:
|
||||
print(f"\n{Colors.BOLD}{Colors.WHITE} ═══ ACTIONABLE SIGNALS (Grade B or above) ═══{Colors.RESET}")
|
||||
for r in actionable:
|
||||
print_detailed_report(r)
|
||||
else:
|
||||
print(f"\n{Colors.GRAY} No high-grade signals detected. "
|
||||
f"Use --detail for full breakdown or --min-grade C to see more.{Colors.RESET}")
|
||||
else:
|
||||
print(f"{Colors.YELLOW} No data available. Check your internet connection.{Colors.RESET}")
|
||||
|
||||
print(f"\n{Colors.CYAN}{'═' * 80}{Colors.RESET}")
|
||||
print(f"{Colors.GRAY} Disclaimer: This tool is for educational/research purposes only.{Colors.RESET}")
|
||||
print(f"{Colors.GRAY} Past performance ≠ future results. Always use proper risk management.{Colors.RESET}")
|
||||
print(f"{Colors.CYAN}{'═' * 80}{Colors.RESET}\n")
|
||||
run_live(args, config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
+155
@@ -0,0 +1,155 @@
|
||||
"""
|
||||
ForexBot2 — Backtest Metrics
|
||||
==============================
|
||||
Pure functions over Trade records & equity curves.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
@dataclass
|
||||
class Trade:
|
||||
"""A single closed trade."""
|
||||
entry_time: pd.Timestamp
|
||||
exit_time: pd.Timestamp
|
||||
direction: str # "BUY" / "SELL"
|
||||
entry_price: float
|
||||
exit_price: float
|
||||
stop_loss: float
|
||||
take_profit: float
|
||||
exit_reason: str # "tp" / "sl" / "end_of_data"
|
||||
risk_pips: float
|
||||
pnl_pips: float
|
||||
pnl_pct: float # P&L as % of equity at trade open
|
||||
pnl_currency: float # P&L in account currency (units = balance)
|
||||
duration_bars: int
|
||||
grade: str # signal grade at entry
|
||||
score_at_entry: float
|
||||
confidence_at_entry: float
|
||||
|
||||
|
||||
@dataclass
|
||||
class BacktestResult:
|
||||
"""Complete backtest output."""
|
||||
trades: List[Trade] = field(default_factory=list)
|
||||
equity_curve: pd.Series = field(default_factory=lambda: pd.Series(dtype=float))
|
||||
initial_balance: float = 0.0
|
||||
final_balance: float = 0.0
|
||||
metrics: Dict[str, float] = field(default_factory=dict)
|
||||
params: Dict[str, float] = field(default_factory=dict)
|
||||
|
||||
|
||||
def compute_metrics(
|
||||
trades: List[Trade],
|
||||
equity_curve: pd.Series,
|
||||
initial_balance: float,
|
||||
elapsed_days: Optional[float] = None,
|
||||
) -> Dict[str, float]:
|
||||
"""All summary metrics from a list of closed trades + equity curve."""
|
||||
if not trades:
|
||||
return {
|
||||
"total_trades": 0,
|
||||
"win_rate": 0.0,
|
||||
"profit_factor": 0.0,
|
||||
"expectancy_per_trade": 0.0,
|
||||
"expectancy_pips": 0.0,
|
||||
"total_return_pct": 0.0,
|
||||
"cagr": 0.0,
|
||||
"max_drawdown_pct": 0.0,
|
||||
"sharpe": 0.0,
|
||||
"sortino": 0.0,
|
||||
"avg_trade_duration_bars": 0.0,
|
||||
"longest_winning_streak": 0,
|
||||
"longest_losing_streak": 0,
|
||||
"final_balance": initial_balance,
|
||||
}
|
||||
|
||||
pnl_currency = np.array([t.pnl_currency for t in trades])
|
||||
pnl_pips = np.array([t.pnl_pips for t in trades])
|
||||
|
||||
wins = pnl_currency > 0
|
||||
losses = pnl_currency < 0
|
||||
|
||||
total_wins = pnl_currency[wins].sum() if wins.any() else 0.0
|
||||
total_losses = -pnl_currency[losses].sum() if losses.any() else 0.0
|
||||
|
||||
win_rate = wins.sum() / len(trades) * 100
|
||||
profit_factor = (total_wins / total_losses) if total_losses > 0 else float("inf")
|
||||
expectancy_per_trade = pnl_currency.mean()
|
||||
expectancy_pips = pnl_pips.mean()
|
||||
|
||||
final_balance = float(equity_curve.iloc[-1]) if len(equity_curve) else initial_balance
|
||||
total_return_pct = (final_balance / initial_balance - 1) * 100
|
||||
|
||||
# CAGR
|
||||
if elapsed_days and elapsed_days > 0 and final_balance > 0:
|
||||
years = elapsed_days / 365.25
|
||||
cagr = ((final_balance / initial_balance) ** (1 / years) - 1) * 100 if years > 0 else 0.0
|
||||
else:
|
||||
cagr = 0.0
|
||||
|
||||
# Max drawdown
|
||||
if len(equity_curve):
|
||||
running_max = equity_curve.cummax()
|
||||
drawdown = (equity_curve - running_max) / running_max
|
||||
max_dd = float(drawdown.min()) * 100
|
||||
else:
|
||||
max_dd = 0.0
|
||||
|
||||
# Sharpe / Sortino — daily returns from equity curve
|
||||
sharpe, sortino = 0.0, 0.0
|
||||
if len(equity_curve) > 2:
|
||||
daily = equity_curve.resample("1D").last().ffill().pct_change().dropna()
|
||||
if len(daily) > 1 and daily.std() > 0:
|
||||
sharpe = float(daily.mean() / daily.std() * np.sqrt(252))
|
||||
downside = daily[daily < 0]
|
||||
if len(downside) > 1 and downside.std() > 0:
|
||||
sortino = float(daily.mean() / downside.std() * np.sqrt(252))
|
||||
|
||||
# Streaks
|
||||
win_loss_seq = [1 if t.pnl_currency > 0 else (-1 if t.pnl_currency < 0 else 0) for t in trades]
|
||||
longest_win = _longest_streak(win_loss_seq, 1)
|
||||
longest_loss = _longest_streak(win_loss_seq, -1)
|
||||
|
||||
avg_duration = float(np.mean([t.duration_bars for t in trades]))
|
||||
|
||||
return {
|
||||
"total_trades": len(trades),
|
||||
"wins": int(wins.sum()),
|
||||
"losses": int(losses.sum()),
|
||||
"win_rate": float(win_rate),
|
||||
"profit_factor": float(profit_factor),
|
||||
"expectancy_per_trade": float(expectancy_per_trade),
|
||||
"expectancy_pips": float(expectancy_pips),
|
||||
"total_return_pct": float(total_return_pct),
|
||||
"cagr": float(cagr),
|
||||
"max_drawdown_pct": float(max_dd),
|
||||
"sharpe": float(sharpe),
|
||||
"sortino": float(sortino),
|
||||
"avg_trade_duration_bars": avg_duration,
|
||||
"longest_winning_streak": longest_win,
|
||||
"longest_losing_streak": longest_loss,
|
||||
"final_balance": final_balance,
|
||||
}
|
||||
|
||||
|
||||
def _longest_streak(seq: List[int], target: int) -> int:
|
||||
best = current = 0
|
||||
for x in seq:
|
||||
if x == target:
|
||||
current += 1
|
||||
best = max(best, current)
|
||||
else:
|
||||
current = 0
|
||||
return best
|
||||
|
||||
|
||||
def trades_to_dataframe(trades: List[Trade]) -> pd.DataFrame:
|
||||
"""Convert trade journal to a DataFrame for CSV/JSON export."""
|
||||
if not trades:
|
||||
return pd.DataFrame()
|
||||
return pd.DataFrame([asdict(t) for t in trades])
|
||||
+4
-12
@@ -413,15 +413,7 @@ class NewsEngine:
|
||||
|
||||
@staticmethod
|
||||
def _get_affected_pairs(currency: str) -> List[str]:
|
||||
"""Get all major pairs affected by a currency event."""
|
||||
pairs = {
|
||||
"USD": ["EURUSD=X", "GBPUSD=X", "USDJPY=X", "USDCHF=X", "AUDUSD=X", "USDCAD=X", "NZDUSD=X"],
|
||||
"EUR": ["EURUSD=X", "EURGBP=X", "EURJPY=X", "EURCHF=X"],
|
||||
"GBP": ["GBPUSD=X", "EURGBP=X", "GBPJPY=X"],
|
||||
"JPY": ["USDJPY=X", "EURJPY=X", "GBPJPY=X", "AUDJPY=X", "CADJPY=X"],
|
||||
"CHF": ["USDCHF=X", "EURCHF=X"],
|
||||
"AUD": ["AUDUSD=X", "AUDJPY=X"],
|
||||
"CAD": ["USDCAD=X", "CADJPY=X"],
|
||||
"NZD": ["NZDUSD=X"],
|
||||
}
|
||||
return pairs.get(currency, [])
|
||||
"""Map a currency event to USD/JPY (the only watched pair)."""
|
||||
if currency in ("USD", "JPY"):
|
||||
return ["USDJPY=X"]
|
||||
return []
|
||||
|
||||
@@ -1,3 +1,5 @@
|
||||
numpy>=1.24
|
||||
pandas>=2.0
|
||||
requests>=2.28
|
||||
pyarrow>=14.0
|
||||
tqdm>=4.66
|
||||
|
||||
+108
-127
@@ -1,16 +1,10 @@
|
||||
"""
|
||||
Forex Signal Intelligence Platform - Signal Aggregator
|
||||
========================================================
|
||||
Combines technical analysis across multiple timeframes with
|
||||
fundamental/news context to produce final actionable signals.
|
||||
|
||||
This is the brain — it decides what's actually worth paying attention to.
|
||||
|
||||
Key principles:
|
||||
1. Multi-timeframe agreement = higher confidence
|
||||
2. High-impact news overrides technicals
|
||||
3. Divergences between timeframes = caution
|
||||
4. No signal is better than a bad signal
|
||||
ForexBot2 — Signal Aggregator
|
||||
==============================
|
||||
Combines multi-timeframe technical analysis with optional news context
|
||||
into a graded actionable signal. Same code path is used for live mode
|
||||
and walk-forward backtesting; the only difference is whether the input
|
||||
OHLCV is fetched live or sliced from a pre-loaded historical buffer.
|
||||
"""
|
||||
|
||||
import logging
|
||||
@@ -20,10 +14,7 @@ from typing import Dict, List, Optional
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from config import (
|
||||
PlatformConfig, SignalStrength, Timeframe,
|
||||
SYMBOL_DISPLAY,
|
||||
)
|
||||
from config import PlatformConfig, SignalStrength, Timeframe
|
||||
from data_provider import ForexDataProvider
|
||||
from engine import TechnicalEngine, TechnicalAnalysis
|
||||
from news_engine import NewsEngine, FundamentalContext
|
||||
@@ -33,10 +24,9 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
@dataclass
|
||||
class TradeSetup:
|
||||
"""A complete trade setup recommendation."""
|
||||
pair: str
|
||||
display_name: str
|
||||
direction: str # "BUY" or "SELL"
|
||||
direction: str # "BUY" / "SELL"
|
||||
entry_price: float
|
||||
stop_loss: float
|
||||
take_profit_1: float
|
||||
@@ -49,47 +39,38 @@ class TradeSetup:
|
||||
|
||||
@dataclass
|
||||
class SignalReport:
|
||||
"""Complete signal report for a single pair."""
|
||||
symbol: str
|
||||
display_name: str
|
||||
timestamp: str
|
||||
|
||||
# Composite signals
|
||||
primary_signal: SignalStrength = SignalStrength.NEUTRAL
|
||||
primary_score: float = 0.0
|
||||
primary_confidence: float = 0.0
|
||||
|
||||
# Multi-timeframe agreement
|
||||
mtf_agreement: bool = False
|
||||
mtf_signals: Dict[str, SignalStrength] = field(default_factory=dict)
|
||||
mtf_scores: Dict[str, float] = field(default_factory=dict)
|
||||
|
||||
# Technical details
|
||||
primary_analysis: Optional[TechnicalAnalysis] = None
|
||||
confirmation_analyses: List[TechnicalAnalysis] = field(default_factory=list)
|
||||
|
||||
# Fundamental context
|
||||
event_risk: str = "clear"
|
||||
event_description: str = ""
|
||||
news_sentiment: float = 0.0
|
||||
news_description: str = ""
|
||||
|
||||
# Final verdict
|
||||
final_signal: SignalStrength = SignalStrength.NEUTRAL
|
||||
final_score: float = 0.0
|
||||
final_confidence: float = 0.0
|
||||
action_recommendation: str = ""
|
||||
signal_quality: str = "" # "A+", "A", "B", "C", "D", "F"
|
||||
signal_quality: str = "" # A+, A, B, C, D, F
|
||||
|
||||
# Trade setup (only for actionable signals)
|
||||
trade_setup: Optional[TradeSetup] = None
|
||||
|
||||
# Key reasons
|
||||
bullish_reasons: List[str] = field(default_factory=list)
|
||||
bearish_reasons: List[str] = field(default_factory=list)
|
||||
warnings: List[str] = field(default_factory=list)
|
||||
|
||||
# Current price info
|
||||
current_price: float = 0.0
|
||||
spread_estimate: float = 0.0
|
||||
market_regime: str = ""
|
||||
@@ -98,53 +79,67 @@ class SignalReport:
|
||||
|
||||
class SignalAggregator:
|
||||
"""
|
||||
Master signal aggregator.
|
||||
|
||||
Orchestrates data fetching, technical analysis across timeframes,
|
||||
fundamental analysis, and produces final graded signals.
|
||||
Master aggregator. Designed so the backtester can drive it bar-by-bar
|
||||
via `analyze_from_dataframes` without re-fetching from the API.
|
||||
"""
|
||||
|
||||
def __init__(self, config: PlatformConfig):
|
||||
self.config = config
|
||||
self.data_provider = ForexDataProvider(config)
|
||||
self.tech_engine = TechnicalEngine(config.technical)
|
||||
self.news_engine = NewsEngine(config.news)
|
||||
self._news_engine: Optional[NewsEngine] = None # lazy
|
||||
|
||||
def analyze_all_pairs(self) -> List[SignalReport]:
|
||||
"""
|
||||
Analyze all watched pairs and return sorted signal reports.
|
||||
Returns reports sorted by signal quality (best first).
|
||||
"""
|
||||
reports = []
|
||||
context = self.news_engine.get_fundamental_context()
|
||||
def _get_news_engine(self) -> NewsEngine:
|
||||
if self._news_engine is None:
|
||||
self._news_engine = NewsEngine(self.config.news)
|
||||
return self._news_engine
|
||||
|
||||
# Batch-prefetch all pairs × all needed timeframes (3 API calls instead of 21)
|
||||
all_timeframes = (
|
||||
[self.config.primary_timeframe] + self.config.confirmation_timeframes
|
||||
)
|
||||
self.data_provider.prefetch_batch(self.config.watch_pairs, all_timeframes)
|
||||
|
||||
for symbol in self.config.watch_pairs:
|
||||
try:
|
||||
report = self.analyze_pair(symbol, context)
|
||||
reports.append(report)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to analyze {symbol}: {e}")
|
||||
continue
|
||||
|
||||
# Sort by absolute score (strongest signals first)
|
||||
reports.sort(key=lambda r: abs(r.final_score), reverse=True)
|
||||
return reports
|
||||
# ── Public: live mode entry point ──────────────────────────────
|
||||
|
||||
def analyze_pair(
|
||||
self,
|
||||
symbol: str,
|
||||
symbol: Optional[str] = None,
|
||||
context: Optional[FundamentalContext] = None,
|
||||
) -> SignalReport:
|
||||
"""Complete analysis of a single pair across all layers."""
|
||||
if context is None:
|
||||
context = self.news_engine.get_fundamental_context()
|
||||
"""Live-mode entry: fetch all needed timeframes then delegate."""
|
||||
if symbol is None:
|
||||
symbol = self.config.symbol
|
||||
|
||||
timeframes = [self.config.primary_timeframe] + list(
|
||||
self.config.confirmation_timeframes
|
||||
)
|
||||
|
||||
dfs: Dict[Timeframe, pd.DataFrame] = {}
|
||||
for tf in timeframes:
|
||||
try:
|
||||
dfs[tf] = self.data_provider.fetch_ohlcv(symbol, tf)
|
||||
except Exception as e:
|
||||
logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")
|
||||
|
||||
return self.analyze_from_dataframes(
|
||||
symbol=symbol,
|
||||
dfs=dfs,
|
||||
context=context,
|
||||
use_news=True,
|
||||
)
|
||||
|
||||
# ── Public: bar-replayable entry point ─────────────────────────
|
||||
|
||||
def analyze_from_dataframes(
|
||||
self,
|
||||
symbol: str,
|
||||
dfs: Dict[Timeframe, pd.DataFrame],
|
||||
context: Optional[FundamentalContext] = None,
|
||||
use_news: bool = True,
|
||||
) -> SignalReport:
|
||||
"""
|
||||
Run the full analysis pipeline on already-fetched dataframes.
|
||||
|
||||
`dfs` must include the primary timeframe and ideally the
|
||||
confirmation timeframes. Each df is treated as the visible history
|
||||
at decision time — the backtester is responsible for slicing so
|
||||
no future bars leak in.
|
||||
"""
|
||||
pair_info = self.data_provider.get_pair_info(symbol)
|
||||
display = pair_info["display"]
|
||||
pip_value = pair_info["pip_value"]
|
||||
@@ -155,11 +150,13 @@ class SignalAggregator:
|
||||
timestamp=str(datetime.now(timezone.utc)),
|
||||
)
|
||||
|
||||
# ── 1. Primary Timeframe Analysis ──────────────────────────
|
||||
try:
|
||||
primary_df = self.data_provider.fetch_ohlcv(
|
||||
symbol, self.config.primary_timeframe
|
||||
)
|
||||
# ── 1. Primary timeframe ───────────────────────────────────
|
||||
primary_df = dfs.get(self.config.primary_timeframe)
|
||||
if primary_df is None or primary_df.empty:
|
||||
report.warnings.append("Primary timeframe data unavailable")
|
||||
report.signal_quality = "F"
|
||||
return report
|
||||
|
||||
primary_analysis = self.tech_engine.analyze(
|
||||
primary_df, symbol, self.config.primary_timeframe.value
|
||||
)
|
||||
@@ -170,18 +167,16 @@ class SignalAggregator:
|
||||
report.market_regime = primary_analysis.market_regime
|
||||
report.volatility_regime = primary_analysis.volatility_regime
|
||||
report.current_price = float(primary_df["Close"].iloc[-1])
|
||||
report.timestamp = str(primary_df.index[-1])
|
||||
report.mtf_signals[self.config.primary_timeframe.value] = primary_analysis.composite_signal
|
||||
report.mtf_scores[self.config.primary_timeframe.value] = primary_analysis.composite_score
|
||||
except Exception as e:
|
||||
logger.error(f"Primary analysis failed for {symbol}: {e}")
|
||||
report.warnings.append(f"Primary timeframe data unavailable: {e}")
|
||||
report.signal_quality = "F"
|
||||
return report
|
||||
|
||||
# ── 2. Confirmation Timeframe Analyses ─────────────────────
|
||||
# ── 2. Confirmation timeframes ─────────────────────────────
|
||||
for tf in self.config.confirmation_timeframes:
|
||||
cf_df = dfs.get(tf)
|
||||
if cf_df is None or cf_df.empty:
|
||||
continue
|
||||
try:
|
||||
cf_df = self.data_provider.fetch_ohlcv(symbol, tf)
|
||||
cf_analysis = self.tech_engine.analyze(cf_df, symbol, tf.value)
|
||||
report.confirmation_analyses.append(cf_analysis)
|
||||
report.mtf_signals[tf.value] = cf_analysis.composite_signal
|
||||
@@ -189,35 +184,51 @@ class SignalAggregator:
|
||||
except Exception as e:
|
||||
logger.warning(f"Confirmation TF {tf.value} failed for {symbol}: {e}")
|
||||
|
||||
# ── 3. Multi-Timeframe Agreement Check ─────────────────────
|
||||
# ── 3. MTF agreement ───────────────────────────────────────
|
||||
report.mtf_agreement = self._check_mtf_agreement(report)
|
||||
|
||||
# ── 4. Fundamental Context ─────────────────────────────────
|
||||
event_risk, event_desc = self.news_engine.get_event_risk_level(symbol, context)
|
||||
# ── 4. Fundamental context (optional) ──────────────────────
|
||||
if use_news:
|
||||
news = self._get_news_engine()
|
||||
if context is None:
|
||||
context = news.get_fundamental_context()
|
||||
event_risk, event_desc = news.get_event_risk_level(symbol, context)
|
||||
report.event_risk = event_risk
|
||||
report.event_description = event_desc
|
||||
|
||||
sentiment, sent_desc = self.news_engine.get_pair_sentiment(symbol, context)
|
||||
sentiment, sent_desc = news.get_pair_sentiment(symbol, context)
|
||||
report.news_sentiment = sentiment
|
||||
report.news_description = sent_desc
|
||||
else:
|
||||
report.event_risk = "clear"
|
||||
report.event_description = ""
|
||||
report.news_sentiment = 0.0
|
||||
report.news_description = ""
|
||||
|
||||
# ── 5. Spread Estimate ─────────────────────────────────────
|
||||
# ── 5. Spread estimate (only meaningful in live mode) ──────
|
||||
if use_news:
|
||||
try:
|
||||
report.spread_estimate = self.data_provider.get_spread_estimate(symbol)
|
||||
except Exception:
|
||||
report.spread_estimate = 0.0
|
||||
else:
|
||||
# Backtest sets spread externally via BacktestConfig
|
||||
report.spread_estimate = 0.0
|
||||
|
||||
# ── 6. Compute Final Signal ────────────────────────────────
|
||||
# ── 6. Final signal ────────────────────────────────────────
|
||||
self._compute_final_signal(report)
|
||||
|
||||
# ── 7. Generate Trade Setup (if signal is actionable) ──────
|
||||
# ── 7. Trade setup for actionable signals ──────────────────
|
||||
if report.signal_quality in ("A+", "A", "B"):
|
||||
report.trade_setup = self._generate_trade_setup(report, pip_value)
|
||||
|
||||
# ── 8. Collect Reasons ─────────────────────────────────────
|
||||
# ── 8. Reasons ─────────────────────────────────────────────
|
||||
self._collect_reasons(report)
|
||||
|
||||
return report
|
||||
|
||||
# ── Internals ──────────────────────────────────────────────────
|
||||
|
||||
def _check_mtf_agreement(self, report: SignalReport) -> bool:
|
||||
"""Check if multiple timeframes agree on direction."""
|
||||
signals = list(report.mtf_signals.values())
|
||||
if len(signals) < 2:
|
||||
return False
|
||||
@@ -229,28 +240,17 @@ class SignalAggregator:
|
||||
return bullish >= threshold or bearish >= threshold
|
||||
|
||||
def _compute_final_signal(self, report: SignalReport):
|
||||
"""
|
||||
Compute the final signal by combining all layers.
|
||||
|
||||
Priority:
|
||||
1. Event risk can downgrade or block signals
|
||||
2. MTF agreement boosts confidence
|
||||
3. News sentiment adds a small bias
|
||||
4. Quality grade combines everything
|
||||
"""
|
||||
score = report.primary_score
|
||||
confidence = report.primary_confidence
|
||||
|
||||
# ── MTF adjustment ─────────────────────────────────────────
|
||||
# MTF
|
||||
if report.mtf_agreement:
|
||||
# All timeframes agree — boost confidence and blend scores
|
||||
confidence = min(confidence + 15, 95)
|
||||
all_scores = list(report.mtf_scores.values())
|
||||
if all_scores:
|
||||
mtf_avg = sum(all_scores) / len(all_scores)
|
||||
score = score * 0.6 + mtf_avg * 0.4
|
||||
else:
|
||||
# Timeframes disagree — reduce confidence
|
||||
confidence *= 0.7
|
||||
if len(report.mtf_scores) >= 2:
|
||||
scores = list(report.mtf_scores.values())
|
||||
@@ -260,35 +260,33 @@ class SignalAggregator:
|
||||
report.warnings.append("Timeframes CONFLICT — mixed signals across timeframes")
|
||||
confidence *= 0.6
|
||||
|
||||
# ── News sentiment adjustment ──────────────────────────────
|
||||
# News sentiment
|
||||
if abs(report.news_sentiment) > 0.2:
|
||||
# Sentiment aligns with technical
|
||||
if (report.news_sentiment > 0 and score > 0) or \
|
||||
(report.news_sentiment < 0 and score < 0):
|
||||
confidence = min(confidence + 5, 95)
|
||||
# Sentiment conflicts with technical
|
||||
elif (report.news_sentiment > 0 and score < -10) or \
|
||||
(report.news_sentiment < 0 and score > 10):
|
||||
report.warnings.append("News sentiment CONFLICTS with technical signal")
|
||||
confidence *= 0.85
|
||||
|
||||
# ── Event risk adjustment ──────────────────────────────────
|
||||
# Event risk
|
||||
if report.event_risk == "danger":
|
||||
report.warnings.append(f"EVENT RISK: {report.event_description}")
|
||||
confidence *= 0.3 # Heavily penalize — event will disrupt price action
|
||||
confidence *= 0.3
|
||||
report.action_recommendation = f"WAIT — {report.event_description}"
|
||||
elif report.event_risk == "caution":
|
||||
report.warnings.append(f"Upcoming event: {report.event_description}")
|
||||
confidence *= 0.7
|
||||
|
||||
# ── Volatility regime adjustment ───────────────────────────
|
||||
# Volatility regime
|
||||
if report.volatility_regime == "extreme":
|
||||
report.warnings.append("EXTREME volatility — wider stops needed, reduced position size")
|
||||
confidence *= 0.8
|
||||
elif report.volatility_regime == "low":
|
||||
report.warnings.append("Low volatility — potential for breakout, tight ranges")
|
||||
|
||||
# ── Final signal determination ─────────────────────────────
|
||||
# Final signal
|
||||
if score >= 40 and confidence >= 65:
|
||||
report.final_signal = SignalStrength.STRONG_BUY
|
||||
elif score >= 15 and confidence >= 50:
|
||||
@@ -303,7 +301,7 @@ class SignalAggregator:
|
||||
report.final_score = score
|
||||
report.final_confidence = confidence
|
||||
|
||||
# ── Quality grade ──────────────────────────────────────────
|
||||
# Quality grade
|
||||
abs_score = abs(score)
|
||||
if abs_score >= 50 and confidence >= 75 and report.mtf_agreement:
|
||||
report.signal_quality = "A+"
|
||||
@@ -318,8 +316,7 @@ class SignalAggregator:
|
||||
else:
|
||||
report.signal_quality = "F"
|
||||
|
||||
# ── Action recommendation ──────────────────────────────────
|
||||
if not report.action_recommendation: # not already set by event risk
|
||||
if not report.action_recommendation:
|
||||
sig_name = report.final_signal.name.replace("_", " ")
|
||||
if report.signal_quality in ("A+", "A"):
|
||||
report.action_recommendation = f"{sig_name} — High-quality setup, consider entry"
|
||||
@@ -331,35 +328,29 @@ class SignalAggregator:
|
||||
report.action_recommendation = "NO TRADE — No clear signal"
|
||||
|
||||
def _generate_trade_setup(self, report: SignalReport, pip_value: float) -> Optional[TradeSetup]:
|
||||
"""Generate entry, SL, TP levels for actionable signals."""
|
||||
if not report.primary_analysis:
|
||||
return None
|
||||
|
||||
price = report.current_price
|
||||
atr = report.primary_analysis.key_levels.get("atr", 0)
|
||||
|
||||
if atr == 0:
|
||||
return None
|
||||
|
||||
is_buy = report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY)
|
||||
rr = self.config.signals.default_rr_ratio
|
||||
sl_mult = self.config.signals.atr_sl_multiplier
|
||||
|
||||
# SL = 1.5x ATR from entry (adaptive to volatility)
|
||||
sl_distance = atr * 1.5
|
||||
sl_distance = atr * sl_mult
|
||||
tp1_distance = sl_distance * rr
|
||||
tp2_distance = sl_distance * rr * 1.5
|
||||
|
||||
if is_buy:
|
||||
entry = price
|
||||
sl = price - sl_distance
|
||||
tp1 = price + tp1_distance
|
||||
tp2 = price + tp2_distance
|
||||
entry, sl = price, price - sl_distance
|
||||
tp1, tp2 = price + tp1_distance, price + tp2_distance
|
||||
direction = "BUY"
|
||||
else:
|
||||
entry = price
|
||||
sl = price + sl_distance
|
||||
tp1 = price - tp1_distance
|
||||
tp2 = price - tp2_distance
|
||||
entry, sl = price, price + sl_distance
|
||||
tp1, tp2 = price - tp1_distance, price - tp2_distance
|
||||
direction = "SELL"
|
||||
|
||||
risk_pips = sl_distance / pip_value
|
||||
@@ -380,21 +371,12 @@ class SignalAggregator:
|
||||
)
|
||||
|
||||
def _collect_reasons(self, report: SignalReport):
|
||||
"""
|
||||
Collect bullish/bearish reasons from all indicators.
|
||||
|
||||
Each indicator's description is captured once via the indicators
|
||||
dict. Pattern signals (candlestick) are already included in the
|
||||
'candlestick' indicator description, so we do NOT re-process
|
||||
pattern_signals to avoid duplicate entries.
|
||||
"""
|
||||
if not report.primary_analysis:
|
||||
return
|
||||
|
||||
for name, ind in report.primary_analysis.indicators.items():
|
||||
if name.startswith("_") or ind.weight == 0:
|
||||
continue
|
||||
|
||||
if ind.signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
|
||||
prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_BUY else ""
|
||||
report.bullish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
|
||||
@@ -402,7 +384,6 @@ class SignalAggregator:
|
||||
prefix = "STRONG: " if ind.signal == SignalStrength.STRONG_SELL else ""
|
||||
report.bearish_reasons.append(f"{prefix}{ind.name}: {ind.description}")
|
||||
|
||||
# Add news context
|
||||
if report.news_sentiment > 0.1:
|
||||
report.bullish_reasons.append(f"News: {report.news_description}")
|
||||
elif report.news_sentiment < -0.1:
|
||||
|
||||
Reference in New Issue
Block a user