Major Issue #1: Confidence Calculation Calibration
- Added calculate_confidence() method with weighted scoring
- Base 40% + Structure 15% + BOS/CHoCH 12% + FVG 8% + OB 10% + Trend 10%
- Capped at 85% (never 100% certain)
Major Issue #2: Pullback Filter ATR-based
- Replaced hardcoded $2, $1.5 thresholds
- Now uses bounce_threshold = 0.15 * ATR
- consolidation_threshold = 0.10 * ATR
Major Issue #3: Smarter Time-based Exit
- Don't cut winners short if profit growing
- Check ML agreement before timeout
- Extend time to 8h if profit > $10 and growing
Major Issue #4: Slippage Validation
- Check actual vs expected price after execution
- Log warning if slippage > 0.15% of price
- Use actual price for position tracking
Major Issue #5: Partial Fill Handling
- Check if filled volume < requested volume
- Log warning with fill ratio
- Use actual volume for position tracking
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Loop: ~1 detik → candle-based (M15) + position check ~10 detik
- Exit Kondisi 3: Golden Time Hold → Early Cut (Smart Hold dihapus)
- AUC rollback threshold: 0.52 → 0.60
- Train/test: tambah 50-bar gap info
- Timer periodik: candle-based intervals
- Performance: split full analysis vs position-check-only
- Golden Time: hapus referensi hold losers
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add model backups from training sessions
- Add training data parquet file
- Add risk state persistence file
- Add research documents (Gemini analysis)
- Update architecture docs
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
main_live.py:
- Switch main loop from time-based (1s) to candle-based (M15)
- Add position-only checks between candles (every 10s)
- Fix memory leak in signal persistence dict (cleanup stale entries)
- Raise auto-retrain rollback AUC threshold from 0.52 to 0.60
src/ml_model.py:
- Add 50-bar gap between train/test split to prevent temporal leakage
src/smart_risk_manager.py:
- Remove dangerous "Smart Hold" behavior (holding losers waiting for golden time)
- Replace with proper early cut logic (loss >30% + negative momentum)
src/smc_polars.py:
- Fix lookahead bias in FVG detection (remove shift(-1), use confirmed bars only)
- Fix lookahead bias in Swing Points (use center=False rolling window)
- Fix lookahead bias in Order Blocks (validate with current bar, not future)
- Enforce minimum 1:2 Risk:Reward ratio on all signals
- Always use current_close as entry price (no stale FVG/OB zone prices)
- Add ATR sanity check with realistic XAUUSD default ($12)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- XGBoost ML model with 37 features for market direction prediction
- Smart Money Concepts (SMC): Order Blocks, FVG, BOS, CHoCH
- HMM market regime detection (trending/ranging/volatile)
- ATR-based stop loss with 1.5 ATR minimum distance
- Broker-level SL protection with fallback
- Time-based exit (max 6 hours per trade)
- Session-aware trading optimized for London/NY overlap
- Auto-retraining based on market conditions
- Telegram notifications and web dashboard
- Backtest results: 63.9% win rate, 2.64 profit factor, 4.83 Sharpe
Backtest period: Jan 2025 - Feb 2026, 654 trades, $4,189 net P/L
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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