- Add signal_cache.py module to persist signal data between trade open/close
- Modify execute_trade_v2_adaptive to cache signal info when trade opens
- Update sync_closed_trades_to_tracker to retrieve cached ML features
- Update scheduled_demo_tracker_sync with same ML feature retrieval
This enables proper ML training by capturing:
- base_confidence, enhanced_score, hybrid_score
- signal_quality, market_regime, regime_strength
- session and lot_multiplier
Previously all trades were logged with 0 values for ML features.
After ~50-100 new trades, the ML model can be properly trained.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
