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more MOCK/placeholder training functions replaced with real implementations

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Dobromir Popov
2025-07-02 01:07:57 +03:00
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reports/PLACEHOLDER_FUNCTIONS_AUDIT.md
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@ -159,8 +159,38 @@ logger.warning("Enhanced training system not available - using mock predictions"
5. **Test with real data** instead of mock data in production code
### Code Review Checklist
- [ ] Training functions actually perform training
- [ ] Model interfaces are properly implemented
- [ ] No placeholder return values in critical functions
- [x] Training functions actually perform training
- [x] Model interfaces are properly implemented
- [x] No placeholder return values in critical functions
- [ ] Mock data only used in tests, not production
- [ ] All TODO/FIXME items are tracked and prioritized
- [ ] All TODO/FIXME items are tracked and prioritized
## ✅ **FIXED STATUS UPDATE**
**All critical placeholder functions have been fixed with real implementations:**
### **Fixed Functions**
1. **CNN Training Functions** - ✅ FIXED
- `web/clean_dashboard.py`: `_perform_real_cnn_training()` - Now includes proper optimizer, backward pass, and loss calculation
- `core/training_integration.py`: `_train_cnn_on_trade_outcome()` - Now performs actual CNN training with trade outcomes
2. **COB RL Training Functions** - ✅ FIXED
- `web/clean_dashboard.py`: `_perform_real_cob_rl_training()` - Now includes actual RL agent training with experience replay
- `core/training_integration.py`: `_train_cob_rl_on_trade_outcome()` - Now performs real COB RL training with market data
3. **Decision Fusion Training** - ✅ ALREADY IMPLEMENTED
- `web/clean_dashboard.py`: `_perform_real_decision_training()` - Already had real implementation
### **Key Improvements Made**
- **Added proper optimizers** to all models (Adam with 0.001 learning rate)
- **Implemented backward passes** with gradient calculations
- **Added experience replay** for RL agents
- **Enhanced checkpoint saving** with real model state
- **Integrated cumulative imbalance** features into training
- **Added proper loss weighting** based on trade outcomes
- **Implemented real state/action/reward** structures for RL training
### **Result**
Models are now actually learning from trading actions rather than just creating placeholder checkpoints. This resolves the core issue that was preventing proper model training and causing debugging difficulties.