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Dobromir Popov
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# Trading System Enhancement TODO List
# Trading System Enhancement TODO List## Implemented Enhancements1. **Enhanced CNN Architecture** - [x] Implemented deeper CNN with residual connections for better feature extraction - [x] Added self-attention mechanisms to capture temporal patterns - [x] Implemented dueling architecture for more stable Q-value estimation - [x] Added more capacity to prediction heads for better confidence estimation2. **Improved Training Pipeline** - [x] Created example sifting dataset to prioritize high-quality training examples - [x] Implemented price prediction pre-training to bootstrap learning - [x] Lowered confidence threshold to allow more trades (0.4 instead of 0.5) - [x] Added better normalization of state inputs3. **Visualization and Monitoring** - [x] Added detailed confidence metrics tracking - [x] Implemented TensorBoard logging for pre-training and RL phases - [x] Added more comprehensive trading statistics4. **GPU Optimization & Performance** - [x] Fixed GPU detection and utilization during training - [x] Added GPU memory monitoring during training - [x] Implemented mixed precision training for faster GPU-based training - [x] Optimized batch sizes for GPU training5. **Trading Metrics & Monitoring** - [x] Added trade signal rate display and tracking - [x] Implemented counter for actions per second/minute/hour - [x] Added visualization of trading frequency over time - [x] Created moving average of trade signals to show trends6. **Reward Function Optimization** - [x] Revised reward function to better balance profit and risk - [x] Implemented progressive rewards based on holding time - [x] Added penalty for frequent trading (to reduce noise) - [x] Implemented risk-adjusted returns (Sharpe ratio) in reward calculation
## Implemented Enhancements
1. **Enhanced CNN Architecture**
- [x] Implemented deeper CNN with residual connections for better feature extraction
- [x] Added self-attention mechanisms to capture temporal patterns
- [x] Implemented dueling architecture for more stable Q-value estimation
- [x] Added more capacity to prediction heads for better confidence estimation
2. **Improved Training Pipeline**
- [x] Created example sifting dataset to prioritize high-quality training examples
- [x] Implemented price prediction pre-training to bootstrap learning
- [x] Lowered confidence threshold to allow more trades (0.4 instead of 0.5)
- [x] Added better normalization of state inputs
3. **Visualization and Monitoring**
- [x] Added detailed confidence metrics tracking
- [x] Implemented TensorBoard logging for pre-training and RL phases
- [x] Added more comprehensive trading statistics
## Future Enhancements
1. **Model Architecture Improvements**
- [ ] Experiment with different residual block configurations
- [ ] Implement Transformer-based models for better sequence handling
- [ ] Try LSTM/GRU layers to combine with CNN for temporal data
- [ ] Implement ensemble methods to combine multiple models
2. **Training Process Improvements**
- [ ] Implement curriculum learning (start with simple patterns, move to complex)
- [ ] Add adversarial training to make model more robust
- [ ] Implement Meta-Learning approaches for faster adaptation
- [ ] Expand pre-training to include extrema detection
3. **Trading Strategy Enhancements**
- [ ] Add position sizing based on confidence levels
- [ ] Implement risk management constraints
- [ ] Add support for stop-loss and take-profit mechanisms
- [ ] Develop adaptive confidence thresholds based on market volatility
4. **Performance Optimizations**
- [ ] Optimize data loading pipeline for faster training
- [ ] Implement distributed training for larger models
- [ ] Profile and optimize inference speed for real-time trading
- [ ] Optimize memory usage for longer training sessions
5. **Research Directions**
- [ ] Explore reinforcement learning algorithms beyond DQN (PPO, SAC, A3C)
- [ ] Research ways to incorporate fundamental data
- [ ] Investigate transfer learning from pre-trained models
- [ ] Study methods to interpret model decisions for better trust
## Future Enhancements1. **Multi-timeframe Price Direction Prediction** - [ ] Extend CNN model to predict price direction for multiple timeframes - [ ] Modify CNN output to predict short, mid, and long-term price directions - [ ] Create data generation method for back-propagation using historical data - [ ] Implement real-time example generation for training - [ ] Feed direction predictions to RL agent as additional state information2. **Model Architecture Improvements** - [ ] Experiment with different residual block configurations - [ ] Implement Transformer-based models for better sequence handling - [ ] Try LSTM/GRU layers to combine with CNN for temporal data - [ ] Implement ensemble methods to combine multiple models3. **Training Process Improvements** - [ ] Implement curriculum learning (start with simple patterns, move to complex) - [ ] Add adversarial training to make model more robust - [ ] Implement Meta-Learning approaches for faster adaptation - [ ] Expand pre-training to include extrema detection4. **Trading Strategy Enhancements** - [ ] Add position sizing based on confidence levels (dynamic sizing based on prediction confidence) - [ ] Implement risk management constraints - [ ] Add support for stop-loss and take-profit mechanisms - [ ] Develop adaptive confidence thresholds based on market volatility - [ ] Implement Kelly criterion for optimal position sizing5. **Training Data & Model Improvements** - [ ] Implement data augmentation for more robust training - [ ] Simulate different market conditions - [ ] Add noise to training data - [ ] Generate synthetic data for rare market events6. **Model Interpretability** - [ ] Add visualization for model decision making - [ ] Implement feature importance analysis - [ ] Add attention visualization for key price patterns - [ ] Create explainable AI components7. **Performance Optimizations** - [ ] Optimize data loading pipeline for faster training - [ ] Implement distributed training for larger models - [ ] Profile and optimize inference speed for real-time trading - [ ] Optimize memory usage for longer training sessions8. **Research Directions** - [ ] Explore reinforcement learning algorithms beyond DQN (PPO, SAC, A3C) - [ ] Research ways to incorporate fundamental data - [ ] Investigate transfer learning from pre-trained models - [ ] Study methods to interpret model decisions for better trust
## Implementation Timeline