4.6 KiB
4.6 KiB
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
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
Short-term (1-2 weeks)
- Run extended training with enhanced CNN model
- Analyze performance and confidence metrics
- Implement the most promising architectural improvements
Medium-term (1-2 months)
- Implement position sizing and risk management features
- Add meta-learning capabilities
- Optimize training pipeline
Long-term (3+ months)
- Research and implement advanced RL algorithms
- Create ensemble of specialized models
- Integrate fundamental data analysis