misc

TODO.md

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+# Trading System Enhancement TODO List
+
+## 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
+
+## 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