# 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