69 lines
2.9 KiB
Markdown
69 lines
2.9 KiB
Markdown
# Trading System Enhancement TODO List
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## Implemented Enhancements
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1. **Enhanced CNN Architecture**
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- [x] Implemented deeper CNN with residual connections for better feature extraction
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- [x] Added self-attention mechanisms to capture temporal patterns
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- [x] Implemented dueling architecture for more stable Q-value estimation
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- [x] Added more capacity to prediction heads for better confidence estimation
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2. **Improved Training Pipeline**
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- [x] Created example sifting dataset to prioritize high-quality training examples
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- [x] Implemented price prediction pre-training to bootstrap learning
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- [x] Lowered confidence threshold to allow more trades (0.4 instead of 0.5)
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- [x] Added better normalization of state inputs
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3. **Visualization and Monitoring**
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- [x] Added detailed confidence metrics tracking
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- [x] Implemented TensorBoard logging for pre-training and RL phases
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- [x] Added more comprehensive trading statistics
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## Future Enhancements
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1. **Model Architecture Improvements**
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- [ ] Experiment with different residual block configurations
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- [ ] Implement Transformer-based models for better sequence handling
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- [ ] Try LSTM/GRU layers to combine with CNN for temporal data
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- [ ] Implement ensemble methods to combine multiple models
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2. **Training Process Improvements**
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- [ ] Implement curriculum learning (start with simple patterns, move to complex)
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- [ ] Add adversarial training to make model more robust
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- [ ] Implement Meta-Learning approaches for faster adaptation
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- [ ] Expand pre-training to include extrema detection
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3. **Trading Strategy Enhancements**
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- [ ] Add position sizing based on confidence levels
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- [ ] Implement risk management constraints
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- [ ] Add support for stop-loss and take-profit mechanisms
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- [ ] Develop adaptive confidence thresholds based on market volatility
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4. **Performance Optimizations**
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- [ ] Optimize data loading pipeline for faster training
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- [ ] Implement distributed training for larger models
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- [ ] Profile and optimize inference speed for real-time trading
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- [ ] Optimize memory usage for longer training sessions
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5. **Research Directions**
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- [ ] Explore reinforcement learning algorithms beyond DQN (PPO, SAC, A3C)
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- [ ] Research ways to incorporate fundamental data
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- [ ] Investigate transfer learning from pre-trained models
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- [ ] Study methods to interpret model decisions for better trust
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## Implementation Timeline
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### Short-term (1-2 weeks)
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- Run extended training with enhanced CNN model
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- Analyze performance and confidence metrics
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- Implement the most promising architectural improvements
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### Medium-term (1-2 months)
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- Implement position sizing and risk management features
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- Add meta-learning capabilities
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- Optimize training pipeline
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### Long-term (3+ months)
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- Research and implement advanced RL algorithms
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- Create ensemble of specialized models
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- Integrate fundamental data analysis |