gogo2/TODO.md

Trading System Enhancement TODO List

Implemented Enhancements

1. Enhanced CNN Architecture

   • Implemented deeper CNN with residual connections for better feature extraction
   • Added self-attention mechanisms to capture temporal patterns
   • Implemented dueling architecture for more stable Q-value estimation
   • Added more capacity to prediction heads for better confidence estimation

2. Improved Training Pipeline

   • Created example sifting dataset to prioritize high-quality training examples
   • Implemented price prediction pre-training to bootstrap learning
   • Lowered confidence threshold to allow more trades (0.4 instead of 0.5)
   • Added better normalization of state inputs

3. Visualization and Monitoring

   • Added detailed confidence metrics tracking
   • Implemented TensorBoard logging for pre-training and RL phases
   • 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