gogo2/TODO.md

🚀 GOGO2 Enhanced Trading System - TODO

🎯 IMMEDIATE PRIORITIES (System Stability & Core Performance)

1. System Stability & Dashboard

• Ensure dashboard remains stable and responsive during training
• Fix any memory leaks or performance degradation issues
• Optimize real-time data processing to prevent system overload
• Implement graceful error handling and recovery mechanisms
• Monitor and optimize CPU/GPU resource usage

2. Model Training Improvements

• Validate comprehensive state building (13,400 features) is working correctly
• Ensure enhanced reward calculation is improving model performance
• Monitor training convergence and adjust learning rates if needed
• Implement proper model checkpointing and recovery
• Track and improve model accuracy metrics

3. Real Market Data Quality

• Validate data provider is supplying consistent, high-quality market data
• Ensure COB (Change of Bid) integration is working properly
• Monitor WebSocket connections for stability and reconnection logic
• Implement data validation checks to catch corrupted or missing data
• Optimize data caching and retrieval performance

4. Core Trading Logic

• Verify orchestrator is making sensible trading decisions
• Ensure confidence thresholds are properly calibrated
• Monitor position management and risk controls
• Validate trading executor is working reliably
• Track actual vs. expected trading performance

📊 MONITORING & VISUALIZATION (Deferred)

TensorBoard Integration (Ready but Deferred)

• Completed: TensorBoardLogger utility class with comprehensive logging methods
• Completed: Integration in enhanced_rl_training_integration.py for training metrics
• Completed: Enhanced run_tensorboard.py with improved visualization options
• Completed: Feature distribution analysis and state quality monitoring
• Completed: Reward component tracking and model performance comparison

Status: TensorBoard integration is fully implemented and ready for use, but deferred until core system stability is achieved. Once the training system is stable and performing well, TensorBoard can be activated to provide detailed training visualization and monitoring.

Usage (when activated):

python run_tensorboard.py  # Access at http://localhost:6006

Future Monitoring Enhancements

• Real-time performance benchmarking dashboard
• Comprehensive logging for all trading decisions
• Real-time PnL tracking and reporting
• Model interpretability and decision explanation system

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