gogo2/TODO.md

🚀 GOGO2 Enhanced Trading System - TODO

📈 PRIORITY TASKS (Real Market Data Only)

1. Real Market Data Enhancement

• Optimize live data refresh rates for 1s timeframes
• Implement data quality validation checks
• Add redundant data sources for reliability
• Enhance WebSocket connection stability

2. Model Architecture Improvements

• Optimize 504M parameter model for faster inference
• Implement dynamic model scaling based on market volatility
• Add attention mechanisms for price prediction
• Enhance multi-timeframe fusion architecture

3. Training Pipeline Optimization

• Implement progressive training on expanding real datasets
• Add real-time model validation against live market data
• Optimize GPU memory usage for larger batch sizes
• Implement automated hyperparameter tuning

4. Risk Management & Real Trading

• Implement position sizing based on market volatility
• Add dynamic leverage adjustment
• Implement stop-loss and take-profit automation
• Add real-time portfolio risk monitoring

5. Performance & Monitoring

• Add real-time performance benchmarking
• Implement comprehensive logging for all trading decisions
• Add real-time PnL tracking and reporting
• Optimize dashboard update frequencies

6. Model Interpretability

• Add visualization for model decision making
• Implement feature importance analysis
• Add attention visualization for CNN layers
• Create real-time 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