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 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

4. GPU Optimization & Performance

   • Fixed GPU detection and utilization during training
   • Added GPU memory monitoring during training
   • Implemented mixed precision training for faster GPU-based training
   • Optimized batch sizes for GPU training

5. Trading Metrics & Monitoring

   • Added trade signal rate display and tracking
   • Implemented counter for actions per second/minute/hour
   • Added visualization of trading frequency over time
   • Created moving average of trade signals to show trends

6. Reward Function Optimization

   • Revised reward function to better balance profit and risk
   • Implemented progressive rewards based on holding time
   • Added penalty for frequent trading (to reduce noise)
   • Implemented risk-adjusted returns (Sharpe ratio) in reward calculation

Future Enhancements

1. 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 information

2. 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

3. 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

4. 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 sizing

5. 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 events

6. Model Interpretability

   • Add visualization for model decision making
   • Implement feature importance analysis
   • Add attention visualization for key price patterns
   • Create explainable AI components

7. 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

8. 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

Models

how we manage our training W&B checkpoints? we need to clean up old checlpoints. for every model we keep 5 checkpoints maximum and rotate them. by default we always load te best, and during training when we save new we discard the 6th ordered by performance

add integration of the checkpoint manager to all training pipelines

skip creating examples or documentation by code. just make sure we use the manager when we run our main training pipeline (with the main dashboard/📊 Enhanced Web Dashboard/main.py) . remove wandb integration from the training pipeline

do we load the best model for each model type? or we do a cold start each time?

UI we stopped showing executed trades on the chart. let's add them back . update chart every second as well. the list with closed trades is not updated. clear session button does not clear all data.

fix the dash. it still flickers every 10 seconds for a second. update the chart every second. maintain zoom and position of the chart if possible. set default chart to 15 minutes, but allow zoom out to the current 5 hours (keep the data cached)

Training

how effective is our training? show current loss and accuracy on the chart. also show currently loaded models for each model type

Training what are our rewards and penalties in the RL training pipeline? reprt them so we can evaluate them and make sure they are working as expected and do improvements

allow models to be dynamically loaded and unloaded from the webui (orchestrator)

show cob data in the dashboard over ws

report and audit rewards and penalties in the RL training pipeline

clean dashboard

initial dash loads 180 historical candles, but then we drop them when we get the live ones. all od them instead of just the last. so in one minute we have a 2 candles chart :) use existing checkpoint manager if it;s not too bloated as well. otherwise re-implement clean one where we keep rotate up to 5 checkpoints - best if we can reliably measure performance, otherwise latest 5

✅ Trading Integration

• Recent signals show with confidence levels
• Manual BUY/SELL buttons work
• Executed vs blocked signals displayed
• Current position shows correctly
• Session P&L updates in real-time

✅ COB Integration

• System status shows "COB: Active"
• ETH/USDT COB data displays
• BTC/USDT COB data displays
• Order book metrics update

✅ Training Pipeline

• CNN model status shows "Active"
• RL model status shows "Training"
• Training metrics update
• Model performance data available

✅ Performance

• Chart updates every second
• No flickering or data loss
• WebSocket connection stable
• Memory usage reasonable

we should load the models in a way that we do a back propagation and other model specificic training at realtime as training examples emerge from the realtime data we process. we will save only the best examples (the realtime data dumps we feed to the models) so we can cold start other models if we change the architecture. if it's not working, perform a cleanup of all traininn and trainer code to make it easer to work withm to streamline latest changes and to simplify and refactor it

let's also work on the transformer model - we will add a candlestick tokenizer that will use 8 dimentional vectors to represent candlesticks: 5 dim for OHLCV data, 1 for the timestamp, timeframe and symbol

also, adjust our bybit api so we trade with usdt futures - where we can have up to 50x leverage. on spots we can have 10x max

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1. on the dash buy/sell buttons do not open/close positions in live mode .

2. we also need to fix our Current Order Book data shown on the dash - it is not consistent ande definitely not fast/low latency. let's store all COB data aggregated to 1S buckets and 0.2s sec ticks. show COB datasource updte rate

3. we don't calculate the COB imbalance correctly - we have MA with 4 time windows.

4. we have some more work on the models statistics and overview but we can focust there later when we fix the other issues

5. audit and backtest if calculate_williams_pivot_points works correctly. show pivot points on the dash on the 1m candlesticks

can we enhance our RL reward/punish to promote closing loosing trades and keep winning ones taking into account the predicted price direction and conviction. For example the more loosing a open position is the more we should be biased to closing it. but if the models predict with high certainty that there will be a big move up we will be more tolerant to a drawdown. and the opposite - we should be inclined to close winning trades but keep them as long as the price goes up and we project more upside. Do you think there is a smart way to implement that in the current RL and other training pipelines? I want it more to be a part of a proper rewardfunction bias rather than a algorithmic calculation on the post signal processing as I prefer that this is a behaviour the moedl learns and is adapted to the current condition without hard bowndaries. THINK REALY HARD

do we evaluate and reward/punish each model at each reference?

in our realtime Reinforcement learning training how do we calculate the score (reward/penalty?) Let's use the mean squared difference between the prediction and the empirical outcome. We should do a training run at each inference which will use the last inference's prediction and the current price as outcome. do that up to 6 last predictions and calculating accuracity separately to have a better picture of the ability to predict couple of timeframes in the future. additionally to the frequent inference every 1 or 5s (i forgot the curent CNN rate) do an inference at each new timeframe interval. model should get the full data (multi timeframe - ETH (main) 1s 1m 1h 1d and 1m for BTC, SPX and one more) but should also know on what timeframe it is predicting. we predict only on the main symbol - so in 4 timeframes. bur on every hour we will do 4 inferences - one for each timeframe