Implementation Plan

Enhanced Data Provider and COB Integration

1. Enhance the existing DataProvider class with standardized model inputs
- Extend the current implementation in core/data_provider.py
- Implement standardized COB+OHLCV data frame for all models
- Create unified input format: 300 frames OHLCV (1s, 1m, 1h, 1d) ETH + 300s of 1s BTC
- Integrate with existing multi_exchange_cob_provider.py for COB data
- Requirements: 1.1, 1.2, 1.3, 1.6
1.1. Implement standardized COB+OHLCV data frame for all models
- Create BaseDataInput class with standardized format for all models
- Implement OHLCV: 300 frames of (1s, 1m, 1h, 1d) ETH + 300s of 1s BTC
- Add COB: ±20 buckets of COB amounts in USD for each 1s OHLCV
- Include 1s, 5s, 15s, and 60s MA of COB imbalance counting ±5 COB buckets
- Ensure all models receive identical input format for consistency
- Requirements: 1.2, 1.3, 8.1
1.2. Implement extensible model output storage
- Create standardized ModelOutput data structure
- Support CNN, RL, LSTM, Transformer, and future model types
- Include model-specific predictions and cross-model hidden states
- Add metadata support for extensible model information
- Requirements: 1.10, 8.2
1.3. Enhance Williams Market Structure pivot point calculation
- Extend existing williams_market_structure.py implementation
- Improve recursive pivot point calculation accuracy
- Add unit tests to verify pivot point detection
- Integrate with COB data for enhanced pivot detection
- Requirements: 1.5, 2.7
[-] 1.4. Optimize real-time data streaming with COB integration
- Enhance existing WebSocket connections in enhanced_cob_websocket.py
- Implement 10Hz COB data streaming alongside OHLCV data
- Add data synchronization across different refresh rates
- Ensure thread-safe access to multi-rate data streams
- Requirements: 1.6, 8.5
1.5. Fix WebSocket COB data processing errors
- Fix 'NoneType' object has no attribute 'append' errors in COB data processing
- Ensure proper initialization of data structures in MultiExchangeCOBProvider
- Add validation and defensive checks before accessing data structures
- Implement proper error handling for WebSocket data processing
- Requirements: 1.1, 1.6, 8.5
1.6. Enhance error handling in COB data processing
- Add validation for incoming WebSocket data
- Implement reconnection logic with exponential backoff
- Add detailed logging for debugging COB data issues
- Ensure system continues operation with last valid data during failures
- Requirements: 1.6, 8.5

Enhanced CNN Model Implementation

2. Enhance the existing CNN model with standardized inputs/outputs
- Extend the current implementation in NN/models/enhanced_cnn.py
- Accept standardized COB+OHLCV data frame: 300 frames (1s,1m,1h,1d) ETH + 300s 1s BTC
- Include COB ±20 buckets and MA (1s,5s,15s,60s) of COB imbalance ±5 buckets
- Output BUY/SELL trading action with confidence scores - Requirements: 2.1, 2.2, 2.8, 1.10
2.1. Implement CNN inference with standardized input format
- Accept BaseDataInput with standardized COB+OHLCV format
- Process 300 frames of multi-timeframe data with COB buckets
- Output BUY/SELL recommendations with confidence scores
- Make hidden layer states available for cross-model feeding
- Optimize inference performance for real-time processing
- Requirements: 2.2, 2.6, 2.8, 4.3
2.2. Enhance CNN training pipeline with checkpoint management
- Integrate with checkpoint manager for training progress persistence
- Store top 5-10 best checkpoints based on performance metrics
- Automatically load best checkpoint at startup
- Implement training triggers based on orchestrator feedback
- Store metadata with checkpoints for performance tracking
- Requirements: 2.4, 2.5, 5.2, 5.3, 5.7
2.3. Implement CNN model evaluation and checkpoint optimization
- Create evaluation methods using standardized input/output format
- Implement performance metrics for checkpoint ranking
- Add validation against historical trading outcomes
- Support automatic checkpoint cleanup (keep only top performers)
- Track model improvement over time through checkpoint metadata
- Requirements: 2.5, 5.8, 4.4

Enhanced RL Model Implementation

3. Enhance the existing RL model with standardized inputs/outputs
- Extend the current implementation in NN/models/dqn_agent.py
- Accept standardized COB+OHLCV data frame: 300 frames (1s,1m,1h,1d) ETH + 300s 1s BTC
- Include COB ±20 buckets and MA (1s,5s,15s,60s) of COB imbalance ±5 buckets
- Output BUY/SELL trading action with confidence scores
- Requirements: 3.1, 3.2, 3.7, 1.10
3.1. Implement RL inference with standardized input format
- Accept BaseDataInput with standardized COB+OHLCV format
- Process CNN hidden states and predictions as part of state input
- Output BUY/SELL recommendations with confidence scores
- Include expected rewards and value estimates in output
- Optimize inference performance for real-time processing
- Requirements: 3.2, 3.7, 4.3
3.2. Enhance RL training pipeline with checkpoint management
- Integrate with checkpoint manager for training progress persistence
- Store top 5-10 best checkpoints based on trading performance metrics
- Automatically load best checkpoint at startup
- Implement experience replay with profitability-based prioritization
- Store metadata with checkpoints for performance tracking
- Requirements: 3.3, 3.5, 5.4, 5.7, 4.4
3.3. Implement RL model evaluation and checkpoint optimization
- Create evaluation methods using standardized input/output format
- Implement trading performance metrics for checkpoint ranking
- Add validation against historical trading opportunities
- Support automatic checkpoint cleanup (keep only top performers)
- Track model improvement over time through checkpoint metadata
- Requirements: 3.3, 5.8, 4.4

Enhanced Orchestrator Implementation

4. Enhance the existing orchestrator with centralized coordination
- Extend the current implementation in core/orchestrator.py
- Implement DataSubscriptionManager for multi-rate data streams
- Add ModelInferenceCoordinator for cross-model coordination
- Create ModelOutputStore for extensible model output management
- Add TrainingPipelineManager for continuous learning coordination
- Requirements: 4.1, 4.2, 4.5, 8.1
4.1. Implement data subscription and management system
- Create DataSubscriptionManager class
- Subscribe to 10Hz COB data, OHLCV, market ticks, and technical indicators
- Implement intelligent caching for "last updated" data serving
- Maintain synchronized base dataframe across different refresh rates
- Add thread-safe access to multi-rate data streams
- Requirements: 4.1, 1.6, 8.5
4.2. Implement model inference coordination
- Create ModelInferenceCoordinator class
- Trigger model inference based on data availability and requirements
- Coordinate parallel inference execution for independent models
- Handle model dependencies (e.g., RL waiting for CNN hidden states)
- Assemble appropriate input data for each model type
- Requirements: 4.2, 3.1, 2.1
4.3. Implement model output storage and cross-feeding
- Create ModelOutputStore class using standardized ModelOutput format
- Store CNN predictions, confidence scores, and hidden layer states
- Store RL action recommendations and value estimates
- Support extensible storage for LSTM, Transformer, and future models
- Implement cross-model feeding of hidden states and predictions
- Include "last predictions" from all models in base data input
- Requirements: 4.3, 1.10, 8.2
4.4. Implement training pipeline management
- Create TrainingPipelineManager class
- Call each model's training pipeline with prediction-result pairs
- Manage training data collection and labeling
- Coordinate online learning updates based on real-time performance
- Track prediction accuracy and trigger retraining when needed
- Requirements: 4.4, 5.2, 5.4, 5.7
4.5. Implement enhanced decision-making with MoE
- Create enhanced DecisionMaker class
- Implement Mixture of Experts approach for model integration
- Apply confidence-based filtering to avoid uncertain trades
- Support configurable thresholds for buy/sell decisions
- Consider market conditions and risk parameters in decisions
- Requirements: 4.5, 4.8, 6.7
4.6. Implement extensible model integration architecture
- Create MoEGateway class supporting dynamic model addition
- Support CNN, RL, LSTM, Transformer model types without architecture changes
- Implement model versioning and rollback capabilities
- Handle model failures and fallback mechanisms
- Provide model performance monitoring and alerting
- Requirements: 4.6, 8.2, 8.3

Trading Executor Implementation

5. Design and implement the trading executor
- Create a TradingExecutor class that accepts trading actions from the orchestrator
- Implement order execution through brokerage APIs
- Add order lifecycle management
- Requirements: 7.1, 7.2, 8.6
5.1. Implement brokerage API integrations
- Create a BrokerageAPI interface
- Implement concrete classes for MEXC and Binance
- Add error handling and retry mechanisms
- Requirements: 7.1, 7.2, 8.6
5.2. Implement order management
- Create an OrderManager class
- Implement methods for creating, updating, and canceling orders
- Add order tracking and status updates
- Requirements: 7.1, 7.2, 8.6
5.3. Implement error handling
- Add comprehensive error handling for API failures
- Implement circuit breakers for extreme market conditions
- Add logging and notification mechanisms
- Requirements: 7.1, 7.2, 8.6

Risk Manager Implementation

6. Design and implement the risk manager
- Create a RiskManager class
- Implement risk parameter management
- Add risk metric calculation
- Requirements: 7.1, 7.3, 7.4
6.1. Implement stop-loss functionality
- Create a StopLossManager class
- Implement methods for creating and managing stop-loss orders
- Add mechanisms to automatically close positions when stop-loss is triggered
- Requirements: 7.1, 7.2
6.2. Implement position sizing
- Create a PositionSizer class
- Implement methods for calculating position sizes based on risk parameters
- Add validation to ensure position sizes are within limits
- Requirements: 7.3, 7.7
6.3. Implement risk metrics
- Add methods to calculate risk metrics (drawdown, VaR, etc.)
- Implement real-time risk monitoring
- Add alerts for high-risk situations
- Requirements: 7.4, 7.5, 7.6, 7.8

Dashboard Implementation

7. Design and implement the dashboard UI
- Create a Dashboard class
- Implement the web-based UI using Flask/Dash
- Add real-time updates using WebSockets
- Requirements: 6.1, 6.8
7.1. Implement chart management
- Create a ChartManager class
- Implement methods for creating and updating charts
- Add interactive features (zoom, pan, etc.)
- Requirements: 6.1, 6.2
7.2. Implement control panel
- Create a ControlPanel class
- Implement start/stop toggles for system processes
- Add sliders for adjusting buy/sell thresholds
- Requirements: 6.6, 6.7
7.3. Implement system status display
- Add methods to display training progress
- Implement model performance metrics visualization
- Add real-time system status updates
- Requirements: 6.5, 5.6
7.4. Implement server-side processing
- Ensure all processes run on the server without requiring the dashboard to be open
- Implement background tasks for model training and inference
- Add mechanisms to persist system state
- Requirements: 6.8, 5.5

Integration and Testing

8. Integrate all components
- Connect the data provider to the CNN and RL models
- Connect the CNN and RL models to the orchestrator
- Connect the orchestrator to the trading executor
- Requirements: 8.1, 8.2, 8.3
8.1. Implement comprehensive unit tests
- Create unit tests for each component
- Implement test fixtures and mocks
- Add test coverage reporting
- Requirements: 8.1, 8.2, 8.3
8.2. Implement integration tests
- Create tests for component interactions
- Implement end-to-end tests
- Add performance benchmarks
- Requirements: 8.1, 8.2, 8.3
8.3. Implement backtesting framework
- Create a backtesting environment
- Implement methods to replay historical data
- Add performance metrics calculation
- Requirements: 5.8, 8.1
8.4. Optimize performance
- Profile the system to identify bottlenecks
- Implement optimizations for critical paths
- Add caching and parallelization where appropriate
- Requirements: 8.1, 8.2, 8.3

13 KiB Raw Blame History

Implementation Plan

Enhanced Data Provider and COB Integration

Enhanced CNN Model Implementation

Enhanced RL Model Implementation

Enhanced Orchestrator Implementation

Trading Executor Implementation

Risk Manager Implementation

Dashboard Implementation

Integration and Testing

13 KiB

Raw Blame History