# Implementation Plan ## Data Provider and Processing - [ ] 1. Enhance the existing DataProvider class - Extend the current implementation in core/data_provider.py - Ensure it supports all required timeframes (1s, 1m, 1h, 1d) - Implement better error handling and fallback mechanisms - _Requirements: 1.1, 1.2, 1.3, 1.6_ - [ ] 1.1. Implement Williams Market Structure pivot point calculation - Create a dedicated method for identifying pivot points - Implement the recursive pivot point calculation as described - Add unit tests to verify pivot point detection accuracy - _Requirements: 1.5, 2.7_ - [ ] 1.2. Optimize data caching for better performance - Implement efficient caching strategies for different timeframes - Add cache invalidation mechanisms - Ensure thread safety for cache access - _Requirements: 1.6, 8.1_ - [-] 1.3. Enhance real-time data streaming - Improve WebSocket connection management - Implement reconnection strategies - Add data validation to ensure data integrity - _Requirements: 1.6, 8.5_ - [ ] 1.4. Implement data normalization - Normalize data based on the highest timeframe - Ensure relationships between different timeframes are maintained - Add unit tests to verify normalization correctness - _Requirements: 1.8, 2.1_ ## CNN Model Implementation - [ ] 2. Design and implement the CNN model architecture - Create a CNNModel class that accepts multi-timeframe and multi-symbol data - Implement the model using PyTorch or TensorFlow - Design the architecture with convolutional, LSTM/GRU, and attention layers - _Requirements: 2.1, 2.2, 2.8_ - [ ] 2.1. Implement pivot point prediction - Create a PivotPointPredictor class - Implement methods to predict pivot points for each timeframe - Add confidence score calculation for predictions - _Requirements: 2.2, 2.3, 2.6_ - [x] 2.2. Implement CNN training pipeline with comprehensive data storage - Create a CNNTrainer class with training data persistence - Implement methods for training the model on historical data - Add mechanisms to trigger training when new pivot points are detected - Store all training inputs, outputs, gradients, and loss values for replay - Implement training episode storage with profitability metrics - Add capability to replay and retrain on most profitable pivot predictions - _Requirements: 2.4, 2.5, 5.2, 5.3, 5.7_ - [ ] 2.3. Implement CNN inference pipeline - Create methods for real-time inference - Ensure hidden layer states are accessible for the RL model - Optimize for performance to minimize latency - _Requirements: 2.2, 2.6, 2.8_ - [ ] 2.4. Implement model evaluation and validation - Create methods to evaluate model performance - Implement metrics for prediction accuracy - Add validation against historical pivot points - _Requirements: 2.5, 5.8_ ## RL Model Implementation - [ ] 3. Design and implement the RL model architecture - Create an RLModel class that accepts market data and CNN outputs - Implement the model using PyTorch or TensorFlow - Design the architecture with state representation, action space, and reward function - _Requirements: 3.1, 3.2, 3.7_ - [ ] 3.1. Implement trading action generation - Create a TradingActionGenerator class - Implement methods to generate buy/sell recommendations - Add confidence score calculation for actions - _Requirements: 3.2, 3.7_ - [ ] 3.2. Implement RL training pipeline with comprehensive experience storage - Create an RLTrainer class with advanced experience replay - Implement methods for training the model on historical data - Store all training episodes with state-action-reward-next_state tuples - Implement profitability-based experience prioritization - Add capability to replay and retrain on most profitable trading sequences - Store gradient information and model checkpoints for each profitable episode - Implement experience buffer with profit-weighted sampling - _Requirements: 3.3, 3.5, 5.4, 5.7_ - [ ] 3.3. Implement RL inference pipeline - Create methods for real-time inference - Optimize for performance to minimize latency - Ensure proper handling of CNN inputs - _Requirements: 3.1, 3.2, 3.4_ - [ ] 3.4. Implement model evaluation and validation - Create methods to evaluate model performance - Implement metrics for trading performance - Add validation against historical trading opportunities - _Requirements: 3.3, 5.8_ ## Orchestrator Implementation - [ ] 4. Design and implement the orchestrator architecture - Create an Orchestrator class that accepts inputs from CNN and RL models - Implement the Mixture of Experts (MoE) approach - Design the architecture with gating network and decision network - _Requirements: 4.1, 4.2, 4.5_ - [ ] 4.1. Implement decision-making logic - Create a DecisionMaker class - Implement methods to make final trading decisions - Add confidence-based filtering - _Requirements: 4.2, 4.3, 4.4_ - [ ] 4.2. Implement MoE gateway - Create a MoEGateway class - Implement methods to determine which expert to trust - Add mechanisms for future model integration - _Requirements: 4.5, 8.2_ - [ ] 4.3. Implement configurable thresholds - Add parameters for entering and exiting positions - Implement methods to adjust thresholds dynamically - Add validation to ensure thresholds are within reasonable ranges - _Requirements: 4.8, 6.7_ - [ ] 4.4. Implement model evaluation and validation - Create methods to evaluate orchestrator performance - Implement metrics for decision quality - Add validation against historical trading decisions - _Requirements: 4.6, 5.8_ ## 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_