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+# 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_
+
+- [ ] 2.2. Implement CNN training pipeline
+  - Create a CNNTrainer class
+  - Implement methods for training the model on historical data
+  - Add mechanisms to trigger training when new pivot points are detected
+  - _Requirements: 2.4, 2.5, 5.2, 5.3_
+
+- [ ] 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
+  - Create an RLTrainer class
+  - Implement methods for training the model on historical data
+  - Add experience replay for improved sample efficiency
+  - _Requirements: 3.3, 3.5, 5.4_
+
+- [ ] 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_
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