gogo2/.kiro/specs/multi-modal-trading-system/tasks.md

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 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