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

Multi-Modal Trading System Design Document

Overview

The Multi-Modal Trading System is designed as an advanced algorithmic trading platform that combines Convolutional Neural Networks (CNN) and Reinforcement Learning (RL) models orchestrated by a decision-making module. The system processes multi-timeframe and multi-symbol market data (primarily ETH and BTC) to generate trading actions.

This design document outlines the architecture, components, data flow, and implementation details for the system based on the requirements and existing codebase.

Architecture

The system follows a modular architecture with clear separation of concerns:

graph TD
    A[Data Provider] --> B[Data Processor]
    B --> C[CNN Model]
    B --> D[RL Model]
    C --> E[Orchestrator]
    D --> E
    E --> F[Trading Executor]
    E --> G[Dashboard]
    F --> G
    H[Risk Manager] --> F
    H --> G

Key Components

1. Data Provider: Centralized component responsible for collecting, processing, and distributing market data from multiple sources.
2. Data Processor: Processes raw market data, calculates technical indicators, and identifies pivot points.
3. CNN Model: Analyzes patterns in market data and predicts pivot points across multiple timeframes.
4. RL Model: Learns optimal trading strategies based on market data and CNN predictions.
5. Orchestrator: Makes final trading decisions based on inputs from both CNN and RL models.
6. Trading Executor: Executes trading actions through brokerage APIs.
7. Risk Manager: Implements risk management features like stop-loss and position sizing.
8. Dashboard: Provides a user interface for monitoring and controlling the system.

Components and Interfaces

1. Data Provider

The Data Provider is the foundation of the system, responsible for collecting, processing, and distributing market data to all other components.

Key Classes and Interfaces

• DataProvider: Central class that manages data collection, processing, and distribution.
• MarketTick: Data structure for standardized market tick data.
• DataSubscriber: Interface for components that subscribe to market data.
• PivotBounds: Data structure for pivot-based normalization bounds.

Implementation Details

The DataProvider class will:

• Collect data from multiple sources (Binance, MEXC)
• Support multiple timeframes (1s, 1m, 1h, 1d)
• Support multiple symbols (ETH, BTC)
• Calculate technical indicators
• Identify pivot points
• Normalize data
• Distribute data to subscribers

Based on the existing implementation in core/data_provider.py, we'll enhance it to:

• Improve pivot point calculation using Williams Market Structure
• Optimize data caching for better performance
• Enhance real-time data streaming
• Implement better error handling and fallback mechanisms

2. CNN Model

The CNN Model is responsible for analyzing patterns in market data and predicting pivot points across multiple timeframes.

Key Classes and Interfaces

• CNNModel: Main class for the CNN model.
• PivotPointPredictor: Interface for predicting pivot points.
• CNNTrainer: Class for training the CNN model.

Implementation Details

The CNN Model will:

• Accept multi-timeframe and multi-symbol data as input
• Output predicted pivot points for each timeframe (1s, 1m, 1h, 1d)
• Provide confidence scores for each prediction
• Make hidden layer states available for the RL model

Architecture:

• Input layer: Multi-channel input for different timeframes and symbols
• Convolutional layers: Extract patterns from time series data
• LSTM/GRU layers: Capture temporal dependencies
• Attention mechanism: Focus on relevant parts of the input
• Output layer: Predict pivot points and confidence scores

Training:

• Use programmatically calculated pivot points as ground truth
• Train on historical data
• Update model when new pivot points are detected
• Use backpropagation to optimize weights

3. RL Model

The RL Model is responsible for learning optimal trading strategies based on market data and CNN predictions.

Key Classes and Interfaces

• RLModel: Main class for the RL model.
• TradingActionGenerator: Interface for generating trading actions.
• RLTrainer: Class for training the RL model.

Implementation Details

The RL Model will:

• Accept market data, CNN predictions, and CNN hidden layer states as input
• Output trading action recommendations (buy/sell)
• Provide confidence scores for each action
• Learn from past experiences to adapt to the current market environment

Architecture:

• State representation: Market data, CNN predictions, CNN hidden layer states
• Action space: Buy, Sell
• Reward function: PnL, risk-adjusted returns
• Policy network: Deep neural network
• Value network: Estimate expected returns

Training:

• Use reinforcement learning algorithms (DQN, PPO, A3C)
• Train on historical data
• Update model based on trading outcomes
• Use experience replay to improve sample efficiency

4. Orchestrator

The Orchestrator is responsible for making final trading decisions based on inputs from both CNN and RL models.

Key Classes and Interfaces

• Orchestrator: Main class for the orchestrator.
• DecisionMaker: Interface for making trading decisions.
• MoEGateway: Mixture of Experts gateway for model integration.

Implementation Details

The Orchestrator will:

• Accept inputs from both CNN and RL models
• Output final trading actions (buy/sell)
• Consider confidence levels of both models
• Learn to avoid entering positions when uncertain
• Allow for configurable thresholds for entering and exiting positions

Architecture:

• Mixture of Experts (MoE) approach
• Gating network: Determine which expert to trust
• Expert models: CNN, RL, and potentially others
• Decision network: Combine expert outputs

Training:

• Train on historical data
• Update model based on trading outcomes
• Use reinforcement learning to optimize decision-making

5. Trading Executor

The Trading Executor is responsible for executing trading actions through brokerage APIs.

Key Classes and Interfaces

• TradingExecutor: Main class for the trading executor.
• BrokerageAPI: Interface for interacting with brokerages.
• OrderManager: Class for managing orders.

Implementation Details

The Trading Executor will:

• Accept trading actions from the orchestrator
• Execute orders through brokerage APIs
• Manage order lifecycle
• Handle errors and retries
• Provide feedback on order execution

Supported brokerages:

• MEXC
• Binance
• Bybit (future extension)

Order types:

• Market orders
• Limit orders
• Stop-loss orders

6. Risk Manager

The Risk Manager is responsible for implementing risk management features like stop-loss and position sizing.

Key Classes and Interfaces

• RiskManager: Main class for the risk manager.
• StopLossManager: Class for managing stop-loss orders.
• PositionSizer: Class for determining position sizes.

Implementation Details

The Risk Manager will:

• Implement configurable stop-loss functionality
• Implement configurable position sizing based on risk parameters
• Implement configurable maximum drawdown limits
• Provide real-time risk metrics
• Provide alerts for high-risk situations

Risk parameters:

• Maximum position size
• Maximum drawdown
• Risk per trade
• Maximum leverage

7. Dashboard

The Dashboard provides a user interface for monitoring and controlling the system.

Key Classes and Interfaces

• Dashboard: Main class for the dashboard.
• ChartManager: Class for managing charts.
• ControlPanel: Class for managing controls.

Implementation Details

The Dashboard will:

• Display real-time market data for all symbols and timeframes
• Display OHLCV charts for all timeframes
• Display CNN pivot point predictions and confidence levels
• Display RL and orchestrator trading actions and confidence levels
• Display system status and model performance metrics
• Provide start/stop toggles for all system processes
• Provide sliders to adjust buy/sell thresholds for the orchestrator

Implementation:

• Web-based dashboard using Flask/Dash
• Real-time updates using WebSockets
• Interactive charts using Plotly
• Server-side processing for all models

Data Models

Market Data

@dataclass
class MarketTick:
    symbol: str
    timestamp: datetime
    price: float
    volume: float
    quantity: float
    side: str  # 'buy' or 'sell'
    trade_id: str
    is_buyer_maker: bool
    raw_data: Dict[str, Any] = field(default_factory=dict)

OHLCV Data

@dataclass
class OHLCVBar:
    symbol: str
    timestamp: datetime
    open: float
    high: float
    low: float
    close: float
    volume: float
    timeframe: str
    indicators: Dict[str, float] = field(default_factory=dict)

Pivot Points

@dataclass
class PivotPoint:
    symbol: str
    timestamp: datetime
    price: float
    type: str  # 'high' or 'low'
    level: int  # Pivot level (1, 2, 3, etc.)
    confidence: float = 1.0

Trading Actions

@dataclass
class TradingAction:
    symbol: str
    timestamp: datetime
    action: str  # 'buy' or 'sell'
    confidence: float
    source: str  # 'rl', 'cnn', 'orchestrator'
    price: Optional[float] = None
    quantity: Optional[float] = None
    reason: Optional[str] = None

Model Predictions

@dataclass
class CNNPrediction:
    symbol: str
    timestamp: datetime
    pivot_points: List[PivotPoint]
    hidden_states: Dict[str, Any]
    confidence: float

@dataclass
class RLPrediction:
    symbol: str
    timestamp: datetime
    action: str  # 'buy' or 'sell'
    confidence: float
    expected_reward: float

Error Handling

Data Collection Errors

• Implement retry mechanisms for API failures
• Use fallback data sources when primary sources are unavailable
• Log all errors with detailed information
• Notify users through the dashboard

Model Errors

• Implement model validation before deployment
• Use fallback models when primary models fail
• Log all errors with detailed information
• Notify users through the dashboard

Trading Errors

• Implement order validation before submission
• Use retry mechanisms for order failures
• Implement circuit breakers for extreme market conditions
• Log all errors with detailed information
• Notify users through the dashboard

Testing Strategy

Unit Testing

• Test individual components in isolation
• Use mock objects for dependencies
• Focus on edge cases and error handling

Integration Testing

• Test interactions between components
• Use real data for testing
• Focus on data flow and error propagation

System Testing

• Test the entire system end-to-end
• Use real data for testing
• Focus on performance and reliability

Backtesting

• Test trading strategies on historical data
• Measure performance metrics (PnL, Sharpe ratio, etc.)
• Compare against benchmarks

Live Testing

• Test the system in a live environment with small position sizes
• Monitor performance and stability
• Gradually increase position sizes as confidence grows

Implementation Plan

The implementation will follow a phased approach:

1. Phase 1: Data Provider

   • Implement the enhanced data provider
   • Implement pivot point calculation
   • Implement technical indicator calculation
   • Implement data normalization

2. Phase 2: CNN Model

   • Implement the CNN model architecture
   • Implement the training pipeline
   • Implement the inference pipeline
   • Implement the pivot point prediction

3. Phase 3: RL Model

   • Implement the RL model architecture
   • Implement the training pipeline
   • Implement the inference pipeline
   • Implement the trading action generation

4. Phase 4: Orchestrator

   • Implement the orchestrator architecture
   • Implement the decision-making logic
   • Implement the MoE gateway
   • Implement the confidence-based filtering

5. Phase 5: Trading Executor

   • Implement the trading executor
   • Implement the brokerage API integrations
   • Implement the order management
   • Implement the error handling

6. Phase 6: Risk Manager

   • Implement the risk manager
   • Implement the stop-loss functionality
   • Implement the position sizing
   • Implement the risk metrics

7. Phase 7: Dashboard

   • Implement the dashboard UI
   • Implement the chart management
   • Implement the control panel
   • Implement the real-time updates

8. Phase 8: Integration and Testing

   • Integrate all components
   • Implement comprehensive testing
   • Fix bugs and optimize performance
   • Deploy to production

Monitoring and Visualization

TensorBoard Integration (Future Enhancement)

A comprehensive TensorBoard integration has been designed to provide detailed training visualization and monitoring capabilities:

Features

• Training Metrics Visualization: Real-time tracking of model losses, rewards, and performance metrics
• Feature Distribution Analysis: Histograms and statistics of input features to validate data quality
• State Quality Monitoring: Tracking of comprehensive state building (13,400 features) success rates
• Reward Component Analysis: Detailed breakdown of reward calculations including PnL, confidence, volatility, and order flow
• Model Performance Comparison: Side-by-side comparison of CNN, RL, and orchestrator performance

Implementation Status

• Completed: TensorBoardLogger utility class with comprehensive logging methods
• Completed: Integration points in enhanced_rl_training_integration.py
• Completed: Enhanced run_tensorboard.py with improved visualization options
• Status: Ready for deployment when system stability is achieved

Usage

# Start TensorBoard dashboard
python run_tensorboard.py

# Access at http://localhost:6006
# View training metrics, feature distributions, and model performance

Benefits

• Real-time validation of training process
• Early detection of training issues
• Feature importance analysis
• Model performance comparison
• Historical training progress tracking

Note: TensorBoard integration is currently deprioritized in favor of system stability and core model improvements. It will be activated once the core training system is stable and performing optimally.

Conclusion

This design document outlines the architecture, components, data flow, and implementation details for the Multi-Modal Trading System. The system is designed to be modular, extensible, and robust, with a focus on performance, reliability, and user experience.

The implementation will follow a phased approach, with each phase building on the previous one. The system will be thoroughly tested at each phase to ensure that it meets the requirements and performs as expected.

The final system will provide traders with a powerful tool for analyzing market data, identifying trading opportunities, and executing trades with confidence.