kiro design and requirements
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Dobromir Popov
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.kiro/specs/multi-modal-trading-system/design.md
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# Multi-Modal Trading System Design Document
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## Overview
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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.
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This design document outlines the architecture, components, data flow, and implementation details for the system based on the requirements and existing codebase.
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## Architecture
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The system follows a modular architecture with clear separation of concerns:
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```mermaid
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graph TD
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A[Data Provider] --> B[Data Processor]
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B --> C[CNN Model]
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B --> D[RL Model]
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C --> E[Orchestrator]
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D --> E
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E --> F[Trading Executor]
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E --> G[Dashboard]
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F --> G
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H[Risk Manager] --> F
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H --> G
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```
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### Key Components
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1. **Data Provider**: Centralized component responsible for collecting, processing, and distributing market data from multiple sources.
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2. **Data Processor**: Processes raw market data, calculates technical indicators, and identifies pivot points.
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3. **CNN Model**: Analyzes patterns in market data and predicts pivot points across multiple timeframes.
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4. **RL Model**: Learns optimal trading strategies based on market data and CNN predictions.
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5. **Orchestrator**: Makes final trading decisions based on inputs from both CNN and RL models.
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6. **Trading Executor**: Executes trading actions through brokerage APIs.
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7. **Risk Manager**: Implements risk management features like stop-loss and position sizing.
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8. **Dashboard**: Provides a user interface for monitoring and controlling the system.
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## Components and Interfaces
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### 1. Data Provider
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The Data Provider is the foundation of the system, responsible for collecting, processing, and distributing market data to all other components.
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#### Key Classes and Interfaces
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- **DataProvider**: Central class that manages data collection, processing, and distribution.
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- **MarketTick**: Data structure for standardized market tick data.
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- **DataSubscriber**: Interface for components that subscribe to market data.
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- **PivotBounds**: Data structure for pivot-based normalization bounds.
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#### Implementation Details
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The DataProvider class will:
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- Collect data from multiple sources (Binance, MEXC)
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- Support multiple timeframes (1s, 1m, 1h, 1d)
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- Support multiple symbols (ETH, BTC)
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- Calculate technical indicators
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- Identify pivot points
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- Normalize data
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- Distribute data to subscribers
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Based on the existing implementation in `core/data_provider.py`, we'll enhance it to:
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- Improve pivot point calculation using Williams Market Structure
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- Optimize data caching for better performance
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- Enhance real-time data streaming
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- Implement better error handling and fallback mechanisms
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### 2. CNN Model
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The CNN Model is responsible for analyzing patterns in market data and predicting pivot points across multiple timeframes.
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#### Key Classes and Interfaces
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- **CNNModel**: Main class for the CNN model.
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- **PivotPointPredictor**: Interface for predicting pivot points.
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- **CNNTrainer**: Class for training the CNN model.
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#### Implementation Details
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The CNN Model will:
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- Accept multi-timeframe and multi-symbol data as input
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- Output predicted pivot points for each timeframe (1s, 1m, 1h, 1d)
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- Provide confidence scores for each prediction
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- Make hidden layer states available for the RL model
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Architecture:
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- Input layer: Multi-channel input for different timeframes and symbols
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- Convolutional layers: Extract patterns from time series data
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- LSTM/GRU layers: Capture temporal dependencies
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- Attention mechanism: Focus on relevant parts of the input
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- Output layer: Predict pivot points and confidence scores
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Training:
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- Use programmatically calculated pivot points as ground truth
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- Train on historical data
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- Update model when new pivot points are detected
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- Use backpropagation to optimize weights
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### 3. RL Model
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The RL Model is responsible for learning optimal trading strategies based on market data and CNN predictions.
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#### Key Classes and Interfaces
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- **RLModel**: Main class for the RL model.
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- **TradingActionGenerator**: Interface for generating trading actions.
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- **RLTrainer**: Class for training the RL model.
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#### Implementation Details
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The RL Model will:
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- Accept market data, CNN predictions, and CNN hidden layer states as input
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- Output trading action recommendations (buy/sell)
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- Provide confidence scores for each action
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- Learn from past experiences to adapt to the current market environment
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Architecture:
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- State representation: Market data, CNN predictions, CNN hidden layer states
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- Action space: Buy, Sell
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- Reward function: PnL, risk-adjusted returns
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- Policy network: Deep neural network
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- Value network: Estimate expected returns
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Training:
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- Use reinforcement learning algorithms (DQN, PPO, A3C)
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- Train on historical data
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- Update model based on trading outcomes
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- Use experience replay to improve sample efficiency
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### 4. Orchestrator
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The Orchestrator is responsible for making final trading decisions based on inputs from both CNN and RL models.
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#### Key Classes and Interfaces
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- **Orchestrator**: Main class for the orchestrator.
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- **DecisionMaker**: Interface for making trading decisions.
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- **MoEGateway**: Mixture of Experts gateway for model integration.
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#### Implementation Details
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The Orchestrator will:
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- Accept inputs from both CNN and RL models
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- Output final trading actions (buy/sell)
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- Consider confidence levels of both models
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- Learn to avoid entering positions when uncertain
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- Allow for configurable thresholds for entering and exiting positions
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Architecture:
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- Mixture of Experts (MoE) approach
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- Gating network: Determine which expert to trust
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- Expert models: CNN, RL, and potentially others
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- Decision network: Combine expert outputs
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Training:
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- Train on historical data
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- Update model based on trading outcomes
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- Use reinforcement learning to optimize decision-making
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### 5. Trading Executor
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The Trading Executor is responsible for executing trading actions through brokerage APIs.
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#### Key Classes and Interfaces
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- **TradingExecutor**: Main class for the trading executor.
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- **BrokerageAPI**: Interface for interacting with brokerages.
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- **OrderManager**: Class for managing orders.
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#### Implementation Details
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The Trading Executor will:
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- Accept trading actions from the orchestrator
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- Execute orders through brokerage APIs
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- Manage order lifecycle
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- Handle errors and retries
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- Provide feedback on order execution
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Supported brokerages:
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- MEXC
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- Binance
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- Bybit (future extension)
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Order types:
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- Market orders
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- Limit orders
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- Stop-loss orders
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### 6. Risk Manager
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The Risk Manager is responsible for implementing risk management features like stop-loss and position sizing.
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#### Key Classes and Interfaces
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- **RiskManager**: Main class for the risk manager.
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- **StopLossManager**: Class for managing stop-loss orders.
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- **PositionSizer**: Class for determining position sizes.
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#### Implementation Details
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The Risk Manager will:
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- Implement configurable stop-loss functionality
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- Implement configurable position sizing based on risk parameters
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- Implement configurable maximum drawdown limits
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- Provide real-time risk metrics
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- Provide alerts for high-risk situations
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Risk parameters:
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- Maximum position size
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- Maximum drawdown
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- Risk per trade
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- Maximum leverage
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### 7. Dashboard
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The Dashboard provides a user interface for monitoring and controlling the system.
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#### Key Classes and Interfaces
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- **Dashboard**: Main class for the dashboard.
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- **ChartManager**: Class for managing charts.
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- **ControlPanel**: Class for managing controls.
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#### Implementation Details
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The Dashboard will:
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- Display real-time market data for all symbols and timeframes
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- Display OHLCV charts for all timeframes
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- Display CNN pivot point predictions and confidence levels
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- Display RL and orchestrator trading actions and confidence levels
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- Display system status and model performance metrics
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- Provide start/stop toggles for all system processes
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- Provide sliders to adjust buy/sell thresholds for the orchestrator
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Implementation:
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- Web-based dashboard using Flask/Dash
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- Real-time updates using WebSockets
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- Interactive charts using Plotly
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- Server-side processing for all models
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## Data Models
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### Market Data
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```python
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@dataclass
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class MarketTick:
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symbol: str
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timestamp: datetime
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price: float
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volume: float
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quantity: float
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side: str # 'buy' or 'sell'
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trade_id: str
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is_buyer_maker: bool
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raw_data: Dict[str, Any] = field(default_factory=dict)
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```
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### OHLCV Data
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```python
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@dataclass
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class OHLCVBar:
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symbol: str
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timestamp: datetime
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open: float
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high: float
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low: float
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close: float
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volume: float
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timeframe: str
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indicators: Dict[str, float] = field(default_factory=dict)
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```
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### Pivot Points
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```python
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@dataclass
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class PivotPoint:
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symbol: str
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timestamp: datetime
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price: float
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type: str # 'high' or 'low'
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level: int # Pivot level (1, 2, 3, etc.)
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confidence: float = 1.0
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```
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### Trading Actions
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```python
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@dataclass
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class TradingAction:
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symbol: str
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timestamp: datetime
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action: str # 'buy' or 'sell'
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confidence: float
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source: str # 'rl', 'cnn', 'orchestrator'
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price: Optional[float] = None
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quantity: Optional[float] = None
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reason: Optional[str] = None
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```
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### Model Predictions
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```python
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@dataclass
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class CNNPrediction:
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symbol: str
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timestamp: datetime
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pivot_points: List[PivotPoint]
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hidden_states: Dict[str, Any]
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confidence: float
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```
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```python
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@dataclass
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class RLPrediction:
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symbol: str
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timestamp: datetime
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action: str # 'buy' or 'sell'
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confidence: float
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expected_reward: float
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```
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## Error Handling
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### Data Collection Errors
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- Implement retry mechanisms for API failures
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- Use fallback data sources when primary sources are unavailable
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- Log all errors with detailed information
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- Notify users through the dashboard
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### Model Errors
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- Implement model validation before deployment
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- Use fallback models when primary models fail
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- Log all errors with detailed information
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- Notify users through the dashboard
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### Trading Errors
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- Implement order validation before submission
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- Use retry mechanisms for order failures
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- Implement circuit breakers for extreme market conditions
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- Log all errors with detailed information
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- Notify users through the dashboard
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## Testing Strategy
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### Unit Testing
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- Test individual components in isolation
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- Use mock objects for dependencies
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- Focus on edge cases and error handling
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### Integration Testing
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- Test interactions between components
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- Use real data for testing
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- Focus on data flow and error propagation
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### System Testing
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- Test the entire system end-to-end
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- Use real data for testing
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- Focus on performance and reliability
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### Backtesting
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- Test trading strategies on historical data
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- Measure performance metrics (PnL, Sharpe ratio, etc.)
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- Compare against benchmarks
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### Live Testing
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- Test the system in a live environment with small position sizes
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- Monitor performance and stability
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- Gradually increase position sizes as confidence grows
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## Implementation Plan
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The implementation will follow a phased approach:
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1. **Phase 1: Data Provider**
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- Implement the enhanced data provider
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- Implement pivot point calculation
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- Implement technical indicator calculation
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- Implement data normalization
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2. **Phase 2: CNN Model**
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- Implement the CNN model architecture
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- Implement the training pipeline
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- Implement the inference pipeline
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- Implement the pivot point prediction
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3. **Phase 3: RL Model**
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- Implement the RL model architecture
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- Implement the training pipeline
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- Implement the inference pipeline
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- Implement the trading action generation
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4. **Phase 4: Orchestrator**
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- Implement the orchestrator architecture
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- Implement the decision-making logic
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- Implement the MoE gateway
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- Implement the confidence-based filtering
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5. **Phase 5: Trading Executor**
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- Implement the trading executor
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- Implement the brokerage API integrations
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- Implement the order management
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- Implement the error handling
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6. **Phase 6: Risk Manager**
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- Implement the risk manager
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- Implement the stop-loss functionality
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- Implement the position sizing
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- Implement the risk metrics
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7. **Phase 7: Dashboard**
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- Implement the dashboard UI
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- Implement the chart management
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- Implement the control panel
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- Implement the real-time updates
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8. **Phase 8: Integration and Testing**
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- Integrate all components
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- Implement comprehensive testing
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- Fix bugs and optimize performance
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- Deploy to production
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## Conclusion
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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.
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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.
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The final system will provide traders with a powerful tool for analyzing market data, identifying trading opportunities, and executing trades with confidence.
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