Multi-Agent Trading System

A comprehensive trading system using multiple specialized agents for market analysis, signal generation, and risk management.

Architecture Overview

The system consists of 6 main layers with specialized agents:

1. Data/Universe & Preprocessing Layer

• Data Universe Agent: Defines investment universe, fetches and cleans multi-frequency data
• Supports equities, ETFs, futures, options, FX, crypto, and cross-asset data
• Handles data validation, outlier filtering, and timestamp harmonization

2. Feature Engineering & Model Signal Generation

• Technical Analysis Agent: Extracts classic and exotic indicators, patterns, and statistical features
• ML Ensemble Agent: Runs custom ML/AI models including LLMs, ensemble methods, and uncertainty quantification

3. Strategy Modules (Explicit, Separable)

• Momentum Strategy Agent: Multi-factor momentum, sector rotation, breakouts
• Statistical Arbitrage Agent: Pairs trading, mean reversion, cointegration
• Event-Driven Agent: Earnings, macro events, news sentiment analysis
• Options Strategy Agent: Multi-leg strategies, volatility analysis
• Cross-Asset Agent: Portfolio context, correlation analysis

4. Multi-Layer Synthesis, Validation, and Risk

• Signal Synthesis Agent: Combines and arbitrates between strategy signals
• Risk Management Agent: Advanced risk modeling, scenario analysis, stress testing
• Ranking & Filtering Agent: Multi-metric ranking with user preferences

5. Output/Justification, Logging & Audit

• Recommendation Agent: Full rationale with chain-of-thought explanations
• Logging Agent: Comprehensive audit trails for compliance and improvement

Features

• Multi-Asset Support: Equities, ETFs, options, futures, crypto
• Multi-Frequency Analysis: Daily, weekly, monthly data harmonization
• Advanced Technical Analysis: 20+ indicators, pattern recognition
• Machine Learning Integration: Ensemble methods, LLM integration
• Comprehensive Risk Management: VaR, stress testing, scenario analysis
• Full Audit Trail: Complete decision tracking for compliance
• Modular Architecture: Easily extensible and maintainable

Installation

Prerequisites

• Python 3.11+
• Docker & Docker Compose (optional)
• API keys for data providers (Alpha Vantage recommended)

Local Installation

1. Clone the repository:

git clone <repository-url>
cd trading_system

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys and configuration

5. Run the system:

python main.py

Docker Installation

1. Clone and configure:

git clone <repository-url>
cd trading_system
cp .env.example .env
# Edit .env with your configuration

2. Start with Docker Compose:

docker-compose up -d

This will start:

• Trading System application
• PostgreSQL database
• Redis cache
• Jupyter Lab (port 8888)
• Prometheus monitoring (port 9090)
• Grafana dashboards (port 3000)

Configuration

Environment Variables

Create a .env file with the following variables:

# API Keys
ALPHA_VANTAGE_API_KEY=your_api_key_here
QUANDL_API_KEY=your_api_key_here

# Database
DATABASE_URL=postgresql://user:password@localhost:5432/trading_db
REDIS_URL=redis://localhost:6379/0

# LLM APIs (optional)
OPENAI_API_KEY=your_openai_key_here
ANTHROPIC_API_KEY=your_anthropic_key_here

# Environment
ENVIRONMENT=development
DEBUG=true
LOG_LEVEL=INFO

Configuration File

Modify config/config.yaml to customize:

• Asset universe criteria
• Risk management parameters
• Strategy weights and thresholds
• Output preferences

Usage

Basic Usage

from datetime import datetime, timedelta
from main import TradingSystem

# Initialize system
system = TradingSystem()

# Define analysis period
end_date = datetime.now()
start_date = end_date - timedelta(days=365)

# Run analysis
results = await system.run_analysis(
    start_date=start_date,
    end_date=end_date,
    asset_classes=["equities", "etfs"],
    exchanges=["NYSE", "NASDAQ"],
    custom_symbols=["AAPL", "GOOGL", "MSFT"]
)

Advanced Configuration

# Custom universe configuration
universe_config = {
    "equities": {
        "market_cap_min": 5e9,  # $5B minimum
        "volume_min": 2e6,      # 2M daily volume
        "sectors": ["Technology", "Healthcare"]
    },
    "risk_management": {
        "max_portfolio_risk": 0.015,  # 1.5% daily VaR
        "max_single_position": 0.03   # 3% max position
    }
}

Output Format

The system provides structured recommendations:

{
  "rank": 1,
  "strategy_name": "AAPL Momentum Breakout",
  "signal": "BUY",
  "strategies_used": ["Momentum", "Technical", "ML"],
  "expected_return": 0.08,
  "volatility": 0.25,
  "sharpe_ratio": 1.45,
  "max_drawdown": 0.12,
  "confirming_factors": [
    "Technical breakout confirmed",
    "ML model 85% confidence",
    "Positive sentiment spike"
  ],
  "rationale": "Strong momentum with technical confirmation...",
  "warnings": ["High volatility period", "Earnings next week"]
}

API Documentation

Core APIs

• Market Data API: Multi-provider market data fetching
• Fundamental Data API: Financial statements and ratios
• News Sentiment API: News analysis and sentiment scoring
• Options Data API: Options chains and Greeks calculation

Agent APIs

Each agent exposes a consistent interface:

# Agent execution
result = await agent.safe_execute(inputs)

# Result structure
{
    "agent_name": "DataUniverseAgent",
    "timestamp": "2024-01-15T10:30:00Z",
    "data": { ... },
    "metadata": { ... },
    "success": true,
    "execution_time_ms": 1250
}

Development

Project Structure

trading_system/
├── agents/                 # Specialized trading agents
│   ├── data_universe/     # Data fetching and cleaning
│   ├── feature_engineering/ # Technical analysis
│   ├── ml_ensemble/       # ML model ensemble
│   ├── strategies/        # Trading strategies
│   ├── synthesis/         # Signal combination
│   ├── risk_management/   # Risk analysis
│   └── output/           # Recommendations
├── core/                  # Core framework
│   ├── base/             # Base classes
│   ├── utils/            # Utilities
│   └── apis/             # External APIs
├── config/               # Configuration files
├── data/                 # Data storage
├── logs/                 # Log files
├── tests/                # Unit tests
└── docs/                 # Documentation

Adding New Agents

1. Create agent class inheriting from BaseAgent:

from core.base.agent import BaseAgent, AgentOutput

class MyCustomAgent(BaseAgent):
    def __init__(self):
        super().__init__("MyCustomAgent", "my_config_section")
    
    async def execute(self, inputs: Dict[str, Any]) -> AgentOutput:
        # Implementation here
        return AgentOutput(
            agent_name=self.name,
            data={"result": "my_result"},
            success=True
        )

2. Register in main system orchestrator
3. Add configuration section to config.yaml
4. Add tests

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=trading_system

# Run specific test
pytest tests/test_data_universe.py

Monitoring

Logs

• Application logs: logs/trading_system.log
• Audit logs: logs/audit.log
• Structured logging with JSON format

Metrics

• Performance metrics via Prometheus
• Custom dashboards in Grafana
• Real-time monitoring of agent execution

Health Checks

# Check system status
curl http://localhost:8000/health

# Check agent status
curl http://localhost:8000/agents/status

Security

• API key management via environment variables
• Input validation and sanitization
• Audit logging for compliance
• Rate limiting on external API calls

Performance

• Asynchronous agent execution
• Concurrent data fetching
• Redis caching for expensive operations
• Optimized database queries

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

[License information here]

Support

For questions and support:

• Create an issue on GitHub
• Check the documentation in docs/
• Review the configuration examples

Roadmap

• Real-time data streaming
• Advanced ML models (transformers, reinforcement learning)
• Portfolio optimization algorithms
• Risk management enhancements
• Web-based dashboard
• Mobile notifications
• Additional asset classes
• Enhanced backtesting framework