AlphaPulse - AI Hedge Fund

AlphaPulse is an advanced algorithmic trading system that combines multiple AI agents, sophisticated risk management, and portfolio optimization to make data-driven investment decisions in cryptocurrency markets.

Features

• Multi-agent architecture combining technical, fundamental, sentiment, and value analysis
• Risk-first approach with multiple layers of risk controls
• Portfolio optimization using modern portfolio theory
• Extensible framework for adding new strategies and data sources
• Real-time monitoring and performance analytics
• Comprehensive alerting system with multiple notification channels

Getting Started

Prerequisites

• Python 3.9+
• Docker and Docker Compose
• PostgreSQL with TimescaleDB (or use Docker setup)
• Redis (or use Docker setup)

Installation

1. Clone the repository:

   git clone https://github.com/your-org/alpha-pulse.git
   cd alpha-pulse

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the database infrastructure:

   ./scripts/setup_database.sh

4. Initialize the database:

   python src/scripts/init_db.py

Running the System

1. Start the data pipeline:

   python -m alpha_pulse.data_pipeline

2. Launch the trading engine:

   python -m alpha_pulse.main

3. Monitor performance:

   python -m alpha_pulse.monitoring

Database Infrastructure

AlphaPulse uses a robust database infrastructure:

• PostgreSQL with TimescaleDB: For relational data and time-series metrics
• Redis: For caching and real-time messaging

Alerting System

AlphaPulse includes a comprehensive alerting system:

• Rule-Based Alerts: Define conditions that trigger alerts based on metric values
• Multiple Notification Channels: Send alerts via email, Slack, SMS, and web interfaces
• Alert History: Store and query alert history with filtering options
• Acknowledgment: Track which alerts have been acknowledged and by whom

Alerting Configuration

The alerting system is configured via YAML:

alerting:
  enabled: true
  check_interval: 60  # seconds
  
  # Configure notification channels
  channels:
    email:
      enabled: true
      smtp_server: "smtp.example.com"
      # Additional email configuration...
    
    slack:
      enabled: true
      webhook_url: "${AP_SLACK_WEBHOOK}"
      # Additional Slack configuration...
    
    sms:
      enabled: true
      account_sid: "${AP_TWILIO_SID}"
      # Additional SMS configuration...
  
  # Define alert rules
  rules:
    - rule_id: "sharpe_ratio_low"
      name: "Low Sharpe Ratio"
      metric_name: "sharpe_ratio"
      condition: "< 0.5"
      severity: "warning"
      message_template: "Sharpe ratio is {value}, below threshold of 0.5"
      channels: ["email", "slack", "web"]

Running the Alerting System

To start the alerting system:

python -m alpha_pulse.monitoring.alerting

• Redis: For caching and real-time messaging

Database Setup

The database infrastructure can be set up using Docker Compose:

docker-compose -f docker-compose.db.yml up -d

This will start:

• PostgreSQL with TimescaleDB extension
• Redis
• PgAdmin (web interface for PostgreSQL)

Database Schema

The database schema includes:

• Users and authentication
• Portfolios and positions
• Trades and orders
• Time-series metrics
• Alerts and notifications

Database Access

The database access layer provides:

• Connection management
• Repository pattern for data access
• ORM models
• Transaction support

Examples

Check out the examples directory for sample scripts:

• examples/database/demo_database.py: Demonstrates database operations
• examples/trading/demo_ai_hedge_fund.py: Demonstrates the AI Hedge Fund
• examples/monitoring/demo_monitoring.py: Demonstrates the monitoring system
• examples/alerting/demo_alerting.py: Demonstrates the alerting system

To run the database demo:

cd examples/database
./run_demo.sh

To run the alerting demo:

cd examples/alerting
./run_demo.sh

## Documentation

- [API Documentation](API_DOCUMENTATION.md)
- [System Architecture](SYSTEM_ARCHITECTURE.md)
- [Hedge Fund Requirements](HEDGE_FUND_REQUIREMENTS.md)
- [Deployment Guide](DEPLOYMENT.md)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.