AI Hedge Fund User Guide

Getting Started

Welcome to the AI Hedge Fund system - a comprehensive algorithmic trading platform combining multiple AI agents, risk management, and portfolio optimization for cryptocurrency markets.

Prerequisites

• Python 3.8+
• Node.js 14+ (for dashboard)
• Docker (optional, for containerized deployment)
• API keys for supported exchanges

Installation

1. Clone the repository:

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

2. Install Python dependencies:

   pip install -r requirements.txt
   

3. Install dashboard dependencies:

   cd dashboard
   npm install
   cd ..
   

4. Configure your API credentials:

   cp src/alpha_pulse/exchanges/credentials/example.yaml src/alpha_pulse/exchanges/credentials/credentials.yaml
   # Edit credentials.yaml with your exchange API keys
   

5. Run the setup script:

   ./setup.sh
   

System Components

1. Trading Agents

The system uses five specialized AI agents:

Agent	Purpose	Configuration
Technical	Chart pattern analysis	config/agents/technical_agent.yaml
Fundamental	Economic data analysis	config/agents/fundamental_agent.yaml
Sentiment	News and social media analysis	config/agents/sentiment_agent.yaml
Value	Long-term value assessment	config/agents/value_agent.yaml
Activist	Market-moving event detection	config/agents/activist_agent.yaml

2. Risk Management

Risk controls are configured in config/risk_management/risk_config.yaml:

• Position Size Limits: Default max 20% per position
• Portfolio Leverage: Default max 1.5x exposure
• Stop Loss: Default ATR-based with 2% max loss
• Drawdown Protection: Reduces exposure when approaching limits

3. Portfolio Management

Portfolio settings are in config/portfolio/portfolio_config.yaml:

• Optimization Strategy: Choose between Mean-Variance, Risk Parity, or Adaptive
• Rebalancing Schedule: Set frequency and threshold
• Target Metrics: Set optimization goals (Sharpe, Sortino, etc.)

4. Dashboard

The dashboard provides real-time monitoring and control:

• Portfolio View: Current allocations and performance
• Agent Insights: Signals from each agent
• Risk Metrics: Current risk exposure and limits
• System Health: Component status and data flow
• Alerts: System notifications and important events

Usage Guides

Running the System

For a complete demo with all fixes applied:

./run_fixed_demo.sh

For individual components:

# API only
python src/scripts/run_api.py

# Dashboard only
cd dashboard && npm start

# Trading engine
python -m alpha_pulse.main

Backtesting Strategies

1. Configure your backtest in examples/trading/demo_backtesting.py
2. Run the backtest:

   python examples/trading/demo_backtesting.py
   

3. View results in the reports/ directory

Adding Custom Agents

1. Create a new agent class in src/alpha_pulse/agents/
2. Implement the Agent interface defined in src/alpha_pulse/agents/interfaces.py
3. Register your agent in src/alpha_pulse/agents/factory.py
4. Add configuration in config/agents/your_agent.yaml

Customizing Risk Controls

1. Edit config/risk_management/risk_config.yaml
2. Adjust parameters like max position size, drawdown limits, etc.
3. For advanced customization, extend RiskManager in src/alpha_pulse/risk_management/manager.py

Troubleshooting

Common Issues

API Connection Errors

• Check your API credentials in credentials.yaml
• Verify exchange status and rate limits
• Check network connectivity

Portfolio Rebalancing Errors

• Ensure sufficient balance on exchange
• Check minimum order size requirements
• Verify portfolio constraints are not too restrictive

Dashboard Connection Issues

• Ensure API is running (python src/scripts/run_api.py)
• Check port availability (default: 8000)
• Verify WebSocket connection in browser console

Support

For additional support:

• Check the documentation in docs/
• Run example scripts in examples/
• Consult the API documentation at http://localhost:8000/docs when the API is running

Advanced Configuration

Custom Data Sources

To add a new data source:

1. Implement the DataProvider interface in src/alpha_pulse/data_pipeline/providers/
2. Register your provider in src/alpha_pulse/data_pipeline/manager.py
3. Configure your data source in config/data_pipeline_config.yaml

Performance Optimization

For large-scale deployments:

• Use Redis for caching: Configure in config/settings.py
• Enable database sharding: Set in config/database_config.yaml
• Implement GPU acceleration: Configure in config/compute_config.yaml

Logging and Monitoring

• Logs are stored in logs/
• Configure log levels in config/logging_config.yaml
• Metrics are exposed via Prometheus at http://localhost:8000/metrics