AutoHedge enables users to rapidly construct autonomous hedge funds by leveraging swarm intelligence and specialized AI agents. It automates critical trading functions including market analysis, risk management, and trade execution, targeting individuals and entities seeking to deploy AI-driven trading strategies with minimal setup. The primary benefit is the acceleration of hedge fund development through an integrated, agent-based framework.

How It Works

The system employs a sophisticated multi-agent architecture. A Director Agent generates trading theses and coordinates strategy, while specialized agents handle specific tasks: a Quant Agent performs technical and statistical analysis, a Risk Management Agent assesses trade risks and determines position sizing, and an Execution Agent implements trades. This modular design allows for focused expertise within each trading function, promoting a structured and robust approach to automated trading.

Quick Start & Requirements

• Installation: pip install -U autohedge
• Prerequisites: Python 3.8+, swarms package, tickr-agent, and an OPENAI_API_KEY environment variable. A WORKSPACE_DIR is also required.
• Usage: Basic Python example provided in the README demonstrates initialization and running a trading cycle.
• API: An API server can be launched using uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4 after installing dependencies from requirements.txt. API documentation is available at /docs and /redoc.

Highlighted Details

• Multi-Agent Architecture: Specialized agents (Director, Quant, Risk, Execution) for distinct trading functions.
• Real-Time Market Analysis: Integrates with market data providers for live insights.
• Risk-First Approach: Built-in risk management and position sizing are core components.
• Structured Output: Generates trade recommendations and analysis in JSON format using Pydantic models.
• Extensible Framework: Designed for customization and extension with new capabilities.

Maintenance & Community

The project is developed by The Swarm Corporation and welcomes contributions via pull requests. A Discord community is available for support and discussion.

Licensing & Compatibility

The project is licensed under the MIT License, permitting broad use, including commercial applications.

Limitations & Caveats

Operation is dependent on an external OpenAI API key, introducing potential costs and usage limits. Production deployment requires setting up an ASGI server and managing environment variables. The "build in minutes" claim may be aspirational for fully operational hedge funds, as significant configuration and testing are implied for robust performance.