AI Hedge Fund
AI-powered hedge fund simulation using multiple expert agents (Buffett, Ackman) to analyze stocks and make trading decisions. Combines fundamental, technical, sentiment analysis with risk management for educational purposes.
Project Description
The AI Hedge Fund is an educational project that demonstrates how artificial intelligence can be applied to investment decision-making. The system implements a multi-agent architecture where different AI agents, each specialized in specific investment strategies, work together to analyze stocks and generate trading signals.
Key Components:
- Investment Legend Agents (Bill Ackman & Warren Buffett) that apply their proven investment principles
- Specialized Analysis Agents for valuation, sentiment, fundamentals, and technical analysis
- Risk Management Agent to evaluate and limit position exposure
- Portfolio Management Agent to coordinate final trading decisions
The system processes real market data but operates in simulation mode only, making it perfect for learning about AI applications in finance without real monetary risk.
How it's Made
The project is built using a modern Python stack with several key architectural decisions:
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Agent Architecture: Each investment strategy is encapsulated in its own agent class, allowing for modular development and easy addition of new strategies. The agents communicate through a coordinated workflow managed by the Portfolio Manager.
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Data Integration: Uses Financial Datasets API for market data, with built-in support for major tech stocks (AAPL, GOOGL, MSFT, NVDA, TSLA).
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LLM Integration: Leverages multiple LLM providers (OpenAI, Groq, Anthropic) for sophisticated market analysis and decision-making.
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Development Tools:
- Poetry for dependency management and virtual environment handling
- Environment variable management for secure API key storage
- Comprehensive backtesting framework for strategy validation
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Project Structure: Organized into logical components:
- Separate agent modules for each strategy
- Dedicated tools directory for API interactions
- Backtesting system for performance analysis
The system is designed to be educational and extensible, allowing users to easily add new agents or modify existing strategies while maintaining a clear separation of concerns.
