AI Hedge Fund

Detailed Introduction

AI Hedge Fund is a research and educational proof-of-concept that demonstrates how multiple specialized agents (valuation, sentiment, fundamentals, technicals, etc.) can collaborate to produce trading signals. The project provides a command-line interface and an optional web application for backtesting and strategy validation. It emphasizes reproducible research workflows and risk hypothesis testing; it is explicitly for learning purposes and not financial advice.

Main Features

• Agentic collaboration: multiple strategy agents evaluate assets in parallel to produce diverse trading signals.
• Backtesting & risk controls: configurable backtester and risk module for robustness checks on historical windows.
• Pluggable LLM integration: supports major LLM providers and local models (e.g., via the --ollama flag) for strategy reasoning and narrative explanations.
• Full-stack operation: runnable from CLI for automation or via the built-in web app for interactive analysis.

Use Cases

Suitable for researchers, quant hobbyists, and educational settings to explore agent collaboration, LLM-driven decision explanations, and backtesting pipelines. Typical uses include prototyping strategies, teaching, and studying model influence on trading decisions in controlled experiments. The project is not intended for live trading; run experiments in sandboxed historical environments.

Technical Characteristics

• Python implementation with Poetry for dependency management, enabling quick setup in development environments.
• Modular architecture: separates data ingestion, strategy logic, backtester, and presentation layers for easy substitution of data sources or models.
• Configurable data ingestion: supports free sample market data and third-party financial APIs, with API keys managed via .env.
• Local-first privacy: core computations and backtests run locally; network calls are optional to protect sensitive data.