AutoHedge: Build Your Autonomous AI Hedge Fund with Swarm Intelligence
This repository profile is provided by osrepos.com, an open source repository discovery platform.

Summary
AutoHedge is an enterprise-grade autonomous agent hedge fund that leverages swarm intelligence and specialized AI agents. This powerful Python project automates end-to-end market analysis, risk management, and trade execution. It allows users to build and deploy their own AI-driven trading strategies with minimal human intervention.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
AutoHedge is a cutting-edge open-source project designed to help you build your autonomous hedge fund in minutes. It harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution. This enterprise-grade system is built for institutional reliability, offering structured outputs, comprehensive logging, and a risk-first architecture.
Currently, AutoHedge supports full autonomous trading on Solana, with planned expansion to Coinbase and other exchanges. Its multi-agent architecture includes specialized agents for strategy generation, technical analysis, risk assessment, and order execution, ensuring a robust and efficient trading pipeline.
Installation
To get started with AutoHedge, you can install it via pip:
pip install -U autohedge
Environment Variables
AutoHedge requires several environment variables for its operation. Create a .env file in your project root and configure the following:
# Jupiter API (token price & search tools)
# Get a key at https://portal.jup.ag
JUPITER_API_KEY=
# OpenAI (experimental agents)
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
WORKSPACE_DIR="agent_workspace"
# Trading
WALLET_PRIVATE_KEY=""
Refer to the .env.example file in the repository for a full reference of all configurable variables.
Examples
Once installed and configured, you can initiate the AutoHedge system with a simple command:
autohedge
This command starts the autonomous trading operations, allowing the specialized AI agents to begin their market analysis, strategy generation, and trade execution processes.
Why Use AutoHedge
AutoHedge offers a compelling solution for anyone looking to automate and enhance their trading strategies. Here are some key reasons to consider it:
- Advanced AI Automation: Leverage state-of-the-art AI and swarm intelligence for sophisticated market analysis and decision-making.
- End-to-End Trading: Automates the entire trading pipeline, from strategy generation and risk management to trade execution, minimizing manual intervention.
- Institutional Reliability: Designed with a risk-first architecture, structured outputs, and detailed logging, making it suitable for robust, enterprise-grade deployments.
- Modular and Extensible: Its multi-agent architecture allows for easy customization of strategies and integration with new trading venues.
- Focus on Risk Management: Built-in risk management and position sizing ensure a disciplined approach to trading, prioritizing capital preservation.
Links
- GitHub Repository: https://github.com/The-Swarm-Corporation/AutoHedge
- Join our Discord: https://discord.gg/VapjxpSyHC3
- Subscribe on YouTube: https://www.youtube.com/@kyegomez3242
- Connect on LinkedIn: https://www.linkedin.com/in/kye-g-38759a207/
- Follow on X.com: https://x.com/swarms_corp
Related repositories
Similar repositories that may be relevant next.
AutoResearch: AI/ML Research Agents from Idea to Paper-Ready Evidence
September 22, 2026
AutoResearch is an open-source agent workflow designed for AI and machine learning research. It automates the entire research process, from generating ideas and planning experiments to execution, analysis, and independent evaluation. This project helps researchers produce paper-ready evidence efficiently and with traceable provenance.
HarnessRouter: Unified Interface for AI Agent Harnesses
September 22, 2026
HarnessRouter Community Edition provides a self-hosted, Apache-2.0 licensed unified interface for various AI agent harnesses like Codex, Claude Code, and Hermes. It allows users to run multiple agents through a single API, offering features such as sessions, streaming, file handling, and cancellation. The project implements the open-standard Unified Harness Protocol (UHP), ensuring users maintain control over their keys and infrastructure.

AREX-Skill: A Skill Library for Automated Machine Learning and Auto-Research
September 21, 2026
AREX-Skill is a powerful skill library designed to advance automated machine learning and auto-research. It distills over 5,000 executable skills from more than 1,000 popular GitHub repositories, making complex ML knowledge directly usable by coding agents. This project significantly enhances agent performance in various research tasks by providing structured, validated operating knowledge.

oh-my-hermes: Enhance Hermes Agent with Advanced AI Workflow and Memory
September 17, 2026
oh-my-hermes is an all-in-one plugin designed to significantly enhance the Hermes Agent. It provides advanced coding intelligence, a robust long-term memory system, and optimized workflow packages, transforming standard Hermes requests into structured, actionable tasks with clear operational layers.
Source repository
Open the original repository on GitHub.
26 counted GitHub visits