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An AI Hedge Fund Team
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- 11.2k
- Last pushed
- Updated Oct 2026
Repo stats from the GitHub API, cached Oct 2026.
CommunityJev Integrations & Adapters
AI Hedge Fund
Proof-of-concept multi-agent AI hedge fund that trades on paper: LLM investor agents analyse stocks, and a native JevLLM adapter lets an agent run on Jev's typed decisions through the same JSON contract the chat providers use. Educational only; no real trades.
- Stars
- 63.9k
- Language
- Python
- License
- MIT
- Published by
- Community
- Last verified
- Oct 2026
About this project
This is a proof of concept for an AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions. This project is for educational purposes only and is not intended for real trading or investment. Note: the system does not actually make any trades. This project is for educational and research purposes only. Not intended for real trading or investment. No investment advice or guarantees provided. Creator assumes no liability for financial losses.
Quoted from the project's README. Read the full README.
Works forJev for Decision gating
Used inJev for Finance & Fintech
Highlights
- JevLLM calls https://api.typesafe.ai/v1/systemone with model jev-1.13.0 by default.
- Retries on 429 and 5xx (500, 502, 503, 504, 529); credentials are redacted from saved bodies.
- Cache keys hash the semantic request, leaving legacy prompt keys undisturbed.
- 63,926 stars on GitHub; TypeSafe (Jev) sits beside Anthropic, OpenAI, DeepSeek, Google, xAI and Kimi.
- pipx install aihf launches paper trading with a hash-chained session ledger.
Watch out
MIT licensed and explicitly educational: the README states the system makes no trades and gives no investment advice. Running the agents needs a Financial Datasets API key plus one model provider key, with TypeSafe as one option among several.
Questions about this entry
Short answers drawn from the linked project or page.
It ships a JevLLM adapter that calls TypeSafe's /v1/systemone endpoint and adapts Jev's native typed answers into the same JSON contract the chat-provider agents already consume, so an investor agent can run on Jev without free-text parsing fragility. TypeSafe sits beside Anthropic, OpenAI, DeepSeek, Google, xAI and Kimi as a selectable provider.
No. It is a proof of concept for education and research: the README says the system does not actually make trades. The interactive app offers paper trading with fake money over real market days and a backtesting mode, with every session appended to a hash-chained ledger so NAV is a track record.
A Financial Datasets API key for prices, fundamentals and earnings, plus one model provider key for the investor agents (Anthropic, OpenAI, DeepSeek, Google, xAI, Kimi or TypeSafe/Jev). The app asks for them on first use and saves them to ~/.hedge-fund/.env, and shell-exported keys always win.
Install with pipx install aihf, or uv tool install aihf, or pip install aihf into an environment of your choice, then run aihf with no arguments to launch the interactive terminal app. Nothing needs configuring up front.
Paper trading gives each fund a ledger of real market days and fake money, appending every session to a hash-chained ledger so NAV is a track record. Backtesting replays a fund over history and draws its equity curve against a benchmark, saving results to ~/.hedge-fund/research/.
The JevLLM adapter posts to https://api.typesafe.ai/v1/systemone with model jev-1.13.0 by default. It retries on 429 and 5xx statuses (500, 502, 503, 504, 529), and credentials are redacted from saved response bodies.
It is MIT licensed. Clone the repository, run poetry install, then poetry run aihf and poetry run pytest hedge_fund. The README asks contributors to keep pull requests small and focused and to tag feature requests as enhancements.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
X: $10,000 in Jev’s hands
YouTube: Nate Herk compares speed and cost against ordinary models, then builds an X feed classifier and a paper-trading prototype.
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