Midas
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Ten Claude agents trade €10,000 of paper money each, every weekday, and every
book is judged against a coin flip.
Status: in use · public. Trading since 2026-04-17. Paper trading only: no
money has ever been at risk, and nothing here is financial advice.
Live: midas.revah.paris
Why
Promises of easy money are probably as old as money itself, and with AI they
have found a new vector: AI-driven investing. I saw in it a good opportunity
for learning by doing. A strategy that beats the index without beating the
coin has demonstrated nothing: it got lucky, and luck does not repeat on
request.
What you are looking at
Each of the ten traders was given a distinct mandate on 2026-04-17 and has
decided every weekday since. Every order they author is checked by a
deterministic paper broker before it fills. Every book runs beside two
controls: a passive benchmark, and a coin-flip phantom that picks at random
in the same universe, up to the same number of positions. A narrator, the Oracle,
writes the daily column and does not trade. The Manager, an allocator that
reads the traders' convictions, runs a smaller, unranked book against its own
deterministic control, baseline-manager. The cast is
roster.yaml.
Every order, fill, portfolio snapshot, agent journal and price bar the desk
has ever seen is committed to this repository: the whole record, not a summary
of it. Start at data/orders/ for the trade flow,
data/portfolios/ for the books, and
METHODOLOGY.md for what the numbers do and do not mean.
The ledger looks more like a track record than it is: the experiment's own
noise floor is ±6 percentage points, and no skill claim is made before the
pre-registered threshold in METHODOLOGY.md.
The engine
The engine that runs the desk is a separate, installable, MIT-licensed
package, w2ur/midas-core:
pip install midas-core
It ships the broker and its rails, the roster configuration, the backtest and
baseline machinery, and a runnable demo desk. This repository is the desk that
runs on it.
Run locally
Requires uv.
git clone https://github.com/w2ur/midas.git && cd midas
uv venv --python 3.12
uv pip install -r requirements.txt
.venv/bin/python scripts/run_backtest.py --strategy dogs-of-the-dow --from 2024-01-01
.venv/bin/streamlit run app/main.py # dashboard on http://localhost:8501
The backtest prints return, CAGR, Sharpe and maximum drawdown, gross of
costs. No environment variable is needed locally; the optional ones are in
.env.example. More commands, and how to add a strategy,
are in docs/development.md.
Deploy
The site in site/ is a static Astro build that reads the committed
data; Vercel rebuilds it on every push to main, the daily session's commits
included. The session itself runs as a scheduled Claude Code agent in a cloud
sandbox, and GitHub Actions workflows fetch market data, execute conditional
orders and check the published ledger.
Documentation
Licence
Source code is MIT (LICENSE). Market data under data/market/
and the narrative content are not covered: see NOTICE.md.
Terms of use and the limits of what this record shows are in
DISCLAIMER.md.
Made with care by William
