Billy1900

Billy1900 / Midas

Public

Compound Engineering Framework for Alpha Feature Research in Quant Finance

billy1900.github.ioMidas agent-skills agentic-workflow quant quant-finance skills
20
2
100% credibility
Found Apr 05, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

Midas is a framework that uses AI to automatically discover, test, deploy, and monitor predictive trading signals for cryptocurrency perpetual futures.

How It Works

1
🔍 Discover Midas

You find this clever tool on GitHub that helps uncover hidden patterns in crypto prices to predict future moves.

2
⚙️ Try the Free Demo

Run a quick test with sample data to see example trading ideas and reports pop out instantly, no setup needed.

3
🤖 Connect a Smart Helper

Link an AI service like the one from OpenAI so it can think like a trading expert and suggest ideas.

4
📈 Feed in Your Market Data

Share your historical price, volume, and trend info so it studies real crypto behavior.

5
💡 Uncover New Trading Gems

It brainstorms, tests, and picks winning signals that spot profitable price swings ahead of time.

6
🚀 Launch and Watch Live

Put the best signals into action and let it monitor them around the clock for any issues.

📊 Smarter Trading Forever

Enjoy daily reports, automatic fixes, and a growing collection of insights that make your trades better every day.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 20 to 20 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is Midas?

Midas is a Python framework for automating alpha signal discovery and live monitoring in crypto perpetual futures trading. It runs an offline loop that uses LLMs like Claude or GPT to propose and evaluate new predictive features via a domain-specific language, then an online loop to track deployed signals, spot decay, and generate kill signals. Users get a persistent knowledge base of learnings from every run, plus CLI tools like `midas demo` for quick tests and `midas promote` for pipeline management.

Why is it gaining traction?

This compound engineering GitHub project stands out by turning AI into a self-improving quant researcher—each failure or success feeds back into prompts, making iterations cheaper over time. Unlike static backtesters, it handles regime shifts and trading costs out of the box, with OpenAI/Anthropic support and a no-key demo. Devs dig the editable prompt templates and multi-agent evaluation for robust signals.

Who should use this?

Quant engineers at crypto funds building alpha pipelines for perps, especially those tired of manual DSL tweaking or signal babysitting. Ideal for teams experimenting with compound engineering AI on GitHub, integrating with custom feature engines for hourly bars, or monitoring live IC decay in high-vol regimes.

Verdict

Early alpha (19 stars, 1.0% credibility) but battle-ready with full docs, CLI, tests, and synthetic demo—install and run in minutes. Worth a spin for compound engineering Claude workflows if you're in quant finance; skip if you need production-scale deployment yet.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.