workflowstacks
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Qlib: AI for Quant Research

Get AI-powered quant research with Qlib, a platform for founders in finance and fintech.
47,011 stars7,479 forksPythonGuide quality 8/10Updated 7/23/2026100% free · open source
What it does

Qlib provides a platform for founders in finance and fintech to conduct AI-powered quant research and backtest trading strategies

When to use it
  • •When you need to backtest a trading strategy before deploying it in a live market
  • •When you want to analyze and compare the performance of different trading algorithms
  • •When you need to integrate machine learning models into your quantitative trading workflow
Ready-to-paste prompt
python scripts/backtest.py -c scripts/config_backend.yaml -m LSTM -d btc
Heads up: Qlib requires a specific version of Python (3.7 or later) and may not work correctly with earlier versions, so ensure you have the correct version installed before trying to use it
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/qlib && curl -fsSL https://workflowstacks.com/api/skills/qlib/claude-skill -o ~/.claude/skills/qlib/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Qlib: AI for Quant Research works
Codeflow
Free to inspect

Qlib: AI for Quant Research is a very large Python project (~67k lines across 298 code files, plus 40 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit a month ago, MIT license, has a test suite.

Size
Very large codebase
~67k lines · 298 code files · days to read — use, don't read
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Python · Docker
No API keys detected
Python 99%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    pyproject.toml
    Dependencies and the commands it exposes
  3. 3
    qlib/__init__.py
    Inside qlib/ — the main logic begins here
  4. 4
    examples/README.md
    A worked example — copy this to get going
What's in each folder
qlib/Core code — the actual logic234 files
examples/Examples you can copy192 files
docs/Documentation77 files
scripts/Helper scripts43 files
tests/Tests — proof it works40 files
.github/CI / automation (GitHub Actions)13 files
READMEHas testsDocumentedCI checksDocker readyExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
microsoft
Language
Python
Category
ai-agent
Published
8/14/2020

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