qlib: Accelerate Quant Research with AI‑Powered Modeling
From the repo · “Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.”
Qlib provides a platform for founders in finance and fintech to conduct AI-powered quant research and backtest trading strategies
- •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
python scripts/backtest.py -c scripts/config_backend.yaml -m LSTM -d btc
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Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/qlib && curl -fsSL https://workflowstacks.com/api/skills/qlib/claude-skill -o ~/.claude/skills/qlib/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Qlib: Accelerate Quant Research with AI‑Powered Modeling 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.
- 1README.mdStart here — what it does and how to install it
- 2pyproject.tomlDependencies and the commands it exposes
- 3qlib/__init__.pyInside qlib/ — the main logic begins here
- 4examples/README.mdA worked example — copy this to get going
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/qlib && curl -fsSL https://workflowstacks.com/api/skills/qlib/claude-skill -o ~/.claude/skills/qlib/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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