87Score
Machine Learning for Tradingstefan-jansen/machine-learning-for-tradingThe companion code to Stefan Jansen's book. Hundreds of notebooks from features to live strategies.
★ 22k stars · Intermediate · updated 2026-10
Quant and ML deep dive
Qlib Review | Microsoft's Free Quant Lab, and the Leak Nobody Mentions
Qlibmicrosoft/qlibQlib is 
Microsoft Research's free, open-source platform for building and testing machine learning stock models, from raw data to backtest in one pipeline. It suits quant-curious Python users and researchers who want a serious lab without building one. The catch: its official dataset is currently disabled, the built-in data comes from 
Yahoo Finance, and one 
Reddit quant says he spotted future data leaking into model code.
Think of it as a fully stocked university chemistry lab: every instrument is there, but nobody tells you what to mix, and a dirty beaker can ruin the whole experiment.
Read the transcript
Microsoft built a quant research lab and gave it away. Nearly fifty thousand people starred it. Here's why most of them should handle it with gloves.
Qlib is a free platform for building machine learning stock models, from raw data to a tested portfolio. Think of a fully stocked chemistry lab. Every instrument is there. Nobody tells you what to mix.
It's popular because it's complete, and Microsoft Research is behind it.
Five things we like. One, one pipeline from data to backtest. Two, a big library of ready-made models. Three, ready-made signals, called factors, to start from. Four, it pairs with Microsoft's RD-Agent for AI-assisted research. Five, a friendly MIT license.
It works with Python, Docker and Yahoo Finance data, and you hand signals to a broker yourself.
Reviews are mixed. People respect the pipeline. One Reddit quant says he spotted future data leaking into model code. That's one claim, but worth checking.
The real lesson Qlib teaches is process: data, signal, test, then repeat until the idea survives.
Two cautions. The official dataset is currently disabled, so your data is on you. And there's no execution layer. It researches. It doesn't trade.
Best for quant-curious Python users who know some machine learning. Scroll down for setup steps and simpler alternatives.
This is education, not financial advice. Your market edge starts with the right tool. Stay alpha.
FullStack Alpha Editorial Team
Reviewed by Jay Rocco, Chief Editor
What Is Qlib and What Does It Actually Do?
This is a repo (a project's folder of code on 
GitHub) from Microsoft Research with nearly 50,000 stars. It is a research platform, not a trading bot. It covers the full machine learning pipeline: loading price data, turning it into signals called factors, training models, and running a backtest (replaying a strategy on old prices to see what would have happened).
The model shelf is deep. It ships ready-made versions of LightGBM, XGBoost, CatBoost, LSTM, GRU, Transformer and more than a dozen research models, plus factor sets called Alpha158 and Alpha360. One command, qrun, runs a whole experiment from a config file: build the dataset, train, backtest and score. It also includes reinforcement learning for order execution and a sister project, RD-Agent, that uses large language models to mine new factors automatically.
What it does not do is trade. Think of the output as a ranked list of stocks with predicted returns, not a set of orders. There is no broker connection in the README, and the data story is shaky right now. The README says the official dataset is temporarily disabled and points to a community source instead, and warns that the Yahoo Finance data it collects might not be perfect. Microsoft is telling you to bring your own clean data, and you should listen. A pipeline this powerful amplifies whatever you feed it, good or bad.
Community Score
90 / 100
- Adoption · How many people starred it
- 28.2 / 30
- Maintenance · How recently the code was updated
- 25 / 25
- Community sentiment · What traders say on Reddit and Hacker News
- 13 / 20
- Builder activity · How many people built their own copy
- 13.6 / 15
- License clarity · Whether you are clearly allowed to use it
- 10 / 10
GitHub data checked 2026. Community read: mixed.
- Stars
- 49,791
- Forks (people who built on it)
- 7,863
- Written in
- Python
- License
- MIT
- Last update
- 2026-10-22
- Markets
- Stocks
- Skill level
- Advanced
Works with
Any AI, through your own code
Is Qlib Right for You?
Good fit if you are
- Python users who want to learn how quant funds build and test stock prediction models
- Students and researchers comparing machine learning models on the same benchmark
- Builders experimenting with AI agents that generate trading factors through RD-Agent
- Data scientists who already have clean market data and need a pipeline around it
Skip it if you are
- You want a bot that places trades. This is a research lab with no broker connection.
- You do not write Python or have no plan for reliable data. Garbage data in this pipeline produces confident garbage out.
The jargon, in plain English
- Repo
- A project's folder of code on GitHub that anyone can download.
- Backtest
- Replaying a strategy on old prices to see what would have happened.
- Factor
- A measurable signal, like momentum or valuation, that a model uses to rank stocks.
- Data leak (look-ahead bias)
- When a model accidentally sees future information during testing, making results look far better than reality.
- LightGBM
- A fast, popular machine learning model that builds many small decision trees.
- Reinforcement learning
- A way of training software by trial, error and reward, like teaching a dog tricks with treats.
- RD-Agent
- A separate Microsoft project that uses AI language models to propose and test new factors automatically.
What Do Traders Say About Qlib?
Community sentiment is mixed, and there is less of it than the star count suggests. Supporters on r/quant and r/ai_trading see it as a solid foundation for a research pipeline, especially paired with RD-Agent and locally run language models for automated factor research.
Skeptics push hard. Some see the performance claims in its research as outlandish, and one r/quant commenter says he could see data leaks in the model code, meaning future information may slip into tests. That is the cardinal sin of quant research, because it makes a mediocre model look brilliant.
“Also I can see data leaks in the models code.”
Epsilon_ride, r/quant, 2025What they like
- Solid end-to-end research pipeline
- Huge library of ready-made models
- Pairs well with RD-Agent and local AI models
- Backed by Microsoft Research, MIT license
What they complain about
- A commenter reports future data leaks in model code
- Research claims seen as outlandish
- Official dataset currently disabled
- Little active discussion for its size
How Do You Set Up Qlib Safely?
Plain steps, fake money first. Keep your keys in a private settings file, never pasted into the code.
- 01Set up Python with CondaThe README lists Python 3.8 to 3.12 and suggests Conda for a clean environment. On Apple Silicon Macs, LightGBM may need libomp installed through
Homebrew. - 02Install the packageRun pip install pyqlib for the stable release, or install from source to get the newest code. A prebuilt
Docker image is also available. - 03Get data you can trustThe official dataset is temporarily disabled, so the README points to a community dataset or the Yahoo collector. Check the data with the included health check script before training anything.
- 04Run the sample workflowUse qrun with the LightGBM Alpha158 example config to see a full experiment end to end. Read every assumption in that config before trusting the score.
- 05Audit for leaksConfirm no model or factor uses information that was not available at the time. Compare results on a period the model never saw during training.
- 06Paper trade elsewhereThere is no broker link, so test any signal it produces with paper trading (fake money, real prices) on a separate platform before risking capital. Keep broker API keys in an environment file, never in notebooks.
What Do You Need to Run Qlib?
Clean historical stock data
The built-in Yahoo Finance data may be imperfect and the official dataset is disabled, so serious work needs your own data.
A Python environment
Everything runs in Python 3.8 to 3.12; Conda or Docker keeps dependencies sane.
DockerA decent computer
Training neural network models on years of stock data takes memory and time. Some workflow scripts run only on Linux for now.
A separate broker for execution (optional)
The platform stops at research. Turning a signal into trades means another tool and a broker account.
Optional AI model access for RD-Agent
RD-Agent uses large language models to propose factors; you supply the model, local or hosted.
What Are the Risks of Using Qlib?
Look-ahead data leaks
A community member reports seeing data leaks in model code. If future data slips in, backtests lie. Audit every feature date before believing a result.
Unreliable default data
The README itself warns Yahoo-sourced data might not be perfect, and the official dataset is disabled for now. Bad data quietly poisons every model downstream.
Research results that do not survive
Academic benchmark scores often ignore costs, slippage and capacity. A model that wins on paper can lose real money once trading costs show up. Add realistic fees and slippage to every test.
Maintenance friction
The README flags breaking changes around loading saved models and pandas 2.0 issues in some examples. Expect to fix things yourself.
No execution layer
Nothing here places trades. Every handoff to a live system is a new place for bugs.
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What Are the Best Qlib Alternatives?
- 87ScoreMachine Learning for Tradingstefan-jansen/machine-learning-for-trading
78Score
FinRLAI4Finance-Foundation/FinRLDeep reinforcement learning for trading, with ready-made market environments.
★ 17k stars · Advanced · updated 2026-10
60Score
TensorTradetensortrade-org/tensortradeA reinforcement learning framework for building and training trading agents.
★ 7.3k stars · Advanced · updated 2026-02
Should You Use Qlib? Our Verdict
Microsoft Qlib earns its Community Score of 90 on adoption, maintenance and a clean MIT license, but the sentiment score is mixed for good reason. As a free classroom for how quants build stock models, it is hard to beat. You get dozens of models, standard factor sets and a one-command experiment runner.
As a source of money-making signals, treat it with suspicion. The data is your problem, leak claims deserve a careful audit, and nothing here touches a broker. Pick Qlib if you want to learn and research. Skip it if you want a system that trades on Monday.
More Qlib Questions, Answered
What is Microsoft Qlib?
Qlib is an open-source, AI-oriented quant investment platform from Microsoft Research. It handles data processing, model training and backtesting for stock prediction research in one Python pipeline.
Is Qlib free?
Yes. It is free under the MIT license and installs with pip install pyqlib. Your costs are computing power and any paid data you choose to use.
Can Qlib trade stocks automatically?
No. The README describes research, training and backtesting, with no broker connection. You would need a separate execution tool and broker to act on its signals.
Where does Qlib get its data?
Built-in data is collected from Yahoo Finance and the README warns it might not be perfect. The official dataset is temporarily disabled, with a community dataset suggested in the meantime.
Does Qlib use AI or LLMs?
The core is built around machine learning models like LightGBM, LSTM and Transformers. Its sister project RD-Agent adds large language model agents that propose and test new factors automatically.
Sources we checked
- https://github.com/microsoft/qlib
- https://www.reddit.com/r/quant/comments/1lbqr7b/comment/mxv8ria/
- https://www.reddit.com/r/quant/comments/1lbqr7b/anybody_use_qlib/
- https://www.reddit.com/r/quant/comments/1x5rkjn/anyone_working_with_rdagent_in_qlib/
- https://www.reddit.com/r/ai_trading/comments/1xb1tbc/how_many_of_you_guys_have_used_microsofts_qlib_or/