Skip to content
Get packs

Qlib

Open-source Python platform for quant investment research using machine learning.

GitHub Download v0.9.8
Stars
49.9k
Forks
7.9k
Licence
MIT
Language
Python
Last update
7 days ago
Type
Library

What it is

Qlib is a Python platform for quantitative investment research, where analysts build models that try to predict market behavior from historical data. It covers the workflow from exploring ideas to running models, and supports supervised learning, market dynamics modeling, and reinforcement learning. The README also says it now connects to RD-Agent, a separate Microsoft project for automating factor mining and model optimization.

  • Load and prepare historical market data for model training
  • Train and compare supervised learning models on stock datasets
  • Run reinforcement learning experiments for trading decisions
  • Use the notebook tutorials in examples/tutorial to learn the workflow

Great for

  • Quant researchers who want a structured ML workflow for stock data
  • Python developers experimenting with financial machine learning models
  • Students learning how quantitative investment research is organized
  • Teams that want to reuse published quant models such as those listed in the README

Not ideal for

  • People looking for a ready-made trading bot, since the README describes a research platform rather than a turnkey strategy
  • Anyone who needs guaranteed returns or investment advice; the project does not provide this
  • Users who are not comfortable with Python and command-line setup
Step-by-step guide: how to use QlibInstall, first run, examples, tips and FAQs
AI Hedge Fund vs QlibQlib vs VeighNaQlib vs Vibe-TradingMachine Learning for Trading vs QlibQbot vs QlibFinRL vs Qlib

How to get it

  1. 1

    Install with a package manager

    You'll need: Python (version range shown on PyPI; check the README for the exact supported versions)

    Install from PyPI

    pip install pyqlib
  2. 2

    Run it

    • Install the package with pip install pyqlib
    • Open the notebook tutorials in examples/tutorial in the repository
    • Follow the tutorial to prepare a dataset and run a first model workflow
    • Read the documentation at qlib.readthedocs.io before changing configurations
  3. 3

    Get the most out of it

    • Read the artifact loading migration guide before reloading saved models or datasets
    • Use RD-Agent to automate factor mining and model optimization, as described in the README
    • Explore the model implementations listed in the README, such as HIST, TRA, and DoubleEnsemble
    • Use the configuration migration guide if you run configuration-driven workflows

Downloads

Source code (.zip)main branch · straight from GitHubRelease v0.9.8Release notes and sourceOfficial website & docsqlib.readthedocs.io/en/latest/

Strengths

  • Open source under the MIT license with a large community (about 49,800 stars at time of analysis)
  • Covers multiple modeling approaches in one framework
  • Includes documentation, tutorials, and an active CI test badge in the README

Trade-offs

  • The README warns of an unreleased breaking change to how saved models and datasets are loaded, which requires reading a migration guide before upgrading
  • Recent source builds restrict loading artifacts unless users opt in with trusted=True, so careful review is needed
  • Requires a working understanding of quant finance and machine learning to use meaningfully

How it compares

ProjectStarsTypeLevelUpdatedBest for
qlib (this)49.9kLibraryadvanced7 days agoQuant researchers who want a structured ML workflow for stock data
FinRL17kLibraryintermediate12 days agoStudents learning how reinforcement learning is applied to financial markets
daily_stock_analysis67.4kAppintermediate4 days agoIndividual investors who follow a personal watchlist of A-shares, HK, US, or other listed stocks and want a daily summary
machine-learning-for-trading21.7kLearning resourceintermediate8 days agoReaders of the Machine Learning for Trading book who want to run its code
vnpy46.2kFrameworkadvanced10 days agoPython developers who want to build custom quant trading systems
Qbot18.8kAppadvanced8 months agoPython users who want to research quantitative trading strategies

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.