Qlib — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Qlib. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 22, 2026 • Sentiment updated: Jun 21, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As mentioned on GitHub, 'Qlib is an excellent project that provides a path for retail investors to engage in quantitative trading.' Additionally, a user on Dev.to stated, 'Gemma 4 12B is designed to bring high-performance multimodal intelligence directly to your laptop,' showing the community's interest in AI and quant trading.

Pros & Cons

What People Love

Qlib's ease of use, Gemma 4 12B's performance, Dev.to community support

Common Complaints

Funding limitations, Technical issues with Qlib

Biggest Positive: Qlib excellent project

Biggest Negative: Funding limitations

Why Qlib Stands Out

Qlib is valuable because it provides a comprehensive platform for quantitative finance and algorithmic trading, leveraging AI technology to empower Quant research. Its RD-Agent feature automates factor mining and model optimization, making it a unique solution in the market. By supporting diverse ML modeling paradigms, Qlib enables users to create customized models for their specific use cases. Additionally, its integration with other Microsoft tools, such as Azure, makes it a powerful tool for large-scale deployments.

Built With

Build a quantitative trading platform — Qlib provides a wide range of tools and datasets for research and development in quantitative finance, Build a research agent that reads financial reports and predicts stock prices — Qlib's RD-Agent supports automated factor mining and model optimization, Build a portfolio optimization system — Qlib's planning-based portfolio optimization feature enables users to create customized portfolios, Build a market dynamics modeling system — Qlib supports diverse ML modeling paradigms, including supervised learning and reinforcement learning, Build a risk management system — Qlib's Point-in-Time database and Arctic Provider Backend enable users to track and analyze market data

Getting Started

  1. Install Qlib using pip: `pip install pyqlib`
  2. Configure Qlib by setting up the Point-in-Time database and Arctic Provider Backend
  3. Run the Qlib notebook tutorial to get familiar with the platform: `jupyter notebook examples/tutorial`
  4. Use the RD-Agent feature to automate factor mining and model optimization: `python -m qllib.rdagents`
  5. Try building a simple quantitative trading model using Qlib's supervised learning features to verify it works

About

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.

Official site: https://qlib.readthedocs.io/en/latest/

Category & Tags

Category: trading

Tags: algorithmic-trading, auto-quant, deep-learning, finance, fintech, investment, machine-learning, paper, platform, python, quant, quant-dataset, quant-models, quantitative-finance, quantitative-trading, research, research-paper, stock-data

Market Context

Competing with other quant trading platforms