Ross ROSS = Recommend OSS · open-source software intelligence for agents

Qlib

Some data may be outdated — see provenance labels.

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. observed · 2026-09-21

github.com/microsoft/qlib · homepage · Python · MIT (permissive) observed · 2026-09-21

Health v2 · maintenance only

66/100

  • Activity 95
  • Release rhythm 8
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: 235
  • age_days: 2258
  • days_rel: 431
  • days_push: 33
  • n_releases_24m: 2

Full methodology

Adoption not part of the score

48694 stars · 7709 forks observed · 2026-09-21

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-28, confidence 0.97

Qlib is an AI‑oriented quantitative investment platform written in Python. It provides end‑to‑end tools for data preparation, model training, factor mining, and backtesting of trading strategies.

Use cases

  • backtest trading strategies on historical market data
  • train machine learning models to predict stock prices
  • automate factor discovery and selection for quantitative research
  • run reinforcement‑learning agents for portfolio optimization
  • manage experiment tracking and model versioning in quant workflows
  • integrate custom forecasting models into a unified pipeline

When to choose

  • You need a comprehensive, Python‑native framework that covers data ingestion, model development, and backtesting in one place.
  • Your quant research relies on AI techniques such as supervised learning, RL, or large‑scale factor mining.
  • You want built‑in support for daily market data handling, caching, and experiment management.

When to avoid

  • You only require a lightweight backtesting library without AI/ML components.
  • Your stack is not Python‑based or you need native support for other languages.
  • You need a production‑grade trading execution system rather than a research platform.

Facets

library · maturity active

machine-learning data-science etl analytics artificial-intelligence data-science python windows quant algorithmic-trading rl factor-mining stock-data finance linux macos

2 sources

Member repositories

RepositoryRoleHealth v2
microsoft/qlibmain66
microsoft/qlib-serverbackend45

For agents

markdown · JSON · MCP: product_card(name="microsoft/qlib")

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem