Qlib vs FinRL
Qlib is for supervised machine learning: predict which stocks will do better, then build a portfolio. FinRL is for reinforcement learning: train an agent that decides trades directly. Qlib is closer to how quant equity teams work; FinRL is more of a research and teaching tool.
Side by side
| Qlib | FinRL | |
|---|---|---|
| Category | AI and machine learning | AI and machine learning |
| What it is | Microsoft's AI-oriented quant investment platform for building and testing machine-learning alpha models. | Deep reinforcement learning library for trading research and teaching. |
| Best for | Researchers building cross-sectional stock-selection models with machine learning. | Learning and experimenting with reinforcement learning on market data. |
| Language | Python | Jupyter Notebook |
| License | MIT | MIT |
| GitHub stars | 50.2k | 17.0k |
| Last commit | 2026-11-04 | 2026-11-03 |
| Latest release | v0.9.8 (2026-10-08) | v0.3.8 (2026-03-20) |
| Status | Active | Active |
GitHub statistics as of 2026-11-10. Status: active = commits in the last 90 days, slowing = in the last year, inactive = older, archived = read-only.
Strengths · Limitations
Qlib
Strengths
- End-to-end ML research workflow
- Many reference models and benchmarks
- Backed by Microsoft Research, MIT license
Limitations
- Equity factor research, not trading execution
- Default data focuses on China A-shares
- Needs solid ML background
FinRL
Strengths
- Many ready-made environments and tutorials
- Covers several RL algorithms
- Active academic community
Limitations
- Research code, not a production trading system
- RL results are easy to overfit; out-of-sample checks are essential
- Mostly Jupyter notebooks
Which one to choose
Qlib is for supervised machine learning: predict which stocks will do better, then build a portfolio. FinRL is for reinforcement learning: train an agent that decides trades directly. Qlib is closer to how quant equity teams work; FinRL is more of a research and teaching tool.
All frameworks · Descriptions are our own summary and may lag behind each project's documentation. Check the project's license before using it in a product. Nothing here is investment advice.