FinRL vs Qlib
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source Python framework for training and testing reinforcement learning agents on stock trading tasks.
Open source · MIT
Open-source Python platform for quant investment research using machine learning.
The short answer
Choose FinRL if you need…
- Students learning how reinforcement learning is applied to financial markets
- Researchers prototyping and benchmarking DRL trading agents
- Developers who want a starting example of a train-test-backtest pipeline on DOW 30 stock data
- Readers comparing trained agents against baselines such as MVO and the DJIA
Choose Qlib if you need…
- 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
Side-by-side
| FinRL | Qlib | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 17k | 49.9k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 12 days ago | 7 days ago |
What each one is
FinRL
FinRL is a Python framework for applying deep reinforcement learning to financial trading. It provides market environments, learning agents, and a train-test-trade pipeline so users can simulate how an automated agent might make buy and sell decisions on historical stock data. The README describes it as an educational and research prototyping framework, and says a newer production-oriented project, FinRL-X, now exists for live trading.
Qlib
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.
Best for
FinRL
- Students learning how reinforcement learning is applied to financial markets
- Researchers prototyping and benchmarking DRL trading agents
- Developers who want a starting example of a train-test-backtest pipeline on DOW 30 stock data
- Readers comparing trained agents against baselines such as MVO and the DJIA
Qlib
- 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
Strengths
FinRL
- Open source under the MIT license, so it can be used and modified freely
- Includes a complete, runnable example from data download through backtesting
- Supports several widely used DRL algorithms through Stable Baselines 3
Qlib
- 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
Not ideal for
FinRL
- Anyone needing live trading or production deployment, because the README directs those users to FinRL-X / FinRL-Trading
- Users seeking financial advice or guaranteed returns, since the README presents this as a research and education tool and does not claim profitability
- Non-programmers, since setup and running scripts require Python and the command line
Qlib
- 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
Trade-offs
FinRL
- The README says this repository is for education and research, and the active production development has moved to FinRL-X
- Requires familiarity with Python, virtual environments, and reinforcement learning concepts to use effectively
- The README is truncated, so details on supported environments, versions, and data-source options beyond the example are unclear
Qlib
- 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
Getting started
FinRL
Clone the repository
git clone https://github.com/AI4Finance-Foundation/FinRL.gitEnter the folder
cd FinRLFrequently asked questions
What is the difference between FinRL and Qlib?
FinRL: Open-source Python framework for training and testing reinforcement learning agents on stock trading tasks. Qlib: Open-source Python platform for quant investment research using machine learning. FinRL suits students learning how reinforcement learning is applied to financial markets, while Qlib suits quant researchers who want a structured ml workflow for stock data.
Are FinRL and Qlib free?
FinRL: Free and open source. Qlib: Free and open source.
Which is easier to set up, FinRL or Qlib?
FinRL is easier to get started with (rated intermediate); Qlib is rated advanced and usually needs more setup.
Is FinRL or Qlib more popular?
Qlib has more GitHub stars (49.9k) than FinRL (17k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to FinRL and Qlib?
Other options people compare them with include Stable Baselines3.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.