FinRL vs Machine Learning for Trading
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
Code companion to a book on building, testing, and running ML-driven trading strategies.
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 Machine Learning for Trading if you need…
- Readers of the Machine Learning for Trading book who want to run its code
- Students learning applied machine learning on financial time series
- Quantitative researchers who want a worked example of an end-to-end strategy workflow
- Developers who are comfortable with Python and Jupyter notebooks
Side-by-side
| FinRL | Machine Learning for Trading | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 17k | 21.7k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Clone and build from source |
| Main language | Jupyter Notebook | Jupyter Notebook |
| Last code update | 12 days ago | 8 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.
Machine Learning for Trading
This repository holds the code for the third edition of the book Machine Learning for Trading by Stefan Jansen, with 27 chapters and nine case studies. It walks through one workflow: sourcing market data, researching strategy ideas, evaluating them with backtests, and deploying them to live markets. People use it to learn how machine learning is applied to financial data and to run the book's examples themselves.
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
Machine Learning for Trading
- Readers of the Machine Learning for Trading book who want to run its code
- Students learning applied machine learning on financial time series
- Quantitative researchers who want a worked example of an end-to-end strategy workflow
- Developers who are comfortable with Python and Jupyter notebooks
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
Machine Learning for Trading
- Covers the full path from data to live execution in one consistent workflow
- Includes nine case studies across ETFs, futures, FX, options, crypto, and equities
- Uses reproducible Docker environments for each chapter, per 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
Machine Learning for Trading
- People looking for a ready-made profitable trading strategy: the README presents code for learning and research, not a tested investment product
- Complete beginners to Python or finance, since the material assumes working knowledge of both
- Readers who want the second edition: the README says it lives on a separate branch
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
Machine Learning for Trading
- Focused on learning and research code; the README does not promise trading results
- Requires setup of data files and environments before the notebooks run, which may take effort
Getting started
FinRL
Clone the repository
git clone https://github.com/AI4Finance-Foundation/FinRL.gitEnter the folder
cd FinRLMachine Learning for Trading
Clone the repository
git clone https://github.com/stefan-jansen/machine-learning-for-tradingFrequently asked questions
What is the difference between FinRL and Machine Learning for Trading?
FinRL: Open-source Python framework for training and testing reinforcement learning agents on stock trading tasks. Machine Learning for Trading: Code companion to a book on building, testing, and running ML-driven trading strategies. FinRL suits students learning how reinforcement learning is applied to financial markets, while Machine Learning for Trading suits readers of the machine learning for trading book who want to run its code.
Are FinRL and Machine Learning for Trading free?
FinRL: Free and open source. Machine Learning for Trading: Free and open source.
Is FinRL or Machine Learning for Trading more popular?
Machine Learning for Trading has more GitHub stars (21.7k) 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 Machine Learning for Trading?
Other options people compare them with include Stable Baselines3, Qlib.
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.