Welcome to Machine Learning Financial Laboratory

What was only possible with the help of huge R&D teams is now at your disposal, anywhere, anytime.

MlFinLab python library is a perfect toolbox that every financial machine learning researcher needs. It covers every step of the ML strategy creation starting from data structures generation and finishing with backtest statistics.

Hudson & Thames documentation has three core advantages in helping you learn the new techniques: Thoroughness, Flexibility and Credibility.


We want to make the learning process for the advanced tools and approaches effortless for our clients by providing detailed explanations, examples of use and additional context behind them.

The general documentation structure looks the following way:

Documentation page :

Lecture video*

Mathematical concept explanation

Implementation description

Short code example

Link to an extensive example notebook

Presentation slides*

* - optional content


Learn in the way that is most suitable for you as more and more pages are now supplemented with both video lectures and presentation slides on the topic.


All of our implementations are from the most elite and peer-reviewed journals.

Including publications from:

  1. The Journal of Financial Data Science

  2. The Journal of Portfolio Management

  3. The Journal of Algorithmic Finance

  4. Cambridge University Press

Who is Hudson & Thames?

Hudson and Thames Quantitative Research is a company with the goal of bridging the gap between the advanced research developed in quantitative finance and its practical application. We have created three premium python libraries so you can effortlessly access the latest techniques and focus on what matters most: creating your own winning strategy.


With the purchase of the library, our clients get access to the Hudson & Thames Slack community, where our engineers and other quants are always ready to answer your questions.

Alternatively, you can email us at:


This project is licensed under an all rights reserved license and is NOT open-source, and may not be used for any purposes without a commercial license which may be purchased from Hudson and Thames Quantitative Research.