Machine Learning Financial Laboratory (mlfinlab)

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MlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. Adding MlFinLab to your companies pipeline is like adding a department of PhD researchers to your team.

pip install mlfinlab

We source all of our implementations 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

We are making a big drive to include techniques from various authors, however the most dominant author would be Dr. Marcos Lopez de Prado (QuantResearch.org). This package has its foundations in the two graduate level textbooks:

  1. Advances in Financial Machine Learning

  2. Machine Learning for Asset Managers

Academic Journals

Unlocking the Commons

We are currently running a sponsorship model of “Unlocking the Commmons”. Our code base, online documentation, tutorial notebooks and presentations will remain open to everyone for so long as we can meet our minimum sponsorship goals. We have set the deadline: December 2020 - for a monthly total patronage of $4000 USD.

Nadia Eghbal explains it well: “If you’d like to open source a project but want to ensure that others will invest in its long-term maintenance, you could tell your community that you’ll open-source the project once you’ve hit a certain amount of sponsorship. (Writer Tim Carmody refers to this as “unlocking the commons.”)”

Become a Patron and keep MlFinLab Open!

Documentation & Tutorials

We lower barriers to entry for all users by providing extensive documentation and tutorial notebooks, with code examples.

Who is Hudson & Thames?

We are a private research group focused on implementing research based financial machine learning. We all work in virtual teams, spread across the world, primarily: New York, London, and Kyiv.

Sponsors and Donating

A special thank you to our sponsors! If you would like to become a sponsor and help support our research, please sign up on Patreon

Benefits include:

  1. Uninterrupted access: Should the code base pivot to closed source - your company will have access to all implementations and the source code.

  2. A seat on the Hudson & Thames advisory council and votes towards the direction of research and implementations.

  3. Ongoing access to slide show presentations and Jupyter Notebooks. (files can be edited to suit your personal needs such as classroom notes or client presentations.)

  4. Company / Organisation profile on www.hudsonthames.org

  5. Use of Hudson & Thames sponsor badge on your website.

  6. Access to our communities Slack Channel.

  7. Subscription to project release updates and news.

Platinum Sponsor:

Platinum Sponsors

Gold Sponsors:

Gold Sponsors

Contact us

We host a booming community of like minded data scientists and quants, join the Slack Channel now! Open to sponsors of our package.

The channel has the following benefits:

  • Community of like minded individuals.

  • Ask questions about the package implementations and get community feedback.

  • Occasional presentations on topics within financial machine learning.

  • A papers channel where we share the papers which are freely available.

  • Access to members of our research group.

Looking forward to hearing from you!

Early Adopters:

John B. Keown

Roberto Spadim

Zack Gow

Alex Zivkovic

Jack Yu

Егор Тарасенок

Joseph Matthew

Aman Dhaliwal

Justin Gerard

Jason Young

Shaun McDonogh

Austin Hubbell

Christian Beckmann

Jeffrey Wang

Eugene Tartakovsky

Ben Zhang

Ming Wu

Richard Scheiwe

Tianfang Wu

Brian Christopher

Eric Detterman

Eric Huang

Gunther Schulz

Geoff Foster

Golam Sakline

Ilya Pikulin

Isabel Gonzalez

Jason Harris

Joshua Cortez

`Kofi Glover`_

Kristian Schmidt

Lucas Astorian

Luque Li

Mikhail Shishlenin

Minsu Yeom

Mislav Sagovac

Paul Morgen

Rino Hilman

Ruifan Pei

Shawn Unger

Bramantyo Erlangga

Sritanu Chakraborty

Stephen Caraher

Swaminathan Sethuraman

Thiago Matos

Thyme Sage

Tom Celig

Tom Parnell

William Thompkins


This project is licensed under an all rights reserved licence.

LICENSE.txt file for details.