Machine Learning Financial Laboratory (mlfinlab)

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

Praise for MlFinLab

“Financial markets are complex systems like no other. Extracting signal from financial data requires specialized tools that are distinct from those used in general machine learning. The MlFinLab package compiles important algorithms that every quant should know and use.”

- Dr. Marcos Lopez de Prado, Co-founder and CIO at True Positive Technologies; Professor of Practice at Cornell University

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“Those who doubt open-source libraries just need to look at the impact of Pandas, Scikit-learn, and the like. MIFinLab is doing to financial machine learning what Tensorflow and PyTorch are doing to deep learning.”

- Dr. Ernest Chan, Hedge Fund Manager at QTS & Author

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“For many decades, finance has relied on overly simplistic statistical techniques to identify patterns in data. Machine learning promises to change that by allowing researchers to use modern nonlinear and highly dimensional techniques. Yet, applying those machine learning algorithms to model financial problems is easier said than done: finance is not a plug-and-play subject as it relates to machine learning.

MlFinLab provides access to the latest cutting edges methods. MlFinLab is thus essential for quants who want to be ahead of the technology rather than being replaced by it.”

- Dr. Thomas Raffinot, Financial Data Scientist at ENGIE Global Markets

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Documentation, Tutorials, Videos, and Source Code

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

We in the process of experimenting with various product ideas and models. Currently all of our tools which are private, are available to the various Patreon tiers.

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.

• Website

• Github Group

• Project Boards

• Documentation

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 and purchase one of our many tiers.

Benefits include:

1. Access to source code via Github.

2. Access to all online documentation.

3. Access to all slide show presentations and Jupyter Notebook tutorials.

4. Video lecture series and recordings.

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

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

7. Twitter Shoutout!

8. Access to our communities Slack Channel.

9. Subscription to project release updates and news.

Platinum Sponsor:

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.

You can also email us at research@hudsonthames.org

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

License

This project is licensed under an all rights reserved licence.

LICENSE.txt file for details.