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:

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:

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:

Uninterrupted access: Should the code base pivot to closed source - your company will have access to all implementations and the source code.
A seat on the Hudson & Thames advisory council and votes towards the direction of research and implementations.
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.)
Company / Organisation profile on www.hudsonthames.org
Use of Hudson & Thames sponsor badge on your website.
Access to our communities Slack Channel.
Subscription to project release updates and news.

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

License¶

LICENSE.txt file for details.