Welcome to Machine Learning Financial Laboratory!

This repo is public facing and exists for the sole purpose of providing users with an easy way to raise bugs, feature requests, and other issues.

What is MlFinLab?

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. We pride ourselves in the robustness of our codebase - every line of code existing in the modules is extensively tested and documented.

Documentation, Example Notebooks and Lecture Videos

For every technique present in the library we not only provide extensive documentation, with both theoretical explanations and detailed descriptions of available functions, but also supplement the modules with ever-growing array of lecture videos and slides on the implemented methods.

We want you to be able to use the tools right away. To achieve that, every module comes with a number of example notebooks which include detailed examples of the usage of the algorithms. Our goal is to show you the whole pipeline, starting from importing the libraries and ending with strategy performance metrics so you can get the added value from the get-go.

Included modules:

• Backtest Overfitting Tools
• Data Structures
• Labeling
• Sampling
• Feature Engineering
• Models
• Clustering
• Cross-Validation
• Hyper-Parameter Tuning
• Feature Importance
• Bet Sizing
• Synthetic Data Generation
• Networks
• Measures of Codependence
• Useful Financial Features

Licensing options

This project is licensed under an all rights reserved licence.

• Business
• Enterprise

Community

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: [email protected].

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.

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