|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Mlfinlab||3,350||2||4||6 months ago||55||August 18, 2021||34||other||Python|
|MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.|
|Eiten||1,557||3 years ago||9||gpl-3.0||Python|
|Statistical and Algorithmic Investing Strategies for Everyone|
|Quantresearch||1,410||a month ago||mit||Jupyter Notebook|
|Quantitative analysis, strategies and backtests|
|Cracking The Data Science Interview||1,291||2 years ago||1||Jupyter Notebook|
|A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep|
|Machine Learning Asset Management||1,159||2 years ago||2||Jupyter Notebook|
|Machine Learning in Asset Management (by @firmai)|
|Alphapy||956||25 days ago||25||August 29, 2020||13||apache-2.0||Python|
|Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost|
|Data Science Portfolio||833||a year ago||2||mit||Jupyter Notebook|
|Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.|
|How To Learn Deep Learning||644||3 months ago||mit|
|A top-down, practical guide to learn AI, Deep learning and Machine Learning.|
|Deepdow||560||a year ago||5||February 16, 2021||27||apache-2.0||Python|
|Portfolio optimization with deep learning.|
|Erlemar.github.io||321||2 years ago||Jupyter Notebook|
|Data science portfolio|
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.
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.
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.
This project is licensed under an all rights reserved licence.
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].
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.