There is a Machine Learning for Factor Investing easy to use project on github
The project objective is to use machine learning and the Python programming language to model returns on public equity investments across four labels: 1, 3, 6, and 12 month timeframes. Performance measures in the context of this notebook are in reference to machine learning performance measures, and not performance measures on portfolios or individual equities. This project will likely not attempt to create portfolios from our ML models, as portfolio contstruction is beyond the scope of this course.
Mainly, the text Machine Learning for Factor Investing (MLFI) by Tony Guida and Dr. Guillaume Coqueret will be used as a guide for developing our models. MLFI will primarily be supplemented by Dr. Yves Hilpisch’s Python for Finance 2d ed (PyFi) chapter 13. Finally, Dr. Marcos Lopez de Prado’s Advances in Financial Machine Learning (AFML) will be used to aid in hyperparameter tuning with cross validation. AFML will also be referenced for our work with Random Forests.
This includes Data that can be downloaded and Jupyter notebook python program with templates for Random Forests, Support Vector Machines, Bayesian Methods and Logistic Regression.