Machine learning has become an increasingly popular tool in the factor investing and quantitative finance field in recent years. By leveraging large datasets and computational power, machine learning models can uncover complex non-linear relationships and dynamics between stock returns and firm characteristics that go beyond classical linear factor models. On online open source platforms like GitHub, there are many useful resources and implementations of machine learning algorithms applied specifically in the context of factor investing and asset pricing. In this article, we will summarize some of the key GitHub repositories and sample code that demonstrate machine learning techniques for alpha signal generation, portfolio construction, risk management and backtesting in factor investing workflows.
MLFactor – R code for the book Machine Learning for Factor Investing
One of the most comprehensive GitHub repositories for machine learning in factor investing is MLFactor (https://github.com/coqueret/mlfactor), the official code repository associated with the book Machine Learning for Factor Investing (2020) by Guida and Coqueret. The book covers important machine learning algorithms like penalized regression, random forests, boosting methods, neural networks and applies them through 7 chapters of worked examples using stocks data. The R code covers the essential data processing, modeling and backtesting steps for a fully working machine learning factor investing workflow.
mlfinlab – Python implementation of classic finance papers and textbooks
For Python users, mlfinlab (https://github.com/hudson-and-thames/mlfinlab) is a great library developed by Hudson & Thames to reproduce various quantitative finance algorithms found in classic textbooks and research papers. It has useful generic modules for feature engineering, clustering, optimal portfolio weights and also implementations of specific machine learning techniques described in various chapters of Advances in Financial Machine Learning by Marcos Lopez de Prado which is considered one of the seminal books in applying machine learning in finance.
Quantopian – Code examples from their online lectures and community
Although the Quantopian platform itself is shutting down, their GitHub org (https://github.com/quantopian) hosts Jupyter notebook examples and lecture content covering various applications of machine learning in algorithmic trading. Some interesting notebooks include predicting stock volatility using random forests, analyzing alternative data using NLP models, generating alpha factors inspired by academic papers through logistic regression and more. There are code examples showing how machine learning can be integrated into a full pipeline from data pre-processing to signal generation, portfolio optimization and backtesting.
pybt – Python Backtesting library with machine learning estimators
For easily incorporating machine learning models into a backtesting workflow in Python, pybt (https://github.com/cuemacro/pybt) provides useful wrappers around sklearn estimators to make them work with Pandas Series for prediction. It also has built-in functionality for position sizing, transaction costs, rebalancing and covers different portfolio construction schemes like HRP, CLA, vol targeting, inverse volatility etc. The examples show approaches for strategy building combining machine learning and rule-based components.
To summarize, GitHub has many high quality repositories like MLFactor, mlfinlab, Quantopian notebooks and pybt that demonstrate practical implementations and provide working code examples of machine learning techniques applied in factor investing and quantitative asset management. For both R and Python, there are ample resources to learn from.