With the rise of machine learning in finance, more and more investment banks and financial institutions are leveraging machine learning algorithms to gain an edge. This has also led to increased demand for machine learning skills in investment banking. Luckily, there are many high-quality open source machine learning resources and courses available on Github that aspiring investment bankers can use to pick up machine learning skills. Going through these materials can give a significant boost to one’s resume and improve chances of landing investment banking roles that require machine learning expertise.
Awesome machine learning for trading Github repo offers a rich collection of resources
One of the most comprehensive Github repositories for machine learning in trading is Awesome Machine Learning for Trading (https://github.com/firmai/financial-machine-learning). This repo contains a curated list of over 200 open source projects, datasets, blogs, courses, and other materials related to using machine learning in quantitative finance. It covers common machine learning algorithms like regression, deep neural networks, tree-based models, clustering methods etc. and how they can be applied for stock price prediction, portfolio optimization, risk management, high frequency trading strategies, and more in the investment banking domain. The well-organized structure makes it easy for beginners to find the right resources.
Udacity’s Machine Learning for Trading course teaches key concepts with hands-on projects
For those looking for a structured machine learning course, Udacity’s Machine Learning for Trading Nanodegree program (https://github.com/udacity/ML4T_2019Spring) is a great choice. This repository contains the course materials including lecture notes, quiz questions and coding projects. The course covers important machine learning concepts like regression, classification, PCA, momentum strategies, Markov models, etc. tailored to a trading context. Students get to implement their own basic trading strategies and backtesting engine in Python. Completing this gives a practical understanding of how to apply machine learning in investment banking and trading firms.
MLtrading repo has 100+ Jupyter notebooks demonstrating machine learning algorithms
The MLtrading repository (https://github.com/stefan-jansen/machine-learning-for-trading) contains over 100 Jupyter notebooks that demonstrate how to implement various machine learning algorithms useful for trading strategies. Algorithms covered include regression models, CNN, RNN, LSTM, sentiment analysis with NLP, reinforcement learning models like Q-learning, evolution strategies and more. All notebooks provide clear code examples showing data preparation, model training, backtesting and visualization. This is an excellent hands-on resource to gain proficiency in trading-focused machine learning.
Other valuable Github repositories for machine learning in finance
Some other high-quality Github repositories for machine learning in investment banking and quantitative finance include Quantopian Lectures (https://github.com/quantopian/lectures) which explains machine learning concepts tailored to algorithmic trading, ML-in-Trading (https://github.com/stefanonardo/ML-in-Trading) which has basic trading strategy implementations with machine learning, CompInvFinRL (https://github.com/AI4Finance-LLC/CompInvFinRL) which focuses on using reinforcement learning for portfolio management, and Quantiacs (https://github.com/Quantiacs/quantiacs-python) which allows developing and backtesting machine learning trading systems via API access.
In summary, Github hosts some amazing open source machine learning resources like courses, notebooks and code repos that are highly valuable for investment banking professionals and financial engineers looking to break into machine learning. Mastering these materials can significantly improve one’s odds of landing lucrative roles in top investment banks that incorporate machine learning in their workflows.