With the rapid development of artificial intelligence and machine learning technologies, more and more investment professionals have started to utilize machine learning techniques in their work. Github, as the world’s largest open source community, contains many high-quality repositories and practical guides around using machine learning for investment analysis and decision making. In this article, we will summarize some of the most useful machine learning resources on github for investment professionals.
Github repositories implementing machine learning algorithms for finance
There are many github repositories that provide implementations of machine learning models tailored for financial analysis. For example, the repository ‘Machine Learning for Finance in Python’ by bravehead contains Jupyter notebooks demonstrating machine learning techniques like regression, classification and clustering applied to financial datasets. Another useful repository is ‘deep-quant’, which focuses on deep learning models like LSTM for generating stock market predictions and trading signals. The repository ‘algotrading’ by khabbazian contains Python examples of event-driven backtesting and machine learning for algorithmic trading.
MLOps guides for model deployment and monitoring
For investment professionals looking to deploy machine learning models into production for live trading, there are github repositories providing guides and templates for MLOps – the machine learning operationalization process. The repository ‘MLOps-for-Finance’ by Peltarion contains a full MLOps pipeline from data processing to model deployment monitoring. It demonstrates good practices like using docker, Kubernetes, and Prometheus. The repository ‘Productionizing Finance ML’ by nevaehtyler also covers MLOps topics like CI/CD, testing, and model monitoring specific for machine learning in quantitative finance.
Resources for combining machine learning and finance theory
Some github repositories aim to connect machine learning techniques with financial concepts and domain knowledge. The repository ‘machine-learning-for-trading’ by jmportilla provides an excellent learning path that covers both machine learning algorithms and how to apply them in trading systems based on finance and statistics theory. The repository ‘QuantML’ by paulknysh focuses on machine learning models for asset pricing and risk management, with notebooks bridging the gap between financial economics and data science.
Open source libraries for machine learning in finance
There are also many open source Python libraries published on github that are designed for applying machine learning to financial data. For example, PyThalesians by thalesians contains various wrappers and utilities for machine learning model development. The library FinRL by AI4Finance implements deep reinforcement learning algorithms for automated stock trading. The TA-Lib project on github also provides technical analysis indicators that can be used as features for machine learning models.
In summary, github is a great open source resource for investment professionals to learn and apply machine learning techniques, from finding model implementations, MLOps guides, to finance theory references and reusable libraries.