About

I’m Robert, a quantitative finance graduate from the CQF program, with a strong interest in the intersection of physics, statistics, finance, and computer science. For more about my work, you can visit my website.

I learn best through hands-on projects. In early 2020, I started self-educating in quantitative finance, with a focus on algorithmic trading—a core area of the field. After reading Advances in Financial Machine Learning by Dr. Marcos Lopez de Prado, I wanted to implement some of the machine learning techniques presented in the book. However, I soon realized that existing frameworks, like Mlfinlab, were closed source and inaccessible to the public. While there were forks of the project, the code was outdated and incompatible with modern libraries, such as Pandas 2.0 and the latest versions of Numpy. This motivated me to develop Mlfinpy, a package with a well-documented and intuitive API designed to be up-to-date and available to the broader community.