Machine-Learning-for-Asset-Managers and Adv_Fin_ML
These are ecosystem siblings—both are independent educational implementations of the same author's (Marcos López de Prado's) different textbooks, serving as complementary learning resources for different books rather than competing solutions to the same problem.
About Machine-Learning-for-Asset-Managers
emoen/Machine-Learning-for-Asset-Managers
Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
This project provides practical code examples from the book "Machine Learning for Asset Managers" by Prof. Marcos López de Prado. It allows quantitative finance professionals to apply concepts like denoising covariance matrices, calculating various distance metrics, and performing optimal asset clustering. The output helps in building more stable portfolios and understanding asset relationships, particularly useful for portfolio managers or quantitative analysts.
About Adv_Fin_ML
ki33elev/Adv_Fin_ML
Solutions for selected exercises from Advances in Financial Machine Learning by Marcos Lopez De Prado
If you're studying 'Advances in Financial Machine Learning' by Marcos Lopez De Prado and want to see practical implementations, this project provides solutions to selected exercises. You'll input your understanding of the textbook's problems and get executable code demonstrations for financial machine learning techniques. This is designed for quantitative finance practitioners, data scientists in finance, or students diving deep into the book's methodologies.
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