This is the code repository for my award-winning book. All code was written in Colab notebooks and you might have to tweak it for Jupyter notebooks. You can read Chapter 1 here. Or you can read the entire book for free, and many others, over the next 30 days while accessing the vast resources of O'Reilly Media's learning platform by signing-up here.

Editorial Reviews

"In his no-nonsense defiant style, Kanungo dismisses modern orthodoxies to deliver a superb analysis of probabilistic machine learning; not as a solution, but the most sensible way forward for FinTech." — Ian Angell, Professor Emeritus, London School of Economics.

"Explaining the flaws of conventional models, and the realistic predictions of probabilistic ML models for finance and investing, this book is a significant leap forward in minimizing the reliance on intuition." —Bruno Rignel, Chief Investment Officer, Alpha Key Capital Management.

"Finally, a lucid examination of the fallacies of classical statistics, particularly null hypothesis significance testing and confidence intervals. This book demonstrates the power of modern probabilistic ML and their generative ensembles through insightful applications to finance and investing." - Mike Shwe, former Product Manager, TensorFlow Probability, Google Inc.

"An enlightening book. It makes me think about the flaws of recent machine learning models. A probabilistic machine learning approach gives us another tool to apply." Abdullah Karasan, Founder of Leveragai & Faculty, University of Maryland, Baltimore County.

"One of the best Generative Models books of all time" and "One of the best Probabilistic Algorithms books of all time" - BookAuthority.

"For a more detailed understanding, you might want to refer to the book 'Probabilistic Machine Learning for Finance and Investing' by Deepak K. Kanungo. It provides a comprehensive guide on how to transition to probabilistic machine learning for finance and investing." - Bing AI, on concluding its response to the question "How do I apply probabilistic machine learning to finance and investing problems?' Watch video of Bing AI recommending my book.

Also see my book in Google's Bard recommended list of books for applying probabilistic machine learning to finance and investing problems in particular, and applying machine learning in general to that domain.

From the Publisher

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.

Whether they’re based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.

Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty.This book shows you how.

Deepak K. Kanungo is an algorithmic derivatives trader, instructor, and CEO of Hedged Capital, an AI-powered proprietary trading company he founded in 2009. Since 2019, Deepak has taught tens of thousands of O’Reilly Media subscribers worldwide the concepts, processes, and machine learning technologies for algorithmic trading, investing, and finance with Python. He is the instructor for the O’Reilly course Hands-On Algorithmic Trading With Python

In 2005, long before machine learning was an industry buzzword, Deepak invented a probabilistic machine learning method and software system for managing the risks and returns of project portfolios. It is a unique probabilistic framework that has been cited by IBM, Fujitsu and Accenture, among others. See his patent filing here.

Previously, Deepak was a financial advisor at Morgan Stanley during the Great Financial Crisis, a Silicon Valley fintech entrepreneur, a director in the Global Planning Department at Mastercard International, and a senior analyst with Diamond Technology Partners. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).