WILEY Advances in Financial Machine Learning

The best guide to apply ML in finance and personal investment

The title sets high expectations, but the content doesn’t live up to them. This book doesn’t really teach machine learning for trading — at least not in a serious or modern way. It presents well-known techniques like cross-validation in backtesting as if they’re new, which they’re not. Anyone with some experience in finance or data science will find much of this material basic or outdated. The author tries to introduce a strict separation between research and development in the very first chapter. Even big institutions have moved away from such failed ideas. Overall, the book promises more than it delivers. If you’re serious about learning how to use machine learning in trading, you’re better off looking elsewhere.

This book explains about a lot of important tips about how to use machine learning technique in financial data. I tried to use machine learning for my fund managing but I didn't notice about some important tips in this book. Now I'm really excited to use these important technique for analyze the stock data.

This book opens your eyes over the world of algoritmic trading. I'm giving a course of trading and it gives another point of view. I've found very interesting the approach using machine learning in a different way, threading very carefully to prevent errors that are usual, and others that are not as easy to spot when using statistics for this type of problems. Highly recommended lecture but it's a little dense, so you will be looping over the same chapter and when you break the loop, you can find some insight in after chapters.

Written for data scientists and financial professionals, not for beginners. Very insightful.

Per chi si interessa di machine learning e algoritmi per la finanza è davvero un libro ottimale. Insegna molte cose utili dal pretrattamento dei dati all'analisi dei risultati per evitare di finire in algoritmi che non funzionano. Insegna a ragionare su misure di riferimento diverse dalle classiche candele dell'analisi tecnica in modo da aver dati molto più digeribili per algoritmi di automazione.

It has taken more than a year to properly digest the material of this book authored by world–renowned Dr. de Prado, an undisputed authority in his field of study. What I liked in particular is the crystal clear way of conveying the applications of ML methods to the respective fields in finance and their limitations, e.g. applying the fractional differencing to financial time series to maintain the stationarity while not compromising on memory, RANSAC method for outliers detection, introducing a novel Deflated Sharpe Ratio concept to account for controlling of experiments, hence, reestablishing rigorous mathematical standards in finance, a true characteristic befitting an academic discipline. And this is just the tip of the iceberg. Curious researcher may want to check out the list of peer reviewed scientific publications by Dr. de Prado to comprehend the research contribution he had already made and is still making to the field of Finance (one of the recent publications relates to exploratory causal analysis, a discipline at the intersection of experimental design, statistics and CS pertaining to learning cause and effect relationships).