WILEY Advances in Financial Machine Learning Hardcover – Big Book, 4 May 2018

The best guide to apply ML in finance and personal investment

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).

Atende às minhas expectativas atuais e futuras, em termos de trabalhos de pesquisa.

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.

Often books on quantitative finance are too fixated on theoretical know-hows or are completely from a practitioners perspective with disregard to the theoretical foundations. Advances in Financial Machine learning does a great job in presenting a balanced view on the aspects to keep in mind while using Machine Learning in finance. A theoretical model together with machine learning algorithm might look good, but how do you test it with real data sets without falling into the common traps is something quite necessary to know. The book does a great job in making us realise that. Would highly recommend the book to anyone interested in applying machine learning in finance.

If you're coming from a computer science and/or machine learning background, you will learn a lot about how to frame your algorithmic thinking in the domain of finance and will leave you hungry for more hardcore graph theory, parallelization, machine learning (beyond simple random forest ensembles and clustering), advanced algorithms, and gutty details of implementation, which are left for you to explore and enjoy.The purpose of this book is not to explain how to apply Deep Learning to make money, but rather to lay a solid foundation of how to invest in a scientifically rigorous fashion given the modern machine learning toolset and access to PBs of data. In many cases, rather than focussing on the specifics of any given model, Dr. Lopez de Prado focuses on generating and selecting useful features.The book, which is a hybrid of a textbook and a manual, explains using both formal mathematics and empirical evidence why many of the assumptions about Machine Learning applied to the financial world are wrong and follows through with rigorous and practical solutions. For example, one of the most common false assumptions addressed in the book is that of IID samples in financial time series data.Dr. Lopez de Prado manages to pull together ideas from a wide spectrum of academic disciplines including mathematics, econometrics, machine learning, computer science, information theory, and physics to build a strong scientific basis upon which to algorithmically invest. Despite the diversity of subject matter, the book progresses well, building on and reusing early themes and then exploring domain specific topics like market microstructure and quantum computing. Source code to implement many of the methods is provided as a practical toolkit to test out the claims presented. The thorough use of references is particularly helpful as it keeps the content fairly short and to the point.Speed reading not recommended. Using a programming analogy, the mathematical notation is more reminiscent of the explicit verbosity of C++ than that of python (which is used in the book and is meant to be concise). It's not much of a problem but be aware the information content is dense.Something that's mentioned but not explored is how to make use of “alternative datasets”. Given many of the advances in the wider realm of ML have been around data you don’t get from exchanges, it would be nice if some helpful pointers or references for dealing with alternative data were included. That said, it's not the end of the world given the wealth of resources online for analyzing text, image, and video data.Buy this book if you're an experienced programmer getting into Finance or a Financial Professional looking to strengthen your algorithmic understanding. It is densely packed with a wealth of practical methods and breaks down and offers alternatives to faulty investing science.