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Advances in Financial Machine Learning
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Review
"In his new book Advances in Financial Machine Learning, noted financial scholar Marcos López de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the financial world today. He points out that not only are business-as-usual approaches largely impotent in today's high-tech finance, but in many cases they are actually prone to lose money. But López de Prado does more than just expose the mathematical and statistical sins of the finance world. Instead, he offers a technically sound roadmap for finance professionals to join the wave of machine learning. What is particularly refreshing is the author's empirical approach — his focus is on real-world data analysis, not on purely theoretical methods that may look pretty on paper but which in many cases are largely ineffective in practice. The book is geared to finance professionals who are already familiar with statistical data analysis techniques, but it is well worth the effort for those who want to do real state-of-the-art work in the field."—Dr. David H. Bailey, former Complex Systems Lead, Lawrence Berkeley National Laboratory. Co-discoverer of the BBP spigot algorithm "Finance has evolved from a compendium of heuristics based on historical financial statements to a highly sophisticated scientific discipline relying on computer farms to analyze massive data streams in real time. The recent highly impressive advances in machine learning (ML) are fraught with both promise and peril when applied to modern finance. While finance offers up the non-linearities and large data sets upon which ML thrives, it also offers up noisy data and the human element which presently lie beyond the scope of standard ML techniques. To err is human but if you really want to f**k things up, use a computer. Against this background, Dr. López de Prado has written the first comprehensive book describing the application of modern ML to financial modeling. The book blends the latest technological developments in ML with critical life lessons learned from the author's decades of financial experience in leading academic and industrial institutions. I highly recommend this exciting book to both prospective students of financial ML and the professors and supervisors who teach and guide them."—Prof. Peter Carr, Chair of the Finance and Risk Engineering Department, NYU Tandon School of Engineering "Marcos is a visionary who works tirelessly to advance the finance field. His writing is comprehensive and masterfully connects the theory to the application. It is not often you find a book that can cross that divide. This book is an essential read for both practitioners and technologists working on solutions for the investment community."—Landon Downs, President and co-Founder, 1QBit "Academics who want to understand modern investment management need to read this book. In it, Marcos Lopez de Prado explains how portfolio managers use machine learning to derive, test and employ trading strategies. He does this from a very unusual combination of an academic perspective and extensive experience in industry allowing him to both explain in detail what happens in industry and to explain how it works. I suspect that some readers will find parts of the book that they do not understand or that they disagree with, but everyone interested in understanding the application of machine learning to finance will benefit from reading this book."—Prof. David Easley, Cornell University. Chair of the NASDAQ-OMX Economic Advisory Board "For many decades, finance has relied on overly simplistic statistical techniques to identify patterns in data. Machine learning promises to change that by allowing researchers to use modern non-linear and highly-dimensional techniques, similar to those used in scientific fields like DNA analysis and astrophysics. At the same time, applying those machine learning algorithms to model financial problems would be dangerous. Financial problems require very distinct machine learning solutions. Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book."—Prof. Frank Fabozzi, EDHEC Business School. Editor of The Journal of Portfolio Management "This is a welcome departure from the knowledge hoarding that plagues quantitative finance. López de Prado defines for all readers the next era of finance: industrial scale scientific research powered by machines."—John Fawcett, Founder and CEO, Quantopian "Marcos has assembled in one place an invaluable set of lessons and techniques for practitioners seeking to deploy machine learning techniques in finance. If machine learning is a new and potentially powerful weapon in the arsenal of quantitative finance, Marcos' insightful book is laden with useful advice to help keep a curious practitioner from going down any number of blind alleys, or shooting oneself in the foot."—Ross Garon, Head of Cubist Systematic Strategies. Managing Director, Point72 Asset Management "The first wave of quantitative innovation in finance was led by Markowitz optimization. Machine Learning is the second wave and it will touch every aspect of finance. López de Prado's Advances in Financial Machine Learning is essential for readers who want to be ahead of the technology rather than being replaced by it."—Prof. Campbell Harvey, Duke University. Former President of the American Finance Association "The complexity inherent to financial systems justifies the application of sophisticated mathematical techniques. Advances in Financial Machine Learning is an exciting book that unravels a complex subject in clear terms. I wholeheartedly recommend this book to anyone interested in the future of quantitative investments."—Prof. John C. Hull, University of Toronto, Author of Options, Futures, and other Derivatives "Prado's book clearly illustrates how fast this world is moving, and how deep you need to dive if you are to excel and deliver top of the range solutions and above the curve performing algorithms... Prado's book is clearly at the bleeding edge of the machine learning world."—Irish Tech News "Financial data is special for a key reason: The markets have only one past. There is no 'control group', and you have to wait for true out-of-sample data. Consequently, it is easy to fool yourself, and with the march of Moore's Law and the new machine learning, it's easier than ever. López de Prado explains how to avoid falling for these common mistakes. This is an excellent book for anyone working, or hoping to work, in computerized investment and trading."—Dr. David J. Leinweber, Former Managing Director, First Quadrant, Author of Nerds on Wall Street: Math, Machines and Wired Markets"In his new book, Dr. López de Prado demonstrates that financial machine learning is more than standard machine learning applied to financial datasets. It is an important field of research in its own right. It requires the development of new mathematical tools and approaches, needed to address the nuances of financial datasets. I strongly recommend this book to anyone who wishes to move beyond the standard Econometric toolkit." —Dr. Richard R. Lindsey, Managing Partner, Windham Capital Management, Former Chief Economist, U.S. Securities and Exchange Commission"Dr. Lopez de Prado, a well-known scholar and an accomplished portfolio manager who has made several important contributions to the literature on machine learning (ML) in finance, has produced a comprehensive and innovative book on the subject. He has illuminated numerous pitfalls awaiting anyone who wishes to use ML in earnest, and he has provided much needed blueprints for doing it successfully. This timely book, offering a good balance of theoretical and applied findings, is a must for academics and practitioners alike." —Prof. Alexander Lipton, Connection Science Fellow, Massachusetts Institute of Technology. Risk's Quant of the Year (2000) "How does one make sense of todays’ financial markets in which complex algorithms route orders, financial data is voluminous, and trading speeds are measured in nanoseconds? In this important book, Marcos López de Prado sets out a new paradigm for investment management built on machine learning. Far from being a 'black box' technique, this book clearly explains the tools and process of financial machine learning. For academics and practitioners alike, this book fills an important gap in our understanding of investment management in the machine age."—Prof. Maureen O'Hara, Cornell University. Former President of the American Finance Association "Marcos López de Prado has produced an extremely timely and important book on machine learning. The author's academic and professional first-rate credentials shine through the pages of this book - indeed, I could think of few, if any, authors better suited to explaining both the theoretical and the practical aspects of this new and (for most) unfamiliar subject. Both novices and experienced professionals will find insightful ideas, and will understand how the subject can be applied in novel and useful ways. The Python code will give the novice readers a running start, and will allow them to gain quickly a hands-on appreciation of the subject. Destined to become a classic in this rapidly burgeoning field."—Prof. Riccardo Rebonato, EDHEC Business School. Former Global Head of Rates and FX Analytics at PIMCO "A tour de force on practical aspects of machine learning in finance brimming with ideas on how to employ cutting edge techniques, such as fractional differentiation and quantum computers, to gain insight and competitive advantage. A useful volume for finance and machine learning practitioners alike."—Dr. Collin P. Williams, Head of Research, D-Wave Systems
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From the Inside Flap
Today's machine learning (ML) algorithms have conquered the major strategy games, and are routinely used to execute tasks once only possible by a limited group of experts. Over the next few years, ML algorithms will transform finance beyond anything we know today. Advances in Financial Machine Learning was written for the investment professionals and data scientists at the forefront of this evolution. This one-of-a-kind, practical guidebook is your go-to resource of authoritative insight into using advanced ML solutions to overcome real-world investment problems. It demystifies the entire subject and unveils cutting-edge ML techniques specific to investing. With step-by-step clarity and purpose, it quickly brings you up to speed on fully proven approaches to data analysis, model research, and discovery evaluation. Then, it shines a light on the nuanced details behind innovative ways to extract informative features from financial data. To streamline implementation, it gives you valuable recipes for high-performance computing systems optimized to handle this type of financial data analysis. Advances in Financial Machine Learning crosses the proverbial divide that separates academia and the industry. It does not advocate a theory merely because of its mathematical beauty, and it does not propose a solution just because it appears to work. The author transmits the kind of knowledge that only comes from experience, formalized in a rigorous manner. This turnkey guide is designed to be immediately useful to the practitioner by featuring code snippets and hands-on exercises that facilitate the quick absorption and application of best practices in the real world. Stop guessing and profit off data by: Tackling today's most challenging aspects of applying ML algorithms to financial strategies, including backtest overfitting Using improved tactics to structure financial data so it produces better outcomes with ML algorithms Conducting superior research with ML algorithms as well as accurately validating the solutions you discover Learning the tricks of the trade from one of the largest ML investment managers Put yourself ahead of tomorrow's competition today with Advances in Financial Machine Learning.
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Product details
Hardcover: 400 pages
Publisher: Wiley; 1 edition (February 21, 2018)
Language: English
ISBN-10: 1119482089
ISBN-13: 978-1119482086
Product Dimensions:
6.3 x 1.2 x 9.1 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
104 customer reviews
Amazon Best Sellers Rank:
#8,421 in Books (See Top 100 in Books)
TLDR: the book is awesome, it really is on another level, and you will be stuck in the past if you don't ingest this book.If you are not in the target audience I think you will find this book hard to digest. Also I have read some chapters twice and worked through the code samples, so I believe I offer a perspective that other readers may be lacking. Marcos has given a number of lectures titled “The 7 Reasons Most Machine Learning Funds Failâ€, you can find the lecture slides online. The seven core ideas in that lecture are covered in chapters 2-8, with other chapters offering supporting details, or going further in depth. If you have limited time to process the book, I think you would be better served by taking a deep dive on chapters 2-8, rather than skimming the whole thing.The ideas in this book work, and you would be doing yourself a disservice by not reading this book. Some of the ideas range from the common sense (backtesting is not a research tool, feature importance is) to the heretical ("fordecades most financial research has been based on over-differentiated (memory-less) series, leading to spurious forecasts and overfitting.") [That quote was in his 7 Reasons presentation from Quantcon 2018, not the book.] He offers compelling arguments and solutions backed by peer reviewed publications for all his points.The book would be a highly valuable reference even without the code snippets, but he provides functional code and even tools to make it work on large datasets. Once again this code is not for the faint of heart, his use of Pandas will leave even a seasoned financial developer to RTFM.There are some flaws which I can overlook. Strict software engineers will be irked at the code violating PEP8. It is hard to put code samples into a book so things like multiple statements per line can greatly compact the code and make it readable. In chapter 20 he uses threads and processes interchangeably although they are two distinct tools. Chapter 22 felt a little out of place but it seems compulsory for financial authors to include a "just for fun" final chapter. There was a quick discussion at the end of Chapter 14 on performance attribution, which felt rushed and I feel it would be hard for the non-financial portion of the target audience to follow. These are minor items. I found at least three errors in the code which I hear have been corrected in the second printing.It is arguable that the ideas in this book could be extended to any asset class. If I had to guess, I would say this was often applied to trading futures, although bonds, equities, and equity options are briefly mentioned.
I have run through a quick pass of the entire text in one sitting, so I may possibly re-read more in depth and alter my review at some point in the future.My impression is that the text reads a bit like an academic survey of some existing ML methods applied to quantitative finance, a bit heavy on theoretical models and sourcing many fairly recent papers culled from various financial and machine learning literature, many referenced from the author himself. However, the author also points out that he has a lot of experience in the quantitative field and elaborates a bit on the overall systematic step by step process of development that a real team of quants might use. Don't expect an in depth description of specific implementations (like SVMs, Gradient Boosting, NNs,etc), but a more general approach to the various learner methods.The Good:I enjoyed getting his perspective on the overall flow and piece by piece breakdown on each of the steps involved in the process of developing a ML based algorithm, from data collection, partitioning, and scrubbing, all the way to the design and execution phase, including a lengthy description of some of the pitfalls and possible solutions to using various cross-validation methods, in order to gain better confidence in financial data and algorithms, that many already know suffer from characteristics like non-IID properties, data overlap, and time dependencies. On the more concrete side, he also presents many standalone python based functions to concretely implement many of the concepts that he describes.The bad:While it definitely reads like it is written from someone with a strong theoretical background, and much experience in the financial field. I also, felt that it fails in that it never really integrates all of the build up to a practical example of a systematic design implementation, that uses many of his ideas and demonstrates their validity. In other words, do not expect any top level concrete design or systematic design and back-test examples with real financial data and results at all. It is mainly bits and pieces of the pipeline that ultimately may go into a complete systematic development of a system, but no real evidence that any of it is of use, other than to take the author's word, or just accept the theoretical modelling. To clarify further, it's ok to point out the shortcomings of classical portfolio optimization, but show a clear example of an ML based portfolio optimization; how does it perform using various validation methods compared to classical? Using real, cleaned financial data.It would definitely be useful to see at least one complete implementation of a system that utilizes the methods described within. In addition, concepts like quantum computing are great and all, but when you've been at this development long enough, the more fancy and advanced the tools sound, they don't really bring all that much to the table, if you can't even develop a successful system or algorithm at a much simpler level (which is not easy).update(s): I'll just add that, after a closer reading, hasn't really changed my opinion much. However, if it helps anyone I found an excellent simulation of HRP, using real financial data on ilya kipnis great R based blog, QuantStratTradeR. This is the kind of empirical data, that would really add value to the text.
Having spent more than two decades on equity desks building cash and options systems, it is refreshing to come across a book that is so comprehensive. The first part of the book tackles the construction of a data strategy. Marcos provides both theoretical foundations as well as practical examples for those building a data plant geared towards both general trading as well as focusing on machine learning driven strategies. The second part of the book focuses on extending basic machine learning concepts to financial data. The author spends a lot of this section focusing on validation techniques specifically for financial features. The third section of the book focuses entirely on backtesting. In this section, he develops a number of novel approaches to backtesting machine learning models as well as measuring the performance of those models. The fourth section delves into some of the most important ML features in financial markets and how to build models around them. The last section focuses on how to scale your ML models with both off the shelf software, high performance computing hardware(via LBNL's CIFT Project), and quantum computing approaches(via quantum annealer from D-WAVE). All in all, the book provides an excellent roadmap for building and operating ML based trading strategies.
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