PDF Download Advances in Financial Machine Learning
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Advances in Financial Machine Learning
PDF Download Advances in Financial Machine Learning
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Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.
Listeners will learn how to structure big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. Listeners become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
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Audible Audiobook
Listening Length: 12 hours and 58 minutes
Program Type: Audiobook
Version: Unabridged
Publisher: Gildan Media
Audible.com Release Date: June 19, 2018
Language: English, English
ASIN: B07DLCLTCN
Amazon Best Sellers Rank:
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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