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Advances in Financial Machine Learning

4.24  ·  Rating details ·  198 ratings  ·  19 reviews
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. Readers will learn how to structure Big data in a way that is ame ...more
ebook, 400 pages
Published January 23rd 2018 by Wiley (first published 2018)
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Aug 14, 2018 rated it really liked it
Machine Learning is about gaining confidence in your algorithm. Looking at a financial trading model, you only get a limited amount of data from, for example, Bloomberg services on which to formulate confidence. Drilling down you may approximate third party transactions on which you can only obtain partial viability. In this book we look at the various factors that obscure a supply data model and which therefore reduce the information that may be derived. Given a large and diverse supply populat ...more
Denis Vasilev
Oct 24, 2018 rated it it was amazing  ·  review of another edition
Практические советы по применению МЛ в торговле на фондовых рынках. Все по делу, очень интересно было глянуть на основные вопросы работы на одном из самых конкурентных рынков.
Max Bolingbroke
Aug 05, 2018 rated it it was ok
Read his free paper on hierarchical risk parity (SSRN 2708678) instead.
Anthony Wittemann
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 mon
Terran M
May 04, 2018 rated it it was amazing
This book is for people who already understand machine learning or predictive modeling, and who already understand investment, and would like some guidance on applying the one to the other. It is an excellent book if and only if you meet these conditions.

The author has a hint of Taleb-style arrogance, wanting to be recognized for being the smartest person in the room, but not enough to impede enjoyment of the book, and it answers the question of why he published it at all in a field which is oth
Oleksandr Nikitin
May 08, 2019 rated it really liked it
Given the overall sad state of the literature in this area, it's good. Also, it's entertaining. Just don't expect it to be a guide of any kind.
Jul 25, 2018 rated it it was amazing
Knowledge like this is hard to come by because it is much more profitable to implement it than to write about it. Marcos must have had an urge to share his knowledge that overwhelmed the common wisdom in this industry - to not share or sell anything that works.
Jul 06, 2020 rated it did not like it
The single most important point of the book is the characterization of the failure modes of systematic (quant) outfits, what almost never works and what he has seems at least sometimes work. This is extremely useful and is possibly applicable to organizations outside of the systematic domain. de Prado also has a paper covering much the same topics.

Overall the book is useful since few are writing books like this. I only wish more effort and time was put into it to increase the quality and output.
Tony Murray
May 07, 2020 rated it really liked it  ·  review of another edition
Overall a decent textbook but one that I found too abstract to really dig into. I’m sure for specific people it is great but as someone who is technically inclined, it just felt a bit too much about him referencing his papers and prior text. I was honestly hoping to be able to translate some of the code snippets from python into R, but the code was very sparsely commented. I am working on a couple of simulations that the author coded and hope to get those translated. So overall it was a 4 star b ...more
Jason Orthman
Aug 09, 2019 rated it liked it
Very difficult book to rate and review as it’s effectively a text book for advanced participants in the field of coding (Python) and financial machine learning. The concepts and principles are still important. There is no easy win for fund managers who want to utilise financial machine learning to attain alpha. You will need a highly experienced team of skilled professionals across finance, coding, mathematics etc that will continue to keep evolving while avoiding common problems such as over-fi ...more
Ferhat Culfaz
A recycle of many of his papers in book. Has the cutting edge, but mix of very specific and at the same time very vague. Very advanced text and assumes you have vast prior knowledge. Very theoretical yet contains snippets of python code for implementation. Good bibliography after each chapter.
Jaume Sués Caula
Mar 15, 2020 rated it really liked it
Not a recommended reading if you are starting up at quantitative trading. The technical depth is astonishing, with great real-life examples.

In my case, I wanted to immerse myself to get the argot and a sense of the complexity of this world (just after reading Jim Simmons biography).
Jan 20, 2019 rated it it was amazing
Excellent book with practical example and issues in financial machine learning
Tadas Talaikis
Dec 13, 2019 rated it really liked it
For interested, here is package based on this book. ...more
Ifor Williams
Oct 09, 2019 rated it it was amazing  ·  review of another edition
To date, best book on ML for trading - by far.
Feb 04, 2019 rated it really liked it  ·  review of another edition
Application of ML algorithms to financial data is straightforward, at least in a technical sense.
Practically, God (or the devil) is in the details.
Randy Carlson
May 27, 2019 rated it really liked it
Not bad. Very technical on both the finance end and the technical end.
Jun 17, 2020 rated it it was amazing
I don't code but the text was pretty accessible.
Dec 04, 2018 rated it it was amazing
This book contains an overview of tricks and techniques useful for time series analysis. I bet you do not know at least 10 of them even if you work with time series on a daily basis. Almost every mathematical description is accompanied by a code sample and this is a gem that gives this book real value. It would be great if other books in ML had same level of reproducibility AND mathematical rigor.
Davide Bulgarelli
rated it it was amazing
Nov 06, 2018
Mathias Luik
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Apr 28, 2020
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Jun 02, 2020
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Nov 08, 2018
Tony Guida
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Jul 18, 2019
Kyle Banks
rated it it was ok
Mar 02, 2019
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Apr 23, 2020
Mario Filho
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Apr 07, 2019
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May 29, 2019
Rodrast AE
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Sep 29, 2019
Lei Hou
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Jul 05, 2018
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165 likes · 32 comments
“Supervised learning algorithms typically require stationary features. The reason is that we need to map a previously unseen (unlabeled) observation to a collection of labeled examples, and infer from them the label of that new observation. If the features are not stationary, we cannot map the new observation to a large number of known examples. But stationary does not ensure predictive power. Stationarity is a necessary, non-sufficient condition for the high performance of an ML algorithm. The problem is, there is a trade-off between stationarity and memory. We can always make a series more stationary through differentiation, but it will be at the cost of erasing some memory, which will defeat the forecasting purpose of the ML algorithm.” 1 likes
“Dollar bars are formed by sampling an observation every time a pre-defined market value is exchanged. Of course, the reference to dollars is meant to apply to the currency in which the security is denominated, but nobody refers to euro bars, pound bars, or yen bars (although gold bars would make for a fun pun).” 0 likes
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