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Quantitative Trading
How to Build Your Own Algorithmic Trading Business
Second Edition
ERNEST P. CHAN
Copyright © 2021 by Ernest P. Chan. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data is Available:
ISBN 9781119800064 (hardback)
ISBN 9781119800088 (ePDF)
ISBN 9781119800071 (ePub)
Cover Design: Wiley
Cover Image: © Jobalou/Getty Images
To my parents, Hung Yip and Ching, and to Ben and Sarah Ming.
Preface to the 2nd Edition
When I first started thinking about writing the 2nd edition, I had a measure of dread. What could I have added that would be new and interesting? After writing the first draft, I was relieved, and incredibly excited, at the prospect of sharing with you my latest knowledge, techniques, and insights, ranging from the addition of some new functions that make our PCA example run more than 10x faster, to a novel application of machine learning.
In the 1st edition of this book, published more than a decade ago, I maintained that independent quantitative traders can beat institutional managers at their own game. Many of you have taken that advice to heart, and many retail quantitative trading communities and platforms have been built to serve just such an ambition. But does the premise still hold?
Over the years, many readers reached out and told me how successful they have been in improving and trading the strategies I discussed in my books, and others told me how they have simply been inspired by my books to become successful traders. Our fund is invested in some of these readers, some of whom have been managing many millions more dollars than we are. So, the answer to the above question is a resounding “YES!”
I also exhorted retail traders new to quantitative trading to start with the simplest strategies (examples of which are described in this and my previous books). Do simple strategies still work? Or do we all have to become mathematicians or machine learning experts?
My colleagues and I traded some of the strategies described in this book live since it was first published in 2009, and ran true out-of-sample backtests on others, and I was as surprised as they are that many still work after all these years. But the issues of “alpha decay,” and the even-more-dreaded “regime change,” are ever threatening. I will talk more about that below.
Speaking of machine learning and artificial intelligence, I didn't really think much of those techniques in my first book. In fact, the only artificial intelligence platform that I described there has gone out of business. But you may hear that AI is everywhere nowadays, and many fundamental advances in AI have been made since then. For example, the dropout technique that gave birth to deep learning achieved fame in 2012 (Gershgorn, 2017). Should retail traders still avoid AI/ML?
It is as difficult to apply AI/ML to finance in 2021 as it was in 2009, but you may be surprised to hear that we have finally succeeded (Chan, 2020). We have benefited from other giants in the industry who graciously share their insights and knowledge with everyone (López de Prado, 2018). We, in turn, tried to make it easier for every retail trader (even those who are not programmers) to benefit from this technology by launching predictnow.ai. Here is the spoiler: The key to successfully apply AI/ML to finance is to focus on metalabeling – i.e., finding the probability of profit of your own simple basic trading strategy, and not to use it to predict the market directly. Why? Your own trading strategy's past track record is private; no one else is trying to predict its success. Meanwhile, millions of people around the world are watching the same public market, and everyone is trying to predict where it will go. Competition and arbitrage naturally mean that signal-to-noise ratio is very low and predictive successes are few and far in between.
But that's not all. There is another novel use of machine learning that I will discuss in a completely revised
