The best quantitative trading books for beginners

Ernest P. Chan Author Of Quantitative Trading: How to Build Your Own Algorithmic Trading Business
By Ernest P. Chan

Who am I?

A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.

I wrote...

Quantitative Trading: How to Build Your Own Algorithmic Trading Business

By Ernest P. Chan

Quantitative Trading: How to Build Your Own Algorithmic Trading Business

What is my book about?

Can a robot take over your trading while you sip Tequila at the poolside? This book will show you how. You only need rudimentary programming skills, a tiny dose of math, and a healthy dose of grit.

In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, I show you how to apply both time-tested and novel quantitative trading strategies. You’ll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as updated back tests on a variety of trading strategies, which included Matlab, Python, and R code examples. You will also find a guide to selecting the best traders and advisors to manage your money.

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The Books I Picked & Why

Statistics and Data Analysis for Financial Engineering: With R Examples

By David Ruppert, David S. Matteson

Statistics and Data Analysis for Financial Engineering: With R Examples

Why this book?

I have used this book to teach my Financial Risk Analytics course at Northwestern University for many years. As a textbook, it is surprisingly easy to read, and the abundant exercises are great. This would be a foundational text to read after you have read my own books. It puts you on solid ground to understand all the financial babble that you may read elsewhere. It includes extensive coverage of most basic topics important to a serious quantitative trader, while not being overly mathematical. Easily understandable if you have basic programming and math background from first year of university.

Everything is practical in this book, which isn’t what you would expect from a textbook! There is no math for math’s sake. I have used the techniques discussed in this book for real trading, and for creating features at my machine learning SaaS predictnow.ai. Examples: What’s the difference between net and log return? How are returns typically distributed? What do “factors” in factor investing really mean? What is mean reversion trading based on? How can we predict volatility?

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Asset Management: A Systematic Approach to Factor Investing

By Andrew Ang

Asset Management: A Systematic Approach to Factor Investing

Why this book?

As the book’s name suggests, it focuses on factor investing – i.e. long-term investments. Example: what do you think is the real (inflation-adjusted) return of the US stock vs bond markets over time? What is the best way to hedge inflation? (The answer may surprise you!) Nevertheless, a trader will also find inspiration in many of the market themes discussed. Example: Why is a mean-reverting strategy equivalent to shorting realized volatility?

This book has even less math than my 1st book pick, since Andrew Ang used it for his investment class for MBAs. Andrew was a well-known finance professor at Columbia University (where Warren Buffet got his Master’s). He is now Head of BlackRock (AUM=$9.5T!) Systematic Wealth Solutions. I have exchanged emails with him, and he is very friendly and patient with questions.

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Advances in Financial Machine Learning

By Marcos Lopez de Prado

Advances in Financial Machine Learning

Why this book?

By now, you may notice that I like to recommend textbooks. I use this bestseller for my course in Financial Machine Learning at Northwestern University, but really, nobody interested in financial machine learning hasn’t read this book. The topics are highly relevant to every investor or trader – I read it at least 5 times to digest every nugget and have put them to very productive use in my trading as well as in my fintech firm predictnow.ai. It covers basic techniques such as random forest to advanced techniques such as Hierarchical Risk Parity, which is a big improvement over traditional portfolio optimization methods.

Marcos used to be Head of Machine Learning at AQR (AUM=$143B), and now is the Global Head of Quant Research at Abu Dhabi Investment Authority. He is also very approachable to his readers and students. There was seldom an email or message from me to which he didn’t reply, and he is also a fan of my books as well!

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Option Trading: Pricing and Volatility Strategies and Techniques

By Euan Sinclair

Option Trading: Pricing and Volatility Strategies and Techniques

Why this book?

Disclaimer: I like Euan’s books not because he is a friend and has endorsed my books. Long before we became friends, I have bought his book, and said to myself “Wow! This is the first book about options trading that is not just a bunch of trite statements about payouts from various straddles and spreads positions!” It talks about some unique arbitrage opportunities that only professionals knew about. On the other hand, the amount of mathematics is very manageable, and can largely be skipped without affecting the practical applications of the concepts. 

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Algorithmic and High-Frequency Trading

By Alvaro Cartea, Sebastian Jaimungal, Jose Penalva

Algorithmic and High-Frequency Trading

Why this book?

Finally, for those who are not afraid of math, they should read this book because there is a lot of heavy-duty math. The good news for the rest of us is you can ignore all the math and still get a lot out of it, especially knowledge about market microstructure and how to find the theoretically optimal trading strategies given some assumptions about the price dynamics. Even if you don’t want to or can’t solve those darn stochastic differential equations, you can still implement a numerical approximation. At the minimum, you will learn common trading lingo such as “walking the book” or “the ITCH feed”.

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