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Algorithmic Trading: Winning Strategies and Their Rationale

ISBN: 978-1-118-46014-6
224 pages
May 2013
Algorithmic Trading: Winning Strategies and Their Rationale (1118460146) cover image

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Praise for Algorithmic Trading

"Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers."

—DAREN SMITH, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management

"Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses."

—Roger Hunter, Mathematician and Algorithmic Trader

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Table of Contents

Preface ix

CHAPTER 1 Backtesting and Automated Execution 1

CHAPTER 2 The Basics of Mean Reversion 39

CHAPTER 3 Implementing Mean Reversion Strategies 63

CHAPTER 4 Mean Reversion of Stocks and ETFs 87

CHAPTER 5 Mean Reversion of Currencies and Futures 107

CHAPTER 6 Interday Momentum Strategies 133

CHAPTER 7 Intraday Momentum Strategies 155

CHAPTER 8 Risk Management 169

Conclusion 187

Bibliography 191

About the Author 197

About the Website 199

Index 201

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Author Information

ERNEST P. CHAN is the Managing Member of QTS Capital Management, LLC. He has worked for various investment banks (Morgan Stanley, Credit Suisse, Maple) and hedge funds (Mapleridge, Millennium Partners, MANE) since 1997. Chan received his PhD in physics from Cornell University and was a member of IBM's Human Language Technologies group before joining the financial industry. He was a cofounder and principal of EXP Capital Management, LLC, a Chicago-based investment firm. Chan is also the author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley) and a popular financial blogger at http://epchan.blogspot.com. Find out more about him at www.epchan.com.

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Press Release

May 20, 2013
Algorithmic Trading: Winning Strategies and Their Rationale

In his well-received first book Quantitative Trading, Dr. Ernest Chan addressed the essential techniques an algorithmic trader needs to succeed at this demanding endeavor. While some useful example strategies were presented throughout, they were not the main focus of the book.

With this in mind, Dr. Chan has created a practical guide to algorithmic trading strategies that can be readily implemented by both retail and institutional traders alike. More than an academic treatise on financial theory, Algorithmic Trading is an accessible resource that blends some of the most useful financial research done in the last few decades with valuable insights Dr. Chan has gained from actually exploiting some of those theories in live trading.

Engaging and informative, Algorithmic Trading skillfully covers a wide array of strategies. Broadly divided into the mean-reverting and momentum camps, it lays out standard techniques for trading each category of strategies and, equally important, the fundamental reasons why a strategy should work. The emphasis throughout is on simple and linear strategies, as an antidote to the over-fitting and data-snooping biases that often plague complex strategies. Along the way, it provides comprehensive coverage of:

  • Choosing the right automated execution platform as well as a backtesting platform that will allow you to reduce or eliminate common pitfalls associated with algorithmic trading strategies
  • Multiple statistical techniques for detecting "time series" mean reversion or stationarity, and for detecting cointegration of a portfolio of instruments
  • Issues involving risk and money management based on the Kelly formula, but tempered with the author's practical experience in risk management involving black swans, Constant Proportion Portfolio Insurance, and stop losses

Mathematics and software are the twin languages of algorithmic trading. This book stays true to that view by using a level of mathematics that allows for a more precise discussion of the concepts involved in financial markets. And it includes illustrative examples that are built around MATLAB© codes, which are available for download.

While Algorithmic Trading contains an abundance of strategies that will be attractive to both independent and institutional traders, it is not a step-by-step guide to implementing them. It offers a realistic assessment of common algorithmic trading techniques and can help serious traders further refine their skills in this field.

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