Quantitative Trading: How to Build Your Own Algorithmic Trading Business Hardcover – Nov 17 2008
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From the Inside Flap
By some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traderswith limited resources and less computing powerhave wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, author Dr. Ernest Chan, a respected independent trader and consultant, will show you how.
Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.
Organized around the steps you should take to start trading quantitatively, this book skillfully addresses how to:
Find a viable trading strategy that you're both comfortable with and confident in
Backtest your strategywith MATLAB®, Excel, and other platformsto ensure good historical performance
Build and implement an automated trading system to execute your strategy
Scale up or wind down your strategies depending on their real-world profitability
Manage the money and risks involved in holding positions generated by your strategy
Incorporate advanced concepts that most professionals use into your everyday trading activities
And much more
While Dr. Chan takes the time to outline the essential aspects of turning quantitative trading strategies into profits, he doesn't get into overly theoretical or sophisticated theories. Instead, he highlights the simple tools and techniques you can use to gain a much-needed edge over today's institutional traders.
And for those who want to keep up with the latest news, ideas, and trends in quantitative trading, you're welcome to visit Dr. Chan's blog, epchan.blogspot.com, as well as his premium content Web site, epchan.com/subscriptions, which you'll have free access to with purchase of this book.
As an independent trader, you're free from the con-straints found in today's institutional environmentand as long as you adhere to the discipline of quantitative trading, you can achieve significant returns. With this reliable resource as your guide, you'll quickly discover what it takes to make it in such a dynamic and demanding field.
From the Back Cover
Praise for Quantitative Trading
"As technology has evolved, so has the ease in developing trading strategies. Ernest Chan does all traders, current and prospective, a real service by succinctly outlining the tremendous benefits, but also some of the pitfalls, in utilizing many of the recently implemented quantitative trading techniques."
PETER BORISH, Chairman and CEO, Computer Trading Corporation
"Dr. Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, programming knowledge, and real-time system implementation to develop and run an algorithmic trading business step by step in Quantitative Trading."
YASER ANWAR, trader
"Quantitative systematic trading is a challenging field that has always been shrouded in mystery, seemingly too difficult to master by all but an elite few. In this honest and practical guide, Dr. Chan highlights the essential cornerstones of a successful automated trading operation and shares lessons he learned the hard way while offering clear direction to steer readers away from common traps that both individual and institutional traders often succumb to."
ROSARIO M. INGARGIOLA, CTO, Alphacet, Inc.
"This book provides valuable insight into how private investors can establish a solid structure for success in algorithmic trading. Ernie's extensive hands-on experience in building trading systems is invaluable for aspiring traders who wish to take their knowledge to the next level."
RAMON CUMMINS, private investor
"Out of the many books and articles on quantitative trading that I've read over the years, very few have been of much use at all. In most instances, the authors have no real knowledge of the subject matter, or do have something important to say but are unwilling to do so because of fears of having trade secrets stolen. Ernie subscribes to a different credo: Share meaningful information and have meaningful interactions with the quantitative community at large. Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike."
STEVE HALPERN, founder, HCC Capital, LLC
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Most Helpful Customer Reviews on Amazon.com (beta)
There doesn't seem to have been much original research conducted for the purposes of the book. He basically talks about the programs, brokerages, etc. he has used in the past and gives his opinions on a few. This is valuable to some extent, but in a book targeted at starting up a new business, I would have expected him to survey the landscape a little more. Just as an example, his code is in Matlab which he admits is probably outside the price range for many startups. Why not show the code in R? Or one of the cheaper/free Matlab clones he mentions?
There were a few technical areas I thought he breezed over too nonchalantly (assuming strategy return independence in the Kelly formula for example) that could be dangerous, but as the book is targeted at beginners, I won't hit him for those.
As a final point, the book is completely overpriced. Large font + small book + not much more than 150 pages (many of which are either code or modified entries from his blog) = where's the beef? Again, that's OK, but not in the context of a (ridiculous) $60 sticker price. Even the ~$40 at Amazon is about 2x what I believe is justified.
What makes the book work is Dr Chan's personal insights from his career and experiences are intertwined throughout the topics. This leaves the reader with a great deal of confidence in his suggested approach. Most of the issues in the book are covered at a basic to intermediate level - certainly enough for you to be able to go and do further research and to know what the key issues are and what to look out for.
Criticisms - maybe the focus on Matlab, though he does recommend some cheaper clone packages that may run his example code with minor modifications.
This is a practical book. There's no pop trading psychology (though there is a psychology chapter), nor does it give you a system or rely on strategies that aren't generally available to the retail trader.
Being quantitative, strategies where discussed tend to be mean reversion or momentum based. Technical analysis isn't used as a driver for strategies. Ideas for potential quantitative strategies are discussed, but readers looking for up and running systems will need to look elsewhere.
Instead, Quantitative Trading shows the procedure for taking a strategy, combining it with correct test techniques, good execution tools and position management, and from that forming the basis for a genuinely successful home based quantitative operation.
Anyone who trades would benefit from this book as many of its discussed issues, techniques, tips and traps are universal to any trader.
While there is no ready-made, plug-and-play strategies inside the book, it does contain a number of potential strategy ideas that is worth testing. Chan is mostly focused on long/short equity strategies, mostly because that's is where he has his background. A good read, would read it again.
Was Chan's book written for traders like me? Not exactly. The author had in mind a reader who wants to know (1) how to start a quantitative trading business or (2) how to work as a quantitative trader at a major institution. I, on the other hand, was simply looking for ways to enhance my skills as an independent trader for managing my personal accounts.
In reading this book, I felt like a minor league player asking for help from a major league coach. Chan is a true quant with both institutional and independent trading experience. Chan offers way more expertise than I can use. For instance, Chan's book applies to trading that can be characterized as algorithmic, mean-reverting, fully automated, intraday, high frequency, leveraged, risk adjusted, and benchmarked with high Sharpe ratios and low drawdowns. My trading, on the other hand, is mechanical, not algorithmic; momentum based, not mean-reverting; semi-automated, not fully automated, monthly traded, not intraday; low frequency, not high frequency; unleveraged, not leveraged; risk adjusted and benchmarked with high Sharpe ratios and low drawdowns -- these last items being significant points of agreement with Chan.
Chan moves his readers step-by-step from determining their aptitude for quantitative trading in Chapter 1 to growing a quantitative trading business in Chapter 8. Along the way, Chan tells his readers how to select a trading strategy, how to backtest their strategy using MATLAB (or Excel), how to build an automated trading system, how to manage their money and risks, and how to refine and improve their trading strategies.
Quantitative trading is known by several other names: "algorithmic trading," "automated trading," "computer trading," and Chan's favorite, "statistical arbitrage trading." Incidentally, for statistical arbitrage trading to work, both Random Walking and the Efficient Market Hypothesis must fail.
Are there prerequisites? To benefit from Chan's book, the reader needs to have at least a first year college proficiency in statistics, algebra, and computer programming. Given this minimal background, the reader can then proceed to become an independent trader who will be able to outperform institutional money managers at their own game, namely, statistical arbitrage trading.
By the time you finish Chan's book, your statistical arbitrage trading kit will include such tools as geometric mean, moving average, standard deviation, linear regression, Gaussian distribution, mean-reverting time series, half-life time series, principal components analysis, Kelly Formula, and Sharpe ratio -- and the means to achieve a consistent monthly stream of revenue.