I was drawn to E.P. Chan's "Quantitative Trading" (2009) by a process of elimination. After losing half of my buy-and-hold retirement portfolio in the 2007-2009 bear market, I tried and rejected a variety of both fundamental and technical trading strategies. Fear and greed invariably blocked my path to success in any of these trading endeavors. Then I discovered momentum trading strategies that could be automated. Trend following strategies, such as, those proposed by Tom Lydon in "The ETF Trend Following Playbook: Profiting from Trends in Bull or Bear Markets with Exchange Traded Funds" (2009), were especially appealing to me. For example, buy when an equity's price moves above its 200 day moving average. Sell when the price falls below its 200 day moving average. What could be simpler than that? Even more appealing, however, were the relative strength, risk adjusted trading strategies that I discovered at the ETF Replay website. There I found a momentum based, quantitative, statistical model that was mechanical in operation and that separated my trading activities from my emotions, keeping fear and greed in check.
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.
Very good primer on trading and quant trading. I am a beginner and found it very practical and current.
The book does not provide specific strategies (why would anyone give you their profitable strategies anyway?), but it gives guidance on how to go about creating your own strategies and trading business.
It frequently focuses on Matlab, but I just ignored the technicalities of those sections since I do not currently intend to take that route. It's quite useful, however, to get an idea of how sophisticated your competition is!
Other reviewers complained that the price is steep - this may be the case, but the book is interesting to read, and if you get only one single idea from it, it will surely be worth more that the book's cost.
The author does identify the issues at a high level and does provide a few basic strategies. However the issues are not completely flushed out. Neither at a topical or in depth level.
The technical details are not given enough treatment for a novice to really understand the implications or how to apply them. The reader would benefit from an in depth discussion on the pros and cons of various market theories over the brief treatment by the author.
My biggest issue is focused on the chapter addressing Risk Management. The trick of trading really is understand and reducing your risk. Granted it is a complex topic, but honestly the hard part of any trading strategy is determining how to minimize risk while maximizing profits.
He does a good job at laying out most of the topics at a high level, though you would probably be better served by hitting his website and passing on the book.
The focus of this book is equity and futures, and options are specifically not part of the author's trading toolbox. So the arbitrage of trading pairs is one strategy, and one that small investors should avoid. I am not getting paid, so I am really not going to elaborate, but pairs trading is something best left to big players on Wall Street. But I do agree with the author: small investors can easily beat the institutional players; however, they will need more than this book.
I have mixed feelings about this book, but overall, it was underwhelming. First, it really should be emphasized that this is targeted at beginners. Anyone with even a small amount of portfolio management experience will probably be familiar with the techniques discussed. Second, there is a strong emphasis on leverage and Kelly sizing (although he does suggest scaling back, eg half Kelly). Neither of these two statements are negative by themselves, but the combination seems a little dangerous. The type of person who would benefit from the information he presents probably shouldn't be taking large leveraged bets. Admittedly there are plenty of exceptions to that statement, but I think it holds in general. A good first step? Perhaps. A How-to guide for building a trading business? No way.
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.
Product Details :
Hardcover: 208 pages
Publisher: Wiley; 1 edition (November 17, 2008)
Language: English
ISBN-10: 0470284889
ISBN-13: 978-0470284889
Product Dimensions: 6.3 x 0.8 x 9.2 inches
More Details about Quantitative Trading: How to Build Your Own Algorithmic Trading Business 1st edition
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