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Algorithmic Trading: Winning Strategies and Their Rationale Hardcover – May 28, 2013
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From the Inside Flap
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
- Simple techniques for trading mean-reverting portfolioslinear, Bollinger band, and Kalman filterand whether using raw prices, log prices, or ratios make the most sense as inputs to these tests and strategies
- Mean-reverting strategies for stocks, ETFs, currencies, and futures calendar and intermarket spreads
- The four main drivers of momentum in stocks and futures, and strategies that can extract time series and cross sectional momentum
- Newer momentum strategies based on news events and sentiment, leveraged ETFs, order flow, and high-frequency trading
- 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.
About the Author
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.
Top customer reviews
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Things to note:
1. All the examples in the book are again in MATLAB, so if you don't have MATLAB you will be at a disadvantage.
2. Whilst the title of the book includes the phrase Algorithmic Trading. It, like the first book, doesn't actually show you how to connect a MATLAB model or system to the market so it can run as an algorithmic trading platform. This was a criticism of the first book. However, if you Google "MATLAB as an Automated Execution System" you'll find a paper that Dr Chan wrote that shows you how to connect MATLAB to Interactive Brokers via a third party MATLAB interface.
3. Whilst the title doesn't use the word quant, be assured the models are again from the quant school. Readers from the TA school of school of oscillators, Gann, MACD etc are not catered for.
Now the book itself:
In the introduction Dr Chan makes it clear the book contains prototype strategies. The book isn't a collection of "strategy recipes" (his term) rather it's about why some strategies should work and how we can look to test and refine them. For each presented strategy we are given a model using MATLAB code. The code is only a snippet; you need to go to Dr Chan's website for the full code.
Many of the models will need further work to accommodate the reader's circumstances, but Dr Chan is clear that he isn't presenting complete models. The book is essentially about why certain approaches to the market should work in theory given the "maths" and what we know about market operations.
Many of the discussed strategies will be familiar to readers of Dr Chan's blog and his first book. The main division in the book is between mean reversion and momentum strategies, with mean reversion getting the greatest attention.
Dr Chan highlights the challenges facing traders of mean reversion, particularly those focusing on pure stock pairs, his preference now is more towards ETFs.
As you come to expect from Dr Chan his theories are well supported by maths and any reader will get a good primer on stationarity, cointegration, dickey fuller test and the Hurst Exponent.
I devoured the first book and spent many hours coding and testing the ideas that were presented. This time around I felt there isn't much new content for a reader or practitioner with a reasonable interest in pair trading, basket trading or a quant approach to momentum trading.
If you haven't read the first book, then this is a better book. It has been updated to reflect the market conditions of the last few years, plus there are greater descriptions of the theory behind why some of these quant models work and ways in which we should look to improve them. So in effect it is an ideal primer for the quant newbie.
As a standalone book and with the knowledge the ideal reader is quant focused then the book is a four.
Readers who already have the first book and maintain an interest in quant will probably feel a little short changed this time around.
Expect to see a lot sentences like this, and expect to re-read them more than once. I also get a hand-wavy, "look how smart I am" vibe in some parts of the book... e.g. on the same page as the previous example "This is a sublinear function of time..." This means nothing to the vast majority of readers, it's basically mathematical fluff.
I wish the author would get to the point more concisely, use equations instead of verbal descriptions of equations, and also cut out the smug "sublinear" type stuff. It's worth buying because there are hardly any other books out there like this, but barely so.
This kind of credibility, from personal experience of "yeah why didn't others tell me this (which I learned the hard way) before?" combined with so much and so very well explained know-how to learn from reading this book is unprecedented, IMHO. I get the impression that reading this book is a very rare opportunity to learn (mostly, if not all) TRUTH without having to laboriously try to separate wheat from chaff when it comes to learning what has been really happening in the trading world.
Now, if only we could all get access to MATLAB and the appropriate required toolboxes for a less prohibitive price!
I bought this book thinking that I would be able to replicate the author's work using an open source alternative to MATLAB such as R or Octave but it is not straight forward since all of the data is in MATLAB format.
At the very least I would have thought the data would be made available so that the examples could be verified, but "As there are a large number of input files involved, I am afraid I won't be uploading the text files, as they occupy much more disk space and my web host has a strict limit on file upload size." [...]