The best way to learn something really well is to teach it to someone else (Bargh and Schul, 1980). So I confess that one major motivation for my writing this book, the third and the most advanced to date in a series, is to force myself to study in more depth the following topics:

• The latest backtesting and trading platforms and the best and most cost‐effective vendors for all manners of data (Chapter 1);
• How to pick the best broker for algorithmic executions and what precautions we should take (Chapter 1);
• The simplest way to optimize allocations to different assets and strategies (Chapter 1);
• Factor models in all their glory, including those derived from the options market, and why they can be useful to short‐term traders (Chapter 2);
• Time series techniques: ARIMA, VAR, and state space models (with hidden variables) as applied to practical trading (Chapter 3);
• Artificial intelligence/machine learning techniques: particularly methods that will reduce overfitting (Chapter 4);
• Options and volatility trading strategies, including those that involve portfolios of options (Chapter 5);
• Intraday and higher frequency trading: market microstructure, order types and routing optimization, dark pools, adverse selection, order flow, and how to backtest intraday strategies with tick data (Chapter 6);
• Bitcoins: bringing some of the techniques we covered to this new asset class (Chapter 7);
• How to keep up with the latest knowledge (Chapter 8);
• Transitioning from a proprietary trader to an investment advisor (Chapter 8).

I don't know if these topics will excite you or bring you profits, but my study of them has certainly improved my own money management skills. Besides, sharing knowledge and ideas is fun and ultimately conducive to creativity and profits.

You will find most of the materials quite accessible to anyone who has some experience in a quantitative field, be it computer science, engineering, or physics. Not much prior knowledge of trading and finance is assumed (except for the chapter on options, where we do assume basic familiarity). However, if you are completely new to trading, you may find my more basic treatments in Quantitative Trading (Chan, 2009) and Algorithmic Trading (Chan, 2013) easier to understand. This book can be treated as a continuation of my first two books, with coverage on topics that I have not discussed before, but it can also be read independently.

Although many prototype trading strategies have been included as examples, one should definitely not treat them as shrink‐wrapped products ready to deploy in live trading. As I have emphasized in my previous books, nobody should trade someone else's strategies without a thorough, independent backtest, removing all likely sources of biases and data errors, and adding various variations for improvement. Most, if not all, the strategies I describe contain hidden biases in one way or another, waiting for you to unearth and eliminate.

I use MATLAB for all of my research in trading. I find it extremely user‐friendly, with constantly improving and new features, and with an increasing number of specialized toolboxes that I can draw on. For example, without the Statistics and Machine Learning Toolbox, it would take much longer to explore using AI/ML techniques for trading. (See why Google scientist and machine learning expert Kevin Murphy prefers MATLAB to R for AI/ML research in Murphy, 2015.) In the past, readers have complained about the high price of a MATLAB license. But now, it costs only $150 for a “Home” license, with each additional toolbox costing only $45. No serious traders should compromise their productivity because of this small cost. I am also familiar with R, which is a close relative to MATLAB. But frankly, it is no match for MATLAB in terms of performance and user‐friendliness. A detailed comparison of these languages can be found in Chapters 1 and 6. If you don't already know MATLAB, it is very easy to request a one‐month trial license from mathworks.com and use its many free online tutorials to learn the language. One great advantage of MATLAB over R or other open‐source languages is that there is excellent customer support: If you have a question, just email or call the staff at Mathworks. (Often, someone with a PhD will answer your questions.)

I have taught many of these topics to both retail and institutional traders at my biannual workshops in London, as well as online (www.epchan.com). In order to facilitate lecturers who would like to use this as a textbook for a special topics course on Algorithmic Trading, I have included many exercises at the end of most chapters. Some of these exercises should be treated as suggestions for open‐ended projects; there are no ready‐made answers.

Readers will also find all of the software and some data used in the examples on epchan.com/book3. The userid and password are embedded in Box 1.1. But unlike my previous books, some of the data involved in the example strategies are under strict licensing restrictions and therefore are unavailable for free download from my website. Readers are invited to purchase or rent them from their original sources, all of which are described in Chapter 1.

I have benefited from tips, ideas, and help from many people in putting the content together. An incomplete list would include:

• Stephen Aikin, a renowned author (Aikin, 2012) and lecturer, who helped me understand implied quotes due to calendar spreads in the futures markets (Chapter 6).
• David Don and Joseph Signorelli of Lime Brokerage, who corrected some of my misunderstanding of the market microstructure (Chapter 6).
• Jonathan Shore, infinitely knowledgeable about bitcoins, who helped compile some order book data in that market and shared that with me (Chapter 7).
• Dr. Roger Hunter, CTO at our firm, QTS Capital Management, who reviewed my manuscript and who never failed to find software bugs in my codes.
• The team at Interactive Brokers (especially Joanne, Ragini, Mike, Greg, Ian, and Ralph) whose infinite patience with my questions about all issues related to trading are much appreciated.

I would like to thank Professor Thomas Miller of Northwestern University for hiring me to teach the Risk Analytics course at the Master of Science in Predictive Analytics program. In the same vein, I would also like to thank Matthew Clements and Jim Biss at Global Markets Training for organizing the London workshops for me over the years. Quite a few nuggets of knowledge in this book come out of materials or discussions from these courses and workshops.

Trading and research have been made a lot more interesting and enjoyable because I was able to work closely with our team at QTS, who contributed to research, ideas, and general knowledge, some of which find their way into this book. Among them, Roger, of course, without whom there wouldn't be QTS, but also Yang, Marcin, Sam, and last but not least, Ray.

Of course, none of my books would come into existence without the support of Wiley, especially my long‐time editor Bill Falloon, development editor Julie Kerr, production editor Caroline Maria, and copy editor Cheryl Ferguson (from whom no missing “end” to a “for”‐loop can escape). It was truly a great pleasure to work with them, and their enthusiasm and professionalism are greatly appreciated.