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Algorithmic trading and Direct Market Access (DMA) are important tools helping both buy and sell-side traders to achieve best execution.

This book starts from the ground up to provide detailed explanations of both these techniques:

• An introduction to the different types of execution is followed by a review of market microstructure theory. Throughout the book examples from empirical studies bridge the gap between the theory and practice of trading.
• Orders are the fundamental building blocks for any strategy. Market, limit, stop, hidden, iceberg, peg, routed and immediate-or-cancel orders are all described with illustrated examples.
• Trading algorithms are explained and compared using charts to show potential trading patterns. TWAP, VWAP, Percent of Volume, Minimal Impact, Implementation Shortfall, Adaptive Shortfall, Market On Close and Pairs trading algorithms are all covered, together with common variations.
• Transaction costs can have a significant effect on investment returns. An in-depth example shows how these may be broken down into constituents such as market impact, timing risk, spread and opportunity cost and other fees.
• Coverage includes all the major asset classes, from equities to fixed income, foreign exchange and derivatives. Detailed overviews for each of the world’s major markets are provided in the appendices.
• Order placement and execution tactics are covered in more detail, as well as potential enhancements (such as short-term forecasts), for those interested in the specifics of implementing these strategies.
• Cutting edge applications such as portfolio and multi-asset trading are also considered, as are handling news and data mining/artificial intelligence.

There is also a website for this book at www.algo-dma.com

Review:

Terminology time: when the average amateur thinks of “Algorithmic trading,” he thinks of vast machine intelligences duking it out in microseconds using exotic signal processing techniques. Well, in the business, “algorithmic trading” generally refers to the process of finding liquidity for an instrument using a computer, generally done by a buy side trader. This may seem needlessly pedantic, but it’s important, as this is an actual job description, and this is what the book is all about. It also relates to 2009′s favorite whipping boy, “High Frequency Trading,” and could be considered the premier book on this subject -it’s the best one I’ve yet read anyway. In addition to describing the other end of a HF trade done intelligently, it describes how various arbitrageurs and other prop traders make their money (in ch 13 in particular). The appendices are also excellent, and there is a useful key to abbreviations and acronyms: something sorely missed in many other books.

The book is a model of clarity and trading didactics; I have read no better description of this sort of thing, anywhere. While I’m not qualified to say so, as I don’t actually do such things for a living, I suspect it’s extremely complete and accurate introduction to the subject. In addition to the didactics, it contains plenty of folk wisdom, practical advice, obscure information and good old horse sense.

In detail: For part I, ch 1 gives a basic overview of the subject, including necessary definitions (aka DMA versus algo trading versus …). Ch 2 touches on market microstructure; this is excellent, both for the rank amateur, and the professional looking to be grounded in a clear exposition. Ch 3 a description of the different types of markets, asset classes, dark pools, dealer markets and etc. This is all basic stuff; the nuts and bolts of what we’re talking about. On to part II; ch 4 gives a detailed description of the different order types one can use in different markets. Ch 5 gives the basic kinds of trading algorithms; VWAP, implementation shortfall and all that. Ch 6 is on the process of modeling transaction costs; this chapter doesn’t give any algorithms for doing so; it is more of a framework for thinking about the problem from the point of view of the algorithmic trader. I originally thought ch 7 was one of the weaker chapters, though upon reflection it may be one of the most useful ones for assessing market behavior; I was focusing on the classical use of the word “optimal.” Section III, chapter 8 order placement is a sort of review of market microstructure models of price formation, and a strategic break down of the way a trader thinks about the problem of order placement. That and the sections on dark liquidity: gold. Ch 9 is on tactics; also invaluable stuff. How does a trader fake out other traders, look for hidden liquidity, update the limit book to minimize signaling? Ch 10 can be seen as a collection of ways to use forecasting techniques in your trading algorithms. Lots of practical information mixed in here about “forecastibles” that everyone knows about (dividends, witching days, etc). It’s not always obvious how to incorporate known future events into a trading strategy or algorithm: this chapter is very helpful. I’d have liked to see it done in some kind of Bayesian framework, but whatever; this is really practical, useful stuff. Ch 11, infrastructure; this goes over things like FIX (the protocol for talking to the broker), some graphs as to how the actual order process works, ideas on latency, testing, market compliance and so on. I’d have liked a little more information on things like tick databases, trading platforms and trade resolution infrastructure, but maybe such information would be out of date as soon as he wrote it. Anyway, mentioning the words and some problems with typical such software might be useful to the tyro. Part IV, Ch 12 is on portfolio trading. There is a decent introduction to classical portfolio theory, and some good ideas on minimizing portfolio risk using the author’s “marginal contribution to risk” metric. I’d have liked some more information here, but perhaps this is an appropriate chapter for a book more or less on Algo trading and DMA, rather than prop trading. Ch 13 is on various multi-asset trading strategies; roughly speaking, forms of statistical arbitrage and prop trading. It’s sort of an oddball chapter, as this isn’t the primary subject matter of the book, but it’s a topical subject, and I’m glad it’s there. Ch 14 is on trading the news; also a very interesting topic, and subject of ongoing research. The author gives a lot of practical information here which could be useful to the experimenter. Chapter 15 on machine learning is probably the weakest of the book, though I can find no factual faults with it. It is a reasonable introduction to machine learning and data mining techniques. Personally, I’d have lost much of the stuff on ANN’s, and added a bit on reinforcement learning, and perhaps a section on the block bootstrap (one of my favorite hobby horses) used for testing for overfitting. One idea I found really interesting was the notion of using artificial stock markets to test ideas. I’ve fiddled with these, though I never thought of using them to test ideas! That’s a damn good idea. Honestly, I think most of the machine learning papers out there are crap, and their appearance in books like this are more or less smokescreens. Stuff like econometrics and particle filters: way more useful. Don’t tell anyone I told you so. The 70 pages of appendices, well, they’re all super helpful for figuring out how the actual markets work in detail. No doubt some of the details are already out of date, but the over all structure: priceless. A real map of world markets.

Don’t know why he wrote it, but I’m glad he did. I’d have paid twice the cover price for the book. I’ve actually physically worn the thing out (it doesn’t do well on beaches), and will probably order another copy.