rss

Dormeier, Investing with Volume Analysis

In addition to his “real” job managing money, Buff Pelz Dormeier develops technical indicators. He shares some of the fruits of his—and his noteworthy predecessors’—labor in Investing with Volume Analysis: Identify, Follow, and Profit from Trends (FT Press, 2011).

When I started reading this book I suspected that it would be like so many others: long on generalities and short on actionable ideas. The first hundred pages or so do indeed deal with general relationships between price and volume, and some of the material is familiar. But even the familiar material is often presented in an unusual way. Here’s one example.

Newton’s second law of motion, reinterpreted to apply to financial markets, analyzes “how much volume (force) is required to move a security (the object) a given distance (price change) at a given speed (acceleration/momentum). … Richard Wyckoff referred to this principle as the law of effort versus result, which asserts that the effort must be in proportion to the results.” (p. 47) As a corollary of this law, “if more volume (force) is required to produce less price change (acceleration), then the stock is becoming overly bought or sold.” (p. 85)

In apparent contradiction to Wyckoff’s law of effort is the rule of trend volume, according to which “more volume substantiates a stronger trend.” (p. 85) Can these two principles be reconciled? Dormeier suggests that they can, once we bring the notions of strong hands and weak hands into the equation. His discussion is too detailed to summarize here, but it is premised on how strong hands and weak hands play the game. As he writes, “Strong hands buy out of an expectation of capital appreciation. Weak hands buy out of greed and the fear of missing out on an opportunity. Weak hands sell from the fear of losing capital. Strong hands sell to reinvest in better opportunities (which does not have to be other equities).” (p. 87)

Dormeier really hits his stride when he turns “general volume principles into indicators with numerical values.” (p. 113) These indicators have a dual mandate—to lead price and to confirm price. But they don’t all work the same way; they are “tools, each of which is designed to explain a distinct piece of the volume puzzle.” (p. 117) (more…)

Zubulake and Lee, The High Frequency Game Changer

The High Frequency Game Changer: How Automated Trading Strategies Have Revolutionized the Markets by Paul Zubulake and Sang Lee (Wiley, 2011) is not an engaging book. It was definitely not written for the retail investor. Instead, it reads like a series of mini-reports from a consulting firm. It should therefore come as no surprise that the co-authors are a senior analyst and the managing partner at Aite Group, “an independent research and advisory firm focused on business, technology, and regulatory issues and their impact on the financial services industry.”

Rather than write a standard review, I’ll pick out two data points from the book that I think might be of general interest.

The number of electronic trade messages quadrupled between December 2006 and 2010. “If U.S. equities continue their pace, Aite Group expects message volumes to average 1.2 billion messages per day by 2011. The market already saw peak days approaching this number in late 2008. … Options pricing is exponentially worse than equities market data volumes. Current … OPRA data peaks exceed 1 million messages per second. Aite Group expects OPRA will generate peaks exceeding 2.2 million messages per second by the end of 2010.” (p. 47) I don’t know whether this projection came to pass, but the infrastructure demands are evident. No wonder some brokers charge for cancelled options orders.

I wrote about the importance of high performance databases in an earlier review. Zubulake and Lee confirm this: “Speed is essential for firms running strategies that feature both real-time and historical data. Aite Group estimates that 90% of quantitative trading firms currently maintain or are developing at least one trading strategy that requires playing back historical data in conjunction with real-time data.” (p. 113) Sure beats trying to keep all that history in your head!

Go to top