On balance expert systems trump human experts, hence the drive to make trading more systematic and mechanical. The problem is that Building Winning Algorithmic Trading Systems, the title of Kevin J. Davey’s new book (Wiley, 2014), can be tough. Davey recounts his sometimes gut-wrenching journey “from data mining to Monte Carlo simulation to live trading” and provides traders with useful information that will help them avoid his mistakes.
The author joins a rather small fraternity of systems developers who have shared their thoughts, for better or worse, with the reading public. I think here—and this list is in no way meant to be exhaustive—of Howard Bandy (Quantitative Trading Systems), Tushar Chande (Beyond Technical Analysis), Urban Jaekle and Emilio Tomasini (Trading Systems), Perry Kaufman (Trading Systems and Methods), Robert Pardo (The Evaluation and Optimization of Trading Strategies), and Thomas Stridsman (Trading Systems That Work).
The strength of Davey’s book is that it covers the entire process of designing, developing, testing, trading, and monitoring a system. It also includes Easy Language code for three sample strategies, and on the password-protected companion website (the password is given in the book) there are five helpful spreadsheets.
I was particularly taken with Davey’s methods for reviewing the performance of his systems. One “simple,” short-term method assumes that trade results have a normal distribution, which admittedly seldom happens. “To alleviate this concern, we can simply take the Monte Carlo results from numerous runs and use percentiles based on them. This will provide a more accurate representation of the expected bounds of the trading system.” (p. 214) But, whichever approach and whatever parameters the trader uses (the author prefers the Monte Carlo version), the graph will look roughly like a probability cone. With the Monte Carlo version, a trader can include “boundary condition” effects, “like quitting after a certain percentage drawdown.” (p. 216) “For example, if the real-time performance of your strategy falls below the lower 10 percent line, it could mean that your system is no longer working. After all, the odds were 90 percent that your strategy should be working better than this.” (p. 215)
Davey has a spreadsheet to assess the monthly performance of a given system. On it he includes, among other things, the expected monthly and annual profit, the maximum expected intraday drawdown, and the efficiency of the real annualized returns vs. the expected returns as well as the drawdown efficiency. That is, if the system projects an annual gain of $12,268 but returns in actuality $8,318 it has a return efficiency of 68%. If the projected worst drawdown is $1,700 and the actual worst monthly drawdown is $1,028, the drawdown efficiency is 40%.
For traders seeking to develop profitable systems, Davey’s book is a solid, clearly written guide. It not only explains the steps necessary to take a system from idea to real time but also points out pitfalls the developer should be careful to avoid—among them, a system that performs too well. In fact, as the author notes, “the better a trading system tests historically, the less likely it is to perform that well in the future.” (p. 43)
Even more discretionary traders can learn from Davey’s book since not only do they presumably trade with a plan of some sort or another, but their performance can and should always be measured.