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Before applying the scientific approach to the study of the markets, a number of things must be considered. First, a universe of reliable market data on which to perform back-testing and statistical analyses must be available. Since this book is focused on commodities trading, the market data used as the basis for our universe on an end-of-day time frame will be a subset of the diverse set of markets supplied by Pinnacle Data Corporation: these include the agriculturals, metals, energy resources, bonds, currencies, and market indices. Intraday time-frame trading is not addressed in this book, although it is one of our primary areas of interest that may be pursued in a subsequent volume. In addition to standard pricing data, explorations into the effects of various exogenous factors on the markets sometimes require unusual data. For example, data on sunspot activity (solar radiation may influence a number of markets, especially agricultural ones) was obtained from the Royal Observatory of Belgium. Not only is a universe of data needed, but it is necessary to simulate one or more trading accounts to perform back-testing. Such a task requires the use of a trading simulator, a software package that allows simulated trading accounts to be created and manipulated on a computer. The C+ + Trading Simulator from Scientific Consultant Services is the one used most extensively in this book because it was designed to handle portfolio simulations and is familiar to the authors. Other programs, like Omega Research s TradeStation or SystemWriter Plus, also offer basic trading simulation and system testing, as well as assorted charting capabilities. To satisfy the broadest range of readership, we occasionally employ these products, and even Microsoft s Excel spreadsheet, in our analyses. Another important consideration is the optimization of model parameters. When running tests, it is often necessary to adjust the parameters of some component (e.g., an entry model, an exit model, or some piece thereof) to discover the best set of parameters and/or to see how the behavior of the model changes as its parameters change. Several kinds of model parameter optimizations may be con-
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xxi ducted. In manual optimization, the user of the simulator specifies a parameter that is to be manipulated and the range through which that parameter is to be stepped; the user may wish to simultaneously manipulate two or more parameters in this manner, generating output in the form of a table that shows how the parameters interact to affect the outcome. Another method is brute force optimization, which comes in several varieties: The most common form is stepping every parameter through every possible value. If there are many parameters, each having many possible values, running this kind of optimization may take years. Brute force optimization can, however, be a workable approach if the number of parameters, and values through which they must be stepped, is small. Other forms of brute force optimization are not as complete, or as likely to find the global optimum, but can be run much more quickly. Finally, for heavy-duty optimization (and, if naively applied, truly impressive curve-fitting) there are genetic algorithms. An appropriate genetic algorithm (GA) can quickly tind a good solution, if not a global optimum, even when large numbers of parameters are involved, each having large numbers of values through which it must be stepped. A genetic optimizer is an important tool in the arsenal of any trading system developer, but it must be used cautiously, with an ever-present eye to the danger of curve-fitting. In the investigations presented in this book, the statistical assessment techniques, out-ofsample tests, and such other aspects of the analyses as the focus on entire portfolios provide protection against the curve-fitting demon, regardless of the optimization method employed. Jeffrey Owen Katz, Ph.D., and Donna E McCormick
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