Senin, 20 Juni 2011

TOOLS AND MATERIALS NEEDED FOR THE SCIENTIFIC APPROACH

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

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.

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