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[1] Sypros Skouras has compiled an exhaustive bibliography of academic studies through 1998. It is available at http://www.santefe.edu/~spyros/tabiblio.htm
[2] Although these indicators can be based only on closing prices, high and low prices are most commonly used.
the lowest (highest) value in the last k days. Table II lists the first 18 technical trading indicators in Murphy (1999). Neely et al. (1997) and Allen and Karjalainen (1999) used identical operator sets, and the indicators feasible in those studies are listed in the first column. The second column notes the indicators available for the estimation in this study. The set of rules that can be constructed using the operator set described in this article encompasses most common technical rules, and is significantly expanded from the set used by Neely et al. and Allen and Karjalainen.
The evolutionary process used to generate optimal trading rules is the defining characteristic of genetic programming. To start the process, a population of rules is randomly generated. Each of these rules is evaluated for its “fitness”—such as high profitability or low risk. With a probability proportional to each rule’s fitness rank in the population, rules are chosen to participate in genetic operations, such as recombination, and the resulting rules constitute the next generation of rules. This three-step
[3] Using these parameters, each commodity/year required approximately 60 CPU minutes on a 2.8 GHz Intel Xeon Processor for the 20 optimizations.
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