Abstract
The use of mechanical trading systems allows managing a huge amount of data related to the factors affecting investment performance (macroeconomic variables, company information, industrial indicators, market variables, etc.) while avoiding the psychological reactions of traders when they invest in financial markets. When trading is executed in an intra-daily frequency instead a daily frequency, mechanical trading systems needs to be supported by very powerful engines since the amount of data to deal with grow while the response time required to support trades gets shorter. Numerous studies document the use of genetic algorithms (GAs) as the engine driving mechanical trading systems. The empirical insights provided in this paper demonstrate that the combine use of GA together with a GPU-CPU architecture speeds up enormously the power and search capacity of the GA for this kind of financial applications. Moreover, the parallelization allows us to implement and test previous GA approximations. Regarding the investment results, we can report 870% of profit for the S&P 500 companies in a 10-year time period (1996–2006), when the average profit of the S&P 500 in the same period was 273%.
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