Parallel genetic algorithms for stock market trading rules

Abstract

Finding the best trading rules is a well-known problem in the field of technical analysis of stock markets. One option is to employ genetic algorithms, as they offer valuable characteristics towards retrieving a "good enough" solution in a timely manner. However, depending on the problem size, their application might not be a viable option as the iterative search through a multitude of possible solutions does take considerable time. Even more so if a variety of stocks are to be analysed.In this paper we concentrate on the enhancement of a previously published genetic algorithm for the optimisation of technical trading rules, using example data from the Madrid Stock Exchange General Index (IGBM).