Parallel genetic algorithms for stock market trading rules

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• 作者：Janko Straß ; burg^a ; ^{j.strassburg@reading.ac.uk} ; Christian Gonzà ; lez-Martel^b ; ^{cgonzalez@dmc.ulpgc.es} ; Vassil Alexandrov^c ; ^{vassil.alexandrov@bsc.es}
• 关键词：Genetic Algorithms ; Stock Market ; Parallel ; Technical Trading Rules
• 刊名：Procedia Computer Science
• 出版年：2012
• 期刊代码：pc74_18770509
• 类别：cp
• 出版时间：2012
• 卷：9
• 期：Complete
• 页码：1306-1313
• 文件大小：139 K

摘要

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 鈥済ood enough鈥?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).