Parallel genetic algorithms for stock market trading rules
Procedia Computer Science, ISSN: 1877-0509, Vol: 9, Page: 1306-1313
2012
- 19Citations
- 80Captures
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Article Description
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).
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1877050912002645; http://dx.doi.org/10.1016/j.procs.2012.04.143; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84886270889&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1877050912002645; https://api.elsevier.com/content/article/PII:S1877050912002645?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S1877050912002645?httpAccept=text/plain; https://dx.doi.org/10.1016/j.procs.2012.04.143
Elsevier BV
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