Abstract
This paper proposes an approach to analysis of usage patterns of trading rules in stock market trading strategies. Analyzed strategies generate trading decisions based on signals produced by trading rules. Weighted sets of trading rules are used with parameters optimized using evolutionary algorithms. A novel approach to trading rule pattern discovery, inspired by association rule mining methods, is proposed. In the experiments, patterns consisting of up to 5 trading rules were discovered which appear in no less than 50% of trading experts optimized by evolutonary algorithm.
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Michalak, K., Filipiak, P., Lipinski, P. (2013). Usage Patterns of Trading Rules in Stock Market Trading Strategies Optimized with Evolutionary Methods. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_24
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DOI: https://doi.org/10.1007/978-3-642-37192-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37191-2
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