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Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

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dc.abstract.enThe main aim of this paper was to formulate and analyze the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of strategy quality. The efficiency of methods was compared for three different pairs of assets in the case of the moving averages crossover system. The problem was presented for three sets of two assets’ portfolios. In the first case, a strategy was trading on the SPX and DAX index futures; in the second, on the AAPL and MSFT stocks; and finally, in the third case, on the HGF and CBF commodities futures. The methods operated on the in-sample data, containing 16 years of daily prices between 1998 and 2013, and were validated on the out-of-sample period between 2014 and 2017. The major hypothesis verified in this paper is that machine learning methods select strategies with evaluation criterion near the highest one, but in significantly lower execution time than the brute force method (Exhaustive Search).
dc.affiliationUniwersytet Warszawski
dc.contributor.authorRyś, Przemysław
dc.contributor.authorŚlepaczuk, Robert
dc.date.accessioned2024-01-25T05:30:15Z
dc.date.available2024-01-25T05:30:15Z
dc.date.issued2019
dc.description.accesstimeAT_PUBLICATION
dc.description.financeNie dotyczy
dc.description.number52
dc.description.versionFINAL_PUBLISHED
dc.description.volume5
dc.identifier.doi10.1515/CEEJ-2018-0021
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/111634
dc.identifier.weblinkhttp://dx.doi.org/10.1515/ceej-2018-0021
dc.languageeng
dc.pbn.affiliationeconomics and finance
dc.relation.ispartofCentral European Economic Journal
dc.relation.pages206-229
dc.rightsCC-BY
dc.sciencecloudnosend
dc.subject.enAlgorithmic trading
dc.subject.enInvestment strategy
dc.subject.enMachine learning
dc.subject.enOptimization
dc.subject.enDifferential evolutionary method
dc.subject.enCross-validation
dc.subject.enOverfitting
dc.titleMachine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency
dc.typeJournalArticle
dspace.entity.typePublication