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

Ryś Przemysław () and Robert Ślepaczuk
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Ryś Przemysław: Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw and Labyrinth HF project, Warsaw, Poland

Central European Economic Journal, 2018, vol. 5, issue 52, 206-229

Abstract: The main aim of this paper was to formulate and analyse 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 case of 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 was 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).

Keywords: Algorithmic trading; investment strategy; machine learning; optimization; investment strategy; differential evolutionary method; cross-validation; overfitting (search for similar items in EconPapers)
JEL-codes: C61 G11 G17 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ceuecj:v:5:y:2018:i:52:p:206-229:n:17

DOI: 10.1515/ceej-2018-0021

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