Machine learning in algorithmic trading strategy optimization - implementation and efficiency

Przemysław Ryś () and Robert Ślepaczuk
Additional contact information
Przemysław Ryś: Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw

No 2018-25, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The 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 a strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The methods operated on the in sample data, containing 20 years of daily prices between 1998 and 2017. 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 major hypothesis verified in this thesis is that machine learning methods select strategies with evaluation criterion near to the highest one, but in significantly lower execution time than the Exhaustive Search.

Keywords: machine learning; algorithm; trading; investment; automatization; strategy; optimization; differential evolutionary method; cross-validation; overfitting (search for similar items in EconPapers)
JEL-codes: C15 C4 C45 C61 G14 G17 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2018
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.wne.uw.edu.pl/index.php/download_file/4680/ First version, 2018 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2018-25

Access Statistics for this paper

More papers in Working Papers from Faculty of Economic Sciences, University of Warsaw Contact information at EDIRC.
Bibliographic data for series maintained by Marcin Bąba ().