<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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).</p></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).

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

Central European Economic Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The main aim of this paper was to formulate and analyse the machine learning methods, fitted to the strategy parameters optimization specificity. The most...

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

Publicado en línea: 09 ago 2019

Páginas: 206 - 229

DOI: https://doi.org/10.1515/ceej-2018-0021

Palabras clave
Algorithmic trading, investment strategy, machine learning, optimization, investment strategy, differential evolutionary method, cross-validation, overfitting

© 2018 P. Ryś, R. Ślepaczuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publicado en línea: 09 ago 2019

Páginas: 206 - 229

DOI: https://doi.org/10.1515/ceej-2018-0021

Palabras claveAlgorithmic trading, investment strategy, machine learning, optimization, investment strategy, differential evolutionary method, cross-validation, overfitting

© 2018 P. Ryś, R. Ślepaczuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
Algorithmic trading, investment strategy, machine learning, optimization, investment strategy, differential evolutionary method, cross-validation, overfitting