abstract

Fitness function evaluation for MA trading strategies based on genetic algorithms

Authors:

José Pinto,

Rui Ferreira Neves,

Nuno HortaAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 819 - 820

https://doi.org/10.1145/2001858.2002105

Published: 12 July 2011 Publication History

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Abstract

This paper presents a new approach to optimize an investment strategy based on moving averages (MA). The proposed approach optimizes the entry and exit points, for both long and short positions, using a genetic algorithm (GA) kernel. This approach outperforms B&H strategy and explores alternative functions to the classical absolute return fitness function. The approach is demonstrated for major market indexes, such as, S&P 500, FTSE100, DAX30, NIKKEI225.

References

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Gorgulho, A., Neves, R. and Horta N., Using GAs to Balance Technical Indicators on Stock Picking for Financial Portfolio Composition in.Proceedings of the GECCO Montreal, Canada, 2009, 2041--2046.

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Pablo Fernández-Blanco, Diego J. Bodas-Sagi, Francisco J. Soltero, J. Ignacio Hidalgo, Technical market indicators optimization using evolutionary algorithms, In Proceedings of the GECCO, Atlanta, Georgia. USA, 2008, 1951--1957.

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Yan, W. and Clack, C., Evolving Robust GP Solutions for Hedge Fund Stock Selection in Emerging Markets, In Proceedings of the GECCO, London, UK; 2007, 2233--2242.

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Cited By

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Leitão JNeves RHorta NLeitão JNeves RHorta N(2017)Related WorkIdentifying Patterns in Financial Markets10.1007/978-3-319-70160-8_2(3-27)Online publication date: 28-Dec-2017
https://doi.org/10.1007/978-3-319-70160-8_2
Leitão JNeves RHorta N(2016)Combining rules between PIPs and SAX to identify patterns in financial marketsExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.08.03265:C(242-254)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.08.032
Pinto JNeves RHorta N(2015)Boosting Trading Strategies performance using VIX indicator together with a dual-objective Evolutionary Computation optimizerExpert Systems with Applications10.1016/j.eswa.2015.04.05642:19(6699-6716)Online publication date: Nov-2015
https://doi.org/10.1016/j.eswa.2015.04.056
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Index Terms

Fitness function evaluation for MA trading strategies based on genetic algorithms
1. Computing methodologies
  1. Artificial intelligence

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Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Leitão JNeves RHorta NLeitão JNeves RHorta N(2017)Related WorkIdentifying Patterns in Financial Markets10.1007/978-3-319-70160-8_2(3-27)Online publication date: 28-Dec-2017
https://doi.org/10.1007/978-3-319-70160-8_2
Leitão JNeves RHorta N(2016)Combining rules between PIPs and SAX to identify patterns in financial marketsExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.08.03265:C(242-254)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.08.032
Pinto JNeves RHorta N(2015)Boosting Trading Strategies performance using VIX indicator together with a dual-objective Evolutionary Computation optimizerExpert Systems with Applications10.1016/j.eswa.2015.04.05642:19(6699-6716)Online publication date: Nov-2015
https://doi.org/10.1016/j.eswa.2015.04.056
Canelas ANeves RHorta N(2013)A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniquesExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.09.00240:5(1579-1590)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.eswa.2012.09.002
Canelas ANeves RHorta NMoore J(2012)A new SAX-GA methodology applied to investment strategies optimizationProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330310(1055-1062)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330310
Canelas ANeves RHorta NCanelas ANeves RHorta N(2012)Market Analysis Background and Related WorkInvestment Strategies Optimization based on a SAX-GA Methodology10.1007/978-3-642-33110-7_2(5-35)Online publication date: 27-Sep-2012
https://doi.org/10.1007/978-3-642-33110-7_2

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A trading method in FX using evolutionary algorithms: extensions based on reverse trend and settlement timing

Interday foreign exchange trading using linear genetic programming

Co‐evolved genetic programs for stock market trading

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