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A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm

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  • Luís Lobato Macedo

    (AT – Autoridade Tributária e Aduaneira, Sub-Direcção Geral de Cobrança (Revenue & Customs, Deputy Directorate-General for Tax Collection)
    University of Coimbra)

  • Pedro Godinho

    (University of Coimbra
    University of Coimbra)

  • Maria João Alves

    (Institute of System & Computer Engineering (INESC) at Coimbra Rua Antero de Quental
    University of Coimbra)

Abstract

Traditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets in order to ascertain the suitability of TA strategies and rules to achieve consistently superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators by applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets have interesting return figures before trading costs. The inclusion of spreads and commissions weakens returns substantially, suggesting that under a more realistic set of assumptions these markets could be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may be evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and offers the possibility of recovering latent data, thus avoiding premature convergence.

Suggested Citation

  • Luís Lobato Macedo & Pedro Godinho & Maria João Alves, 2020. "A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 349-381, January.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:1:d:10.1007_s10614-016-9641-9
    DOI: 10.1007/s10614-016-9641-9
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    2. Vinícius Ferraz & Thomas Pitz, 2024. "Analyzing the Impact of Strategic Behavior in an Evolutionary Learning Model Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 437-475, February.
    3. Jaime Alberto Gómez Vilchis & Federico Hernández Álvarez & Luis Ignacio Román de la Sancha, 2021. "Autómata Evolutivo (AE) para el mercado accionario usando Martingalas y un Algoritmo Genético," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(4), pages 1-22, Octubre -.

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