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A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm

Author

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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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    1. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    2. Reitz, Stefan, 2006. "On the predictive content of technical analysis," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 121-137, August.
    3. Olson, Dennis, 2004. "Have trading rule profits in the currency markets declined over time?," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 85-105, January.
    4. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
    5. Chih-Ming Hsu, 2014. "An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 2645-2664, December.
    6. Neely, C. J. & Weller, P. A., 2003. "Intraday technical trading in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 22(2), pages 223-237, April.
    7. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    8. Shyh-Wei Chen, 2010. "Testing the hypothesis of market efficiency in the Taiwan-US forward exchange market since 1990," Applied Economics, Taylor & Francis Journals, vol. 42(1), pages 121-132.
    9. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    10. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    11. Ivelina Pavlova & A. M. Parhizgari, 2011. "In search of momentum profits: are they illusory?," Applied Financial Economics, Taylor & Francis Journals, vol. 21(21), pages 1617-1639.
    12. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    13. Armin Shmilovici & Yoav Kahiri & Irad Ben-Gal & Shmuel Hauser, 2009. "Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 33(2), pages 131-154, March.
    14. Neely, Christopher J. & Weller, Paul A. & Ulrich, Joshua M., 2009. "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 467-488, April.
    15. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
    16. Kuang, P. & Schröder, M. & Wang, Q., 2014. "Illusory profitability of technical analysis in emerging foreign exchange markets," International Journal of Forecasting, Elsevier, vol. 30(2), pages 192-205.
    17. Anthony Brabazon & Michael O’Neill, 2004. "Evolving technical trading rules for spot foreign-exchange markets using grammatical evolution," Computational Management Science, Springer, vol. 1(3), pages 311-327, October.
    18. Fama, Eugene F, et al, 1969. "The Adjustment of Stock Prices to New Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(1), pages 1-21, February.
    19. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    20. Paolo Falbo & Cristian Pelizzari, 2011. "Stable classes of technical trading rules," Applied Economics, Taylor & Francis Journals, vol. 43(14), pages 1769-1785.
    21. Chen, Cheng-Wei & Huang, Chin-Sheng & Lai, Hung-Wei, 2009. "The impact of data snooping on the testing of technical analysis: An empirical study of Asian stock markets," Journal of Asian Economics, Elsevier, vol. 20(5), pages 580-591, September.
    22. N.C. Suganya & G.A. Pai, 2012. "Wavelet evolutionary network for complex-constrained portfolio rebalancing," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(7), pages 1367-1385.
    23. Yu, Hao & Nartea, Gilbert V. & Gan, Christopher & Yao, Lee J., 2013. "Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 356-371.
    24. Pedro Godinho, 2012. "Can abnormal returns be earned on bandwidth-bounded currencies? Evidence from a genetic algorithm," Economic Issues Journal Articles, Economic Issues, vol. 17(1), pages 1-26, March.
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    Cited by:

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    2. 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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