Optimization of Trading Rules for the Spanish Stock Market by Genetic Programming

  • Sergio Luengo
  • Stephan Winkler
  • David F. Barrero
  • Bonifacio CastañoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9101)


This paper deals with the development of a method for generating input and output signals in the Spanish stock market. It is based on the application of set of simple trading rules optimized by genetic programming. To this aim we use the HeuristicLab software. To evaluate the performance of our method we make a comparison with other traditional methods such as Buy & Hold and Simple Moving Averages Crossover. We study three different market scenarios: bull market, bear market and sideways market. Empirical test series show that market global behavior has a great influence on the results of each method and that strategies based on genetic programming perform best in the sideways market.


Stock exchange markets Genetic programming Optimization Trading rules Market tendencies 


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  1. 1.
    Bodas-Sagi, D.J., et al.: A parallel evolutionary algorithm for technical market indicators optimization. Nat. Comp. (2012)Google Scholar
  2. 2.
    Brabazon, A., et al.: An Introduction to Evolutionary Computation in Finance. IEEE Computational Intelligence Magazine 3, 42–55 (2008)CrossRefGoogle Scholar
  3. 3.
    Goldberg, D.E., Kalyanmoy, D.: A Comparative Analysis of Selection Schemes Used in Genetic Algorithms. Foundations of Genetic Algorithms, pp. 69–93 (1991)Google Scholar
  4. 4.
    Kaboudan, M.: GP forecasts of stock prices for profitable trading. In: Evolutionary Computation in Economics and Finance, Heidelberg, pp 359–382 (2002)Google Scholar
  5. 5.
    Kirkpatrick, C.D., Dahlquist, J.: Technical Analysis: The Complete Resource for Financial Market Technicians. Financial Times Press (2006)Google Scholar
  6. 6.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT press (1992)Google Scholar
  7. 7.
    Majeed, H., Ryan, C.: Using context-aware crossover to improve the performance of GP permission and/or a fee. In: Proc. GECCO 2006, pp. 847–854 (2006)Google Scholar
  8. 8.
    Mallick, D., Lee, V.C., Ong, Y.S.: An empirical study of genetic programming generated trading rules in computerized stock trading service system. In: Procc. Int. Conf. on Serv. Syst. and Serv. Man., pp. 1–6 (2008)Google Scholar
  9. 9.
    Montana, D.J.: Strongly Typed Genetic Programming. Evo. Comp. 3(2), 199–230 (1995)CrossRefGoogle Scholar
  10. 10.
    Potvina, J.-Y., Sorianoa, P., Vallee, M.: Generating trading rules on the stock markets with genetic programming. Comp. & Oper. Res. 31, 1033–1047 (2004)CrossRefGoogle Scholar
  11. 11.
    Ryan, J.A., et al.: Package quantmod: Quantitative Financial Modeling Framework (2015).
  12. 12.
    Summers, B., et al.: Back to the future: an empirical investigation into the validity of stock index models over time. Appl. Fina. Econ. 14, 209–214 (2004)CrossRefGoogle Scholar
  13. 13.
    Wagner, S., et al.: Architecture and design of the HeuristicLab optimization environment. In: Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, pp. 197–261. Springer (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sergio Luengo
    • 1
  • Stephan Winkler
    • 2
  • David F. Barrero
    • 3
  • Bonifacio Castaño
    • 1
    Email author
  1. 1.Department of Physics and MathematicsUniversity of AlcaláMadridSpain
  2. 2.Bioinformatics Research GroupUniversity of Applied Sciences Upper AustriaHagenbergAustria
  3. 3.Department of Computer ScienceUniversity of AlcaláMadridSpain

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