Skip to main content
SpringerLink
Log in

A parallel evolutionary algorithm for technical market indicators optimization

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 51(2):245–271

    Article  Google Scholar 

  • Álvarez González A (2005) Stock market analysis for speculative purposes. A modern approach. Limusa, Mexico

    Google Scholar 

  • Andrés Toro B, de Girón-Sierra JM, López Orozco JA, Fernández Blanco P (1999) A genetic optimization method for dynamic processes. In: Proceedings of the 14th World Congress IFAC. Pergamon Ed., Beijing, China, pp 373–378

  • Andreu CJ, Ceballos HD (2004) Application of Fuzzy Delphi method to the prediction market. In: XI congress of international association for fuzzy-set management and economy. University Mediterranea of Reggio Calabria

  • Arifovic J (2000) Evolutionary algorithms in macroeconomic models. Macroecon Dyn 4(3):373–414

    Article  MATH  Google Scholar 

  • Bessembinder H, Chan K (1995) The profitability of technical trading rules in the Asian stock markets. Pac-Basic Finance J, Elsevier 3(2–3):257–284

  • Bevilacqua V, Pacelli V, Saladino S (2012) A novel multi objective genetic algorithm for the portfolio optimization. Advanced intelligent computing. Lecture Notes in Computer Science 6838. Springer, Berlin, pp 186–193

  • Bodas-Sagi DJ, Fernández P, Hidalgo JI, Soltero FJ, Risco-Martín JL (2009) Multi-objective optimization of technical market indicators. In: Proceedings of the 11th annual conference on genetic and evolutionary computation. GECCO’09, pp 1999–2004

  • Bodas-Sagi DJ, Soltero FJ, Hidalgo JI, Fernandez P, Fernandez F (2012) A technique for the optimization of the parameters of technical indicators with multi-objective evolutionary algorithms. In: 2012 IEEE congress on evolutionary computation (CEC), pp 1–8

  • Brabazon A, Dempsey I, ONeill M (2008) An introduction to evolutionary computation in finance. IEEE Comput Intell Mag 3(4):42–55

    Article  Google Scholar 

  • Brock W, Lakonishhock J, LeBaron B (1992) Simple technical trading rules and the stochastic of stock returns. J Finance 47(5):1731–1764

    Google Scholar 

  • Cantú-Paz E (2000) Efficient and accurate parallel genetic algorithms. Kluwer Academic Publishers, Norwell

    MATH  Google Scholar 

  • Chen SH, Wang HS, Zhang BT (1999) Forecasting high-frequency financial time series with evolutionary neural trees: the case of Hang-Seng stock index strategies. In: Proceedings of IC-AI’99, pp 437–443

  • Coello C, Lamont GB, Van Delhhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, Berlin

    MATH  Google Scholar 

  • Dimson E, Paul M, Mike S (2000) The millennium book: a century of investment returns. London, ABN-Amro, London Business School

  • Fernández Rodríguez F, Sosvilla Rivero S, Andrada Félix J (1999) Technical analysis in madrid stock market. Moneda y Crédito 213:11–37

  • Fernández-Blanco P, Bodas-Sagi DJ, Soltero FJ, Hidalgo JI (2008) Technical market indicators optimization using evolutionary algorithms. In: Keijzer (ed) Proceedings of the genetic and evolutionary computation conference (GECCO’2008), pp 1851–1858

  • Fonseca CM, Fleming PJ (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithm-part I: unified formulation. IEEE Trans Syst Man Cybern Part A Syst Humans 28(1):3–18

    Google Scholar 

  • Goldberg DD (1999) The race, the hurdle, and the sweet spot: lessons from genetic algorithms for the automation of design innovation and creativity. Evol Des Comp 105–118

  • Graham B (2003) The intelligent investor. Collins Business, New York

    Google Scholar 

  • Graziano JP (2001) Statistical technical analysis. Key indicators and their application to ISR Statistic Technical Analysis. Investigación y Desarrollo. Bolsa de Comercio de Rosario (in Spanish)

  • Gregory-Allen RB, Shawky HA, Stangl J (2009) Quantitative vs. fundamental analysis in institutional money management: where’s the beef? J Investing 18(4):42–52

    Article  Google Scholar 

  • Hadavandi E, Ghanbari A, Abbasian-Nagneh S (2010) Developing an evolutionary neural network model for stock index forecasting. Springer, Berlin, pp 407–415

    Google Scholar 

  • Hassan R, Nath B (2005) Stock market forecasting using hidden markov model: a new approach. In: Proceedings of the 2005 5th international conference on intelligent systems design and applications (ISDA’05). IEEE Computer Society, pp 192–196

  • Hidalgo JI, Prieto M, Lanchares J, Tirado F, de Andrés B, Esteban S., Rivera D (1999) A method for model parameter identification using parallel genetic algorithms. Recent advances in parallel virtual machine and message passing interface. Lecture Notes in Computer Science 1697. Springer, Barcelona (Spain), pp 291–298

  • Kaboudan M (2002) GP forecasts of stock prices for profitable trading. In: Chen S (ed) Evolutionary computation in economics and finance. II. Physica, Heidelberg, pp 359–382

    Google Scholar 

  • Keber C (2002) Evolutionary computation in option pricing: determining implied volatilities based on American put options. In: Evolutionary computation in economics and finance.Physica, Heidelberg, pp 399–415

  • Kirkpatrick C, Dahlquist J (2010) Technical analysis: the complete resource for financial market technicians. Financial Times Press

  • Li R-J, Xiong Z-B (2005) Forecasting stock market with fuzzy neural networks. In: Proceedings of the fourth international conference on machine learning and cybernetics, vol 6, pp 3475–3479

  • Lo A, MacKinley Y (1990) Data snooping biases in test of financial asset pricing models. Rev Financ Stud 3(3):431–467

    Article  Google Scholar 

  • Lohpetch D, Corne D (2011) Multi-objective algorithms for financial trading: multi-objective out-trades single-objective. IEEE Congr Evol Comput 2011:192–199

  • Mantegna RN, Stanley HE (2000) An introduction to ecophysics. Correlations and Complexity in Finance. Cambridge University Press, Cambridge

    Google Scholar 

  • Mills T (1996) Technical analysis and the london stock exchange: testing trading rules using the FT30. Economic Research Paper 96/1, Department of Economics, Loughborough of Technology

  • Murphy JJ (1999) Technical analysis of financial markets. Prentice Hall Press, Upper Saddle River

    Google Scholar 

  • Oussaidène M, Chopard B, Pictet OV, Tomassini M (1997) Pararel Genetic Programming and its application to trading model induction. Parallel Comput 23(8):1183–1198

    Article  MATH  Google Scholar 

  • Parisi A, Parisi F, Díaz D (2006) Models of genetic algorithms and neural networks in the prediction of Asian stock market indexes. Cuadernos de Economía 43:251–284

    Google Scholar 

  • Peña D (2005) Time serie analysis. Alianza, Madrid

    Google Scholar 

  • Pereira R (2002) Forecasting ability but no profitability: an empirical evaluation of genetic algorithm-optimized technical trading rules. Evolutionary computation in economics and finance. Physica, Heidelberg, pp 273–286

    Google Scholar 

  • Pring M (1991) Technical analysis explained: the successful investor’s guide to spotting investment trends and turning points. MacGraw-Hill, New York

    Google Scholar 

  • Santini M, Tettamanzi A (2001) Genetic programming for financial time series prediction. In: Genetic programming, Proceedings of EuroGP’2001, LNCS 2038. Springer, Berlin, pp 361–370

  • Sitte R, Sitte J (2005) Neural network systems technology in the analysis if financial time series. Intelligent Knowledge-Based Systems. Springer, pp 1564–1615

  • Walpole Ronald E, Raymond H, Myers Sharon L (1998) Probability and statistics for engineers. Prentice-Hall Hispanoamericana, S.A. México

  • Yan W, Clack CD (2007) Evolving robust GP solutions for hedge fund stock selection in emerging markets. In: GECCO ‘07: Proceedings of the 9th annual conference on genetic and evolutionary computation. London, England. ACM Press, pp 2234–2241

  • Zhang B, Ohm P, Mühlenbein H (1997) Evolutionary induction of sparse neural trees. Evol Comput 5(2):213–236

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work has been partially supported by Spanish Government grants INNPACTO-IPT-2011-1198-430000-IYELMO, TIN 2008-00508 and MEC CONSOLIDER CSD00C-07-20811.

Author information

Authors and Affiliations

  1. Gestión Informática Empresarial, CES Felipe II (UCM), Madrid, Spain

    Diego José Bodas-Sagi, Pablo Fernández-Blanco & Francisco José Soltero-Domingo

  2. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor García Santesmases, 28040, Madrid, Spain

    José Ignacio Hidalgo

Authors
  1. Diego José Bodas-Sagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pablo Fernández-Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Ignacio Hidalgo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco José Soltero-Domingo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Ignacio Hidalgo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bodas-Sagi, D.J., Fernández-Blanco, P., Hidalgo, J.I. et al. A parallel evolutionary algorithm for technical market indicators optimization. Nat Comput 12, 195–207 (2013). https://doi.org/10.1007/s11047-012-9347-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-012-9347-4

Keywords

Search

Navigation