Skip to main content
Springer Nature Link
Log in

Fuzzy-Evolutionary Modeling for Single-Position Day Trading

  • Chapter
Natural Computing in Computational Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

  • 1026 Accesses

Summary

This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day t and produces a go short, do nothing, go long trading signal for day t+1 based on a dataset of past observations of which actions would have been most profitable. The approach has been applied to trading several financial instruments (large-cap stocks and indices): the experimental results are presented and discussed. A method to enhance the performance of trading rules based on the approach by using ensembles of fuzzy models is finally illustrated. The results clearly indicate that, despite its simplicity, the approach may yield significant returns, outperforming a buy-and-hold strategy.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51:245-271

    Article  Google Scholar 

  2. Various Authors (2006) Special issue on integrating multiple learned models. Machine Learning 36(1-2)

    Google Scholar 

  3. Bacardit J, Krasnogor N (2006) Empirical evaluation of ensemble techniques for a pittsburgh learning classifier system. In Proceedings of the Ninth International Workshop on Learning Classifier Systems. Seattle, WA, USA, July 8-9 2006. ACM Press, New York

    Google Scholar 

  4. Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, Oxford

    MATH  Google Scholar 

  5. Bäck T, Fogel D, Michalewicz Z (2000) Evolutionary Computation. IoP Publishing, Bristol

    Google Scholar 

  6. Beretta M, Tettamanzi A (2003) Learning fuzzy classifiers with evolutionary algorithms. In Pasi G, Bonarini A, Masulli F, (ed) Soft Computing Applications. pp 1-10, Physica Verlag, Heidelberg

    Google Scholar 

  7. Bodie Z, Kane A, Marcus A (2006) Investments (7th edn). McGraw-Hill, New York

    Google Scholar 

  8. Brabazon A, O'Neill M (2006) Biologically Inspired Algorithms for Financial Modelling. Springer, Berlin

    MATH  Google Scholar 

  9. Breiman L (1996) Bagging predictors. Machine Learning 24(2):123-140

    MATH  MathSciNet  Google Scholar 

  10. Cantú -Paz E (1997) A survey of parallel genetic algorithms. Technical Report IlliGAL 97003, University of Illinois at Urbana-Champaign

    Google Scholar 

  11. DeJong K (2002) Evolutionary Computation: A unified approach. MIT Press, Cambridge, MA

    Google Scholar 

  12. Dempster M, Jones C (2001) A real-time adaptive trading system using genetic programming. Quantitative Finance 1:397-413

    Article  Google Scholar 

  13. Dempster M, Jones C Romahi Y, Thompson G (2001) Computational learning techniques for intraday fx trading using popular technical indicators. IEEE Transactions on Neural Networks 12(4):744-754

    Article  Google Scholar 

  14. Harlow H (1991) Asset allocation in a downside-risk framework. Financial Analysts Journal pp. 30-40, September/October

    Google Scholar 

  15. Hellendoorn H, Thomas C (1993) Defuzzification in fuzzy controllers. Intelligent and Fuzzy Systems 1:109-123

    Google Scholar 

  16. Hellstrom T, Holmstrom K (1999) Parameter tuning in trading algorithms using asta. Computational Finance 1:343-357

    Google Scholar 

  17. Mamdani E (1976) Advances in linguistic synthesis of fuzzy controllers. International Journal of Man Machine Studies 8:669-678

    Article  MATH  Google Scholar 

  18. Manderick B, Spiessens P (1989) Fine-grained parallel genetic algorithms. In Schaffer J (ed) Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, CA, Morgan Kaufmann

    Google Scholar 

  19. Marney J, Fyfe C, Tarbert H, Miller D (2001) Risk-adjusted returns to technical trading rules: A genetic programming approach. Computing in Economics and Finance 147, Society for Computational Economics, Yale University, USA, June 2001

    Google Scholar 

  20. Mühlenbein H (1989) Parallel genetic algorithms, population genetics and combinatorial optimization. In Schaffer J (ed) Proceedings of the Third International Conference on Genetic Algorithms, pages 416-421, San Mateo, CA, Morgan Kaufmann

    Google Scholar 

  21. Murphy J (1999) Technical Analysis of the Financial Markets. New York Institute of Finance, New York

    Google Scholar 

  22. Neely C, Weller P, Dittmar R (1997) Is technical analysis in the foreign exchange market profitable? a genetic programming approach. Journal of Financial and Quantitative Analysis 32:405-26

    Article  Google Scholar 

  23. Potvin J-Y, Soriano P, Vallée M (2004) Generating trading rules on the stock markets with genetic programming. Computers and Operations Research 31(7):1033-1047

    Article  MATH  Google Scholar 

  24. Tettamanzi A, Poluzzi R, Rizzotto G (1996) An evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor. In ZadehL, Sanchez E, Shibata T (ed) Genetic algorithms and fuzzy logic systems: Soft computing perspectives. World Scientific, Singapore

    Google Scholar 

  25. Ross T (1995) Fuzzy Logic with Engineering Applications. McGraw-Hill, New York

    MATH  Google Scholar 

  26. Sharpe W (1994) The Sharpe ratio. Journal of Portfolio Management 21(1):49-58

    Article  Google Scholar 

  27. Sortino F, van der Meer R (1991) Downside risk — capturing what's at stake in investment situations. Journal of Portfolio Management 17:27-31

    Article  Google Scholar 

  28. Subramanian H, Ramamoorthy S, Stone P, Kuipers B (2006) Designing safe, profitable automated stock trading agents using evolutionary algorithms. In CattolicoM (ed) Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, Washington, USA, July 8-12, 2006, pp. 1777-1784, ACM

    Chapter  Google Scholar 

  29. Tettamanzi A (1995) An evolutionary algorithm for fuzzy controller synthesis and optimization. IEEE International Conference on Systems, Man and Cybernetics, volume 5/5 pp. 4021-4026, IEEE Press

    Google Scholar 

  30. Tettamanzi A, Carlesi M, Pannese L, Santalmasi M (2007) Business intelligence for strategic marketing: Predictive modelling of customer behaviour using fuzzy logic and evolutionary algorithms. In Giacobini M et al. (ed) Applications of evolutionary computing: Evoworkshops2007, volume4448 of Lecture Notes in Computer Science, pp. 233-240, Valencia, Spain, April 11-13 2007, Springer

    Google Scholar 

  31. Whitley D, Rana S, Heckendorn R (1999) The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7(1):33-47

    Google Scholar 

  32. Wikipedia (2007) Technical analysis — wikipedia, the free encyclopedia. Online; accessed 5 June 2007

    Google Scholar 

  33. Yao X, Xu Y (2006) Recent advances in evolutionary computation. Computer Science and Technology 21(1):1-18

    Article  MathSciNet  Google Scholar 

  34. Yu T, Chen S-H, Kuo T-W (2006) Discovering financial technical trading rules using genetic programming with lambda abstraction. In: O'ReillyU-M, Yu T, RioloR, Worzel B, (ed) Genetic Programming Theory and Practice II. pp 11-30, Springer, Berlin

    Chapter  Google Scholar 

  35. Zadeh L (1965) Fuzzy sets. Information and Control 8:338-353

    Article  MATH  MathSciNet  Google Scholar 

  36. Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning, i-ii. Information Science 8:199-249 & 301-357

    Article  MathSciNet  Google Scholar 

  37. Zadeh L (1992) The calculus of fuzzy if-then rules. AI Expert 7(3):22-27

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Tecnologie dell’Informazione, Università Degli Studi di Milano, Milan, Italy

    Célia da Costa Pereira & Andrea G. B. Tettamanzi

Authors
  1. Célia da Costa Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea G. B. Tettamanzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business Quinn School, University College Dublin, Belfield, Dublin 4, Ireland

    Anthony Brabazon (Head of Research) (Head of Research)

  2. Natural Computing Research and Applications School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael O’Neill (Director) (Director)

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

da Costa Pereira, C., Tettamanzi, A.G.B. (2008). Fuzzy-Evolutionary Modeling for Single-Position Day Trading. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-77477-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation