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Foreign Exchange Trading Using a Learning Classifier System

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Learning Classifier Systems in Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 125))

Summary

We apply a simple Learning Classifier System to a foreign exchange trading problem. The performance of the Learning Classifier System is compared to that of a Genetic Programming approach from the literature.

The simple Learning Classifier System is able to achieve a positive excess return in simulated trading, but results are not yet fully competitive because the Learning Classifier System trades too frequently. However, the Learning Classifier System approach shows potential because returns are obtained with no offline training and the technique is inherently adaptive, unlike many of the machine learning methods currently employed for financial trading.

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References

  1. M. Ahluwalia and L. Bull. A Genetic Programming-Based Classifier System. In Banzhaf et al. [5], pages 11–18.

    Google Scholar 

  2. F. Allen and R. Karjalainen. Using Genetic Algorithms to find technical trading rules. Journal of Financial Economics, 51(2):245–271, 1999.

    Article  Google Scholar 

  3. G. Armano, M. Marchesi, and A. Murru. NXCS: Hybrid approach to stock indexes forecasting. In S. -H. Chen, editor, Genetic Algorithms and Genetic Programming in Computational Finance, pages 125–158. Kluwer, Boston, MA, 2002.

    Google Scholar 

  4. Bank for International Settlements. Triennial Central Bank Survey. Foreign Exchange and Derivatives Market Activity in 2001. Document from the Monetary and Economic Department, Basel, Switzerland, 2002.

    Google Scholar 

  5. W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, editors, GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, San Francisco, 1999.

    Google Scholar 

  6. R. J. Bauer, Jr. Genetic Algorithms and Investment Strategies. Wiley, New York, 1994.

    Google Scholar 

  7. L. Beltrametti, R. Fiorentini, L. Marengo, and R. Tamborini. A learning-to-forecast experiment on the foreign exchange market with a Classifier System. Journal of Economic Dynamics and Control, 21(8–9):1543–1575, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  8. Y. Bengio. Using a financial training criterion rather than a prediction criterion. International Journal of Neural Systems, 8(4):433–443, 1997.

    Article  Google Scholar 

  9. P. Bonelli, A. Parodi, S. Sen, and S. W. Wilson. NEWBOOLE: A fast GBML system. In B. W. Porter and R. J. Mooney, editors, Proceedings of the Seventh International Conference on Machine Learning, pages 153–159. Morgan Kaufmann, San Mateo, 1990.

    Google Scholar 

  10. M. M. Dacorogna, R. Gençay, U. Müller, R. B. Olsen, and O. V. Pictet. An Introduction to High-Frequency Finance. Academic, San Diego, 2001.

    Google Scholar 

  11. M. M. Dacorogna, R. Gençay, U. A. Müller, and O. V. Pictet. Effective return, risk aversion and drawdowns. Physica A, 289(1–2):229–248, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  12. M. de la Maza and D. Yuret. A critique of the standard Neural Network application to financial time series analysis. http://citeseer.ist.psu.edu/146873.html.

  13. M. A. H. Dempster, T. W. Payne, and Y. S. Romahi. Intraday FX trading: Reinforcement vs evolutionary learning. Judge Institute of Management Working Paper 23/2001, University of Cambridge, Cambridge, UK, 2001.

    Google Scholar 

  14. R. Gençay. Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of International Economics, 47(1):91–107, 1999.

    Article  Google Scholar 

  15. C. B. Harley. Learning the evolutionarily stable strategy. Journal of Theorectical Biology, 89(4):611–633, 1981.

    Article  Google Scholar 

  16. D. Jensen. Data snooping, dredging and fishing: The dark side of data mining a SIGKDD99 panel report. SIGKDD Explorations, 1(2):52–54, 2000.

    Article  Google Scholar 

  17. J. R. Koza. Genetic Programming. MIT, Cambridge, 1992.

    MATH  Google Scholar 

  18. P. L. Lanzi. Extending the Representation of Classifier Conditions, Part ii: From Messy Coding to S-Expressions. In Banzhaf et al. [5], pages 345–352.

    Google Scholar 

  19. B. LeBaron. Do moving average trading rule results imply non-linearities in foreign exchange markets? Working Paper 9222, University of Wisconsin-Madison, Social Systems Research Institute, 1992.

    Google Scholar 

  20. R. M. Levich and L. R. Thomas, III. The significance of technical trading-rule profits in the foreign exchange market: A bootstrap approach. Journal of International Money and Finance, 12(5):451–474, 1993.

    Article  Google Scholar 

  21. J. Li and E. P. K. Tsang. Investment decision making using FGP: A case study. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress of Evolutionary Computation, pages 1253–1259. IEEE Press, Piscataway, 1999.

    Google Scholar 

  22. S. Mahfoud and G. Mani. Financial forecasting using Genetic Algorithms. Applied Artificial Intelligence, 10(6):543–565, 1996.

    Article  Google Scholar 

  23. J. Moody and M. Saffell. Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4):875–889, 2001.

    Article  Google Scholar 

  24. J. Moody and L. Wu. Optimization of trading systems and portfolios. In A. S. Weigend, Y. Abu-Mostafa, and A.-P. N. Refenes, editors, Decision Technologies for Financial Engineering, volume 7 of Progress in Neural Processing, pages 23–35. World Scientific, Singapore, 1997.

    Google Scholar 

  25. C. Neely, P. Weller, and R. Dittmar. Is technical analysis in the foreign exchange market profitable? a Genetic Programming approach. Journal of Financial and Quantitative Analysis, 32(4):405–427, 1997.

    Article  Google Scholar 

  26. C. J. Neely. Technical analysis in the foreign exchange market: A layman’s guide. Federal Reserve Bank of St. Louis Review, pages 23–38, September/October 1997.

    Google Scholar 

  27. M. Oussaidène, B. Chopard, O. V. Pictet, and M. Tomassini. Parallel Genetic Programming: An application for trading model induction. In J. R. Koza, D. Goldberg, and R. L. Riolo, editors, Genetic Programming 1996, Proceedings of the First Annual Conference, 1996.

    Google Scholar 

  28. O. V. Pictet, M. M. Dacorogna, B. Chopard, M. Oudsaidène, R. Schirru, and M. Tomassini. Using Genetic Algorithms for robust optimization in financial applications. Neural Network World, 5(4):573–587, 1995.

    Google Scholar 

  29. S. Schulenburg and P. Ross. An adaptive agent based economic model. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Learning Classifier Systems. From Foundations to Applications, volume LNAI-1813 of Lecture Notes in Artificial Intelligence, pages 265–284. Springer, Berlin Heidelberg, New York, 2000.

    Google Scholar 

  30. S. Schulenburg and P. Ross. Strength and money: An LCS approach to increasing returns. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Advances in Learning Classifier Systems. Proceedings of the Third International Workshop (IWLCS-2000), volume LNAI-1996 of Lecture Notes in Artificial Intelligence, pages 114–137. Springer, Berlin Heidelberg New York, 2001.

    Chapter  Google Scholar 

  31. S. Schulenburg and P. Ross. Explorations in LCS models of stock trading. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Advances in Learning Classifier Systems. Proceedings of the Fourth International Workshop (IWLCS-2001), volume LNAI-2321 of Lecture Notes in Artificial Intelligence, pages 151–180. Springer, Berlin Heidelberg New York, 2002.

    Google Scholar 

  32. W. F. Sharpe. Mutual fund performance. Journal of Business, 39(1):119–138, 1966.

    Article  Google Scholar 

  33. W. F. Sharpe. The Sharpe Ratio. Journal of Portfolio Management, 21(1):49–58, 1994.

    Article  Google Scholar 

  34. J. Maynard Smith. Evolution and the Theory of Games. Cambridge University Press, Cambridge, UK, 1982.

    MATH  Google Scholar 

  35. C. Stone and L. Bull. For real! XCS with continuous-valued inputs. Evolutionary Computation, 11(3):299–336, 2003.

    Article  Google Scholar 

  36. C. Stone and L. Bull. Configuring ZCS for continuous-valued single-step Boolean problems. Technical Report UWELCSG05-006, University of the West of England Learning Classifier Group, Bristol, UK, 2005. http://www.cems.uwe.ac.uk/ lcsg/reports/uwelcsg05-006.ps.

  37. R. Sullivan, A. Timmermann, and H. White. Data-snooping, technical trading rule performance and the bootstrap. Disussion Paper 97–31, Department of Economics, University of California, San Diego, 1997.

    Google Scholar 

  38. M. P. Taylor and H. Allen. The use of technical analysis in the foreign exchange market. Journal of International Money and Finance, 11(3):304–314, 1992.

    Article  Google Scholar 

  39. B. Van Roy. Temporal-difference learning and applications in finance. In Y. S. Abu-Mostafa, B. LeBaron, A. W. Lo, and A. S. Weigend, editors, Computational Finance 1999, pages 447–461. MIT, Cambridge, 1999.

    Google Scholar 

  40. S. W. Wilson. Classifier systems and the animat problem. Machine Learning, 2(3): 199–228, 1987.

    Google Scholar 

  41. S. W. Wilson. ZCS: A zeroth order classifier system. Evolutionary Computation, 2(1): 1–18, 1994.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, Bristol, BS16 1QY, UK

    Christopher Stone & Larry Bull

Authors
  1. Christopher Stone
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  2. Larry Bull
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of the West of England, Bristol, BS16 1QY, UK

    Larry Bull

  2. Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  3. Centre for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, 19104, USA

    John Holmes

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Stone, C., Bull, L. (2008). Foreign Exchange Trading Using a Learning Classifier System. In: Bull, L., Bernadó-Mansilla, E., Holmes, J. (eds) Learning Classifier Systems in Data Mining. Studies in Computational Intelligence, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78979-6_8

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  • DOI: https://doi.org/10.1007/978-3-540-78979-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78978-9

  • Online ISBN: 978-3-540-78979-6

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