Journal of Artificial Evolution and Applications

Journal of Artificial Evolution and Applications / 2008 / Article

Research Article | Open Access

Volume 2008 |Article ID 184286 | https://doi.org/10.1155/2008/184286

Antonia Azzini, Andrea G. B. Tettamanzi, "Evolving Neural Networks for Static Single-Position Automated Trading", Journal of Artificial Evolution and Applications, vol. 2008, Article ID 184286, 17 pages, 2008. https://doi.org/10.1155/2008/184286

Evolving Neural Networks for Static Single-Position Automated Trading

Academic Editor: Anthony Brabazon
Received30 Jul 2007
Revised30 Nov 2007
Accepted16 Jan 2008
Published14 Apr 2008

Abstract

This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns.

References

  1. L. Harris, Trading and Exchanges, Market Microstructure for Practitioners, Oxford University Press, New York, NY, USA, 2003.
  2. A. Brabazon and M. O'Neill, Biologically Inspired Algorithms for Financial Modelling, Springer, Berlin, Germany, 2006.
  3. H. Subramanian, S. Ramamoorthy, P. Stone, and B. J. Kuipers, “Designing safe, profitable automated stock trading agents using evolutionary algorithms,” in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO '06), vol. 2, pp. 1777–1784, Seattle, Wash, USA, July 2006. View at: Publisher Site | Google Scholar
  4. A. Azzini and A. G. B. Tettamanzi, “Neuro-genetic single position day trading,” in Proceedings of the Workshop Italiano di Vita Artificiale e Computazione Evolutiva (WIVACE '07), Sicily, Italy, September 2007. View at: Google Scholar
  5. A. Azzini and A. G. B. Tettamanzi, “A neural evolutionary approach to financial modeling,” in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO '06), vol. 2, pp. 1605–1612, Seattle, Wash, USA, July 2006. View at: Publisher Site | Google Scholar
  6. M. A. H. Dempster and C. Jones, “A real-time adaptive trading system using genetic programming,” Quantitative Finance, vol. 1, no. 4, pp. 397–413, 2001. View at: Publisher Site | Google Scholar
  7. M. A. H. Dempster, T. W. Payne, Y. Romahi, and G. W. P. Thompson, “Computational learning techniques for intraday FX trading using popular technical indicators,” IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 744–754, 2001. View at: Publisher Site | Google Scholar
  8. F. Allen and R. Karjalainen, “Using genetic algorithms to find technical trading rules,” Journal of Financial Economics, vol. 51, no. 2, pp. 245–271, 1999. View at: Publisher Site | Google Scholar
  9. D. Cliff, “Explorations in evolutionary design of online auction market mechanisms,” Electronic Commerce Research and Applications, vol. 2, no. 2, pp. 162–175, 2003. View at: Publisher Site | Google Scholar
  10. A. Skabar and I. Cloete, “Neural networks, financial trading and the efficient markets hypothesis,” in Proceedings of the 25th Australasian Conference on Computer Science, vol. 4, pp. 241–249, Australian Computer Science, Melbourne, Victoria, Australia, January-February 2002. View at: Google Scholar
  11. S. Hayward, “Evolutionary artificial neural network optimisation in financial engineering,” in Proceedings of the 4th International Conference on Hybrid Intelligent Systems (HIS '04), pp. 210–215, Kitakyushu, Japan, December 2005. View at: Publisher Site | Google Scholar
  12. L. Yi-Hui, “Evolutionary neural network modeling for forecasting the field failure data of repairable systems,” Expert Systems with Applications, vol. 33, no. 4, pp. 1090–1096, 2007. View at: Publisher Site | Google Scholar
  13. G. Armano, M. Marchesi, and A. Murru, “A hybrid genetic-neural architecture for stock indexes forecasting,” Information Sciences, vol. 170, no. 1, pp. 3–33, 2005. View at: Publisher Site | Google Scholar | MathSciNet
  14. W. Sharpe, “The Sharpe ratio,” Journal of Portfolio Management, vol. 1, pp. 49–58, 1994. View at: Google Scholar
  15. F. Sortino and R. van der Meer, “Downside risk, Capturing what's at stake in investment situations,” Journal of Portfolio Management, vol. 17, pp. 27–31, 1991. View at: Google Scholar
  16. A. G. B. Tettamanzi and M. Tomassini, Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems, Springer, Berlin, Germany, 2001.
  17. D. Montana and L. Davis, “Training feedforward neural networks using genetic algorithms,” in Proceedings of the 11th International Conference on Artificial Intelligence (IJCAI '89), pp. 762–767, Morgan Kaufmann, Detroit, Mich, USA, August 1989. View at: Google Scholar
  18. D. Whitley and J. Kauth, “GENITOR: a different genetic algorithm,” Colorado State University, Fort Collins, Colo, USA, 1988. View at: Google Scholar
  19. R. Keesing and D. G. Stork, “Evolution and learning in neural networks: the number and distribution of learning trials affect the rate of evolution,” in Proceedings of the Conference on Advances in Neural Information Processing Systems 3, pp. 804–810, Denver, Colo, USA, November 1990. View at: Google Scholar
  20. B. Yang, X.-H. Su, and Y.-D. Wang, “BP neural network optimization based on an improved genetic algorithm,” in Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 1, pp. 64–68, Beijing, China, November 2002. View at: Publisher Site | Google Scholar
  21. P. Mordaunt and A. M. S. Zalzala, “Towards an evolutionary neural network for gait analysis,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), vol. 2, pp. 1238–1243, Honolulu, Hawaii, USA, May 2002. View at: Publisher Site | Google Scholar
  22. U. Seiffert, “Multiple layer perceptron training using genetic algorithms,” in Proceedings of the European Symposium on Artificial Neural Networks (ESANN '01), pp. 159–164, Bruges, Belgium, April 2001. View at: Google Scholar
  23. G. A. Vijayalakshmi Pai, “A fast converging evolutionary neural network for the prediction of uplift capacity of Suction Caissons,” in Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems (CIS '04), vol. 1, pp. 654–659, Singapore, December 2004. View at: Google Scholar
  24. J. J. Merelo Guervós, M. Patón, A. Cañas, A. Prieto, and F. Morán, “Optimization of a competitive learning neural network by genetic algorithms,” in Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation (IWANN '93), J. Mira, J. Cabestany, and A. Prieto, Eds., vol. 686 of Lecture Notes in Computer Science, pp. 185–192, Springer, Sitges, Spain, June 1993. View at: Publisher Site | Google Scholar
  25. P. A. Castillo-Valdivieso, M. R. Rivas Santos, J. J. Merelo Guervós, J. Gonzalez, A. Prieto, and G. Romero, “G-prop-III: global optimization of multilayer perceptrons using an evolutionary algorithm,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '99), W. Banzhaf, J. Daida, A. E. Eiben et al., Eds., vol. 1, p. 942, Morgan Kaufmann, Orlando, Fla, USA, July 1999. View at: Google Scholar
  26. P. A. Castillo-Valdivieso, J. J. Merelo Guervós, A. Prieto, I. Rojas, and G. Romero, “Statistical analysis of the parameters of a neuro-genetic algorithm,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1374–1394, 2002. View at: Publisher Site | Google Scholar
  27. D. J. Chalmers, “The evolution of learning: an experiment in genetic connectionism,” in Proceedings of the Connectionist Summer School Workshop, D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, Eds., pp. 81–90, Morgan Kaufmann, San Mateo, Calif, USA, 1990. View at: Google Scholar
  28. A. Radi and R. Poli, “Discovering efficient learning rules for feedforward neural networks using genetic programming,” Department of Computer Science, University of Essex, Essex, UK, January 2002. View at: Google Scholar
  29. X. Yao and Y. Liu, “Evolving artificial neural networks through evolutionary programming,” in Proceedings of the 5th Annual Conference on Evolutionary Programming, pp. 257–266, MIT Press, San Diego, Calif, USA, February-March 1996. View at: Google Scholar
  30. J. C. Figueira Pujol and R. Poli, “Evolution of neural networks using weight mapping,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '99), W. Banzhaf, J. Daida, A. E. Eiben et al., Eds., vol. 2, pp. 1170–1177, Morgan Kaufmann, Orlando, Fla, USA, July 1999. View at: Google Scholar
  31. F. Z. Brill, D. E. Brown, and W. N. Martin, “Fast genetic selection of features for neural network classifiers,” IEEE Transactions on Neural Networks, vol. 3, no. 2, pp. 324–328, 1992. View at: Publisher Site | Google Scholar
  32. C. R. Reeves and S. J. Taylor, “Selection of training data for neural networks by a genetic algorithm,” in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN '98), A. Eiben, D. Back, M. Schoenauer, and H. P. Schwefel, Eds., vol. 1498 of Lecture Notes in Computer Science, pp. 633–642, Springer, Amsterdam, The Netherlands, September 1998. View at: Publisher Site | Google Scholar
  33. X. Yao and Y. Liu, “Towards designing artificial neural networks by evolution,” Applied Mathematics and Computation, vol. 91, no. 1, pp. 83–90, 1998. View at: Publisher Site | Google Scholar
  34. G. F. Miller, P. M. Todd, and S. U. Hegde, “Designing neural networks using genetic algorithms,” in Proceedings of the 3rd International Conference on Genetic Algorithms, J. D. Schaffer, Ed., pp. 379–384, Fairfax, Va, USA, June 1989. View at: Google Scholar
  35. K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evolutionary Computation, vol. 10, no. 2, pp. 99–127, 2002. View at: Publisher Site | Google Scholar
  36. X. Yao and Y. Liu, “A new evolutionary system for evolving artificial neural networks,” IEEE Transactions on Neural Networks, vol. 8, no. 3, pp. 694–713, 1997. View at: Publisher Site | Google Scholar
  37. E. F. M. Filho and A. C. P. de Carvalho, “Evolutionary design of MLP neural network architectures,” in Proceedings of the 4th Brazilian Symposium on Neural Networks (SBRN '97), pp. 58–65, Goiania, Brazil, December 1997. View at: Publisher Site | Google Scholar
  38. S. Harp, T. Samad, and A. Guha, “Towards the genetic synthesis of neural networks,” in Proceedings of the 3rd International Conference on Genetic Algorithms, J. D. Schaffer et al., Ed., pp. 360–369, Morgan Kaufmann, Fairfax, Va, USA, June 1989. View at: Google Scholar
  39. M. C. Moze and P. Smolensky, “Using relevance to reduce network size automatically,” Connection Science, vol. 1, no. 1, pp. 3–16, 1989. View at: Publisher Site | Google Scholar
  40. V. Maniezzo, “Genetic evolution fo the topology and weight distribution of neural networks,” IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 39–53, 1994. View at: Publisher Site | Google Scholar
  41. F. H. F. Leung, H. K. Lam, S. H. Ling, and P. K. S. Tam, “Tuning of the structure and parameters of a neural network using an improved genetic algorithm,” IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 79–88, 2003. View at: Publisher Site | Google Scholar
  42. N. García-Pedrajas, C. Hervás-Martínez, and J. Muñoz-Pérez, “COVNET: a cooperative coevolutionary model for evolving artificial neural networks,” IEEE Transactions on Neural Networks, vol. 14, no. 3, pp. 575–596, 2003. View at: Publisher Site | Google Scholar
  43. M. M. Islam, X. Yao, and K. Murase, “A constructive algorithm for training cooperative neural network ensembles,” IEEE Transactions on Neural Networks, vol. 14, no. 4, pp. 820–834, 2003. View at: Publisher Site | Google Scholar
  44. P. P. Palmes, T. Hayasaka, and S. Usui, “Mutation-based genetic neural network,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 587–600, 2005. View at: Publisher Site | Google Scholar
  45. P. J. Angeline, G. M. Saunders, and J. B. Pollack, “An evolutionary algorithm that constructs recurrent neural networks,” IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 54–64, 1994. View at: Publisher Site | Google Scholar
  46. Z.-H. Tan, “Hybrid evolutionary approach for designing neural networks for classification,” Electronics Letters, vol. 40, no. 15, pp. 955–957, 2004. View at: Publisher Site | Google Scholar
  47. X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999. View at: Publisher Site | Google Scholar
  48. X. Yao, Evolutionary Optimization, Kluwer Academic Publishers, Norwell, Mass, USA, 2002.
  49. A. Azzini, L. Cristaldi, M. Lazzaroni, A. Monti, F. Ponci, and A. G. B. Tettamanzi, “Incipient fault diagnosis in electrical drives by tuned neural networks,” in Proceedings of the IEEE Instrumentation and Measurement Technology Conference (IMTC '06), pp. 1284–1289, Sorrento, Italy, April 2006. View at: Publisher Site | Google Scholar
  50. A. Azzini and A. G. B. Tettamanzi, “A neural evolutionary classification method for brain-wave analysis,” in Proceedings of the European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EVOIASP '06), vol. 3907 of Lecture Notes in Computer Science, pp. 500–504, Budapest, Hungary, April 2006. View at: Publisher Site | Google Scholar
  51. H. Muhlenbein and D. Schlierkamp-Voosen, “The science of breeding and its application to the breeder genetic algorithm (bga),” Evolutionary Computation, vol. 1, no. 4, pp. 335–360, 1993. View at: Google Scholar
  52. H. Schwefel, Numerical Optimization for Computer Models, John Wiley & Sons, Chichester, UK, 1981.
  53. R. Colby, The Encyclopedia of Technical Market Indicators, McGraw-Hill, New York, NY, USA, 2nd edition, 2002.
  54. J. A. Bikker, L. Spierdijk, and P. J. van der Sluis, “Market impact costs of institutional equity trades,” Journal of International Money and Finance, vol. 26, no. 6, pp. 974–1000, 2007. View at: Publisher Site | Google Scholar
  55. J. Chen, H. Hong, M. Huang, and J. D. Kubik, “Does fund size erode mutual fund performance? the role of liquidity and organization,” American Economic Review, vol. 94, no. 5, pp. 1276–1302, 2004. View at: Publisher Site | Google Scholar
  56. C. da Costa Pereira and A. G. B. Tettamanzi, “Fuzzy-evolutionary modeling for single-position day trading,” in Natural Computing in Computational Economics and Finance, A. Brabazon and M. O'Neill, Eds., vol. 100, Springer, Berlin, Germany, 2008. View at: Google Scholar

Copyright © 2008 Antonia Azzini and Andrea G. B. Tettamanzi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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