Research Article | Open Access
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
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.
- L. Harris, Trading and Exchanges, Market Microstructure for Practitioners, Oxford University Press, New York, NY, USA, 2003.
- A. Brabazon and M. O'Neill, Biologically Inspired Algorithms for Financial Modelling, Springer, Berlin, Germany, 2006.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- D. Cliff, “Explorations in evolutionary design of online auction market mechanisms,” Electronic Commerce Research and Applications, vol. 2, no. 2, pp. 162–175, 2003.
- 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.
- 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.
- 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.
- 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.
- W. Sharpe, “The Sharpe ratio,” Journal of Portfolio Management, vol. 1, pp. 49–58, 1994.
- 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.
- A. G. B. Tettamanzi and M. Tomassini, Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems, Springer, Berlin, Germany, 2001.
- 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.
- D. Whitley and J. Kauth, “GENITOR: a different genetic algorithm,” Colorado State University, Fort Collins, Colo, USA, 1988.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- X. Yao and Y. Liu, “Towards designing artificial neural networks by evolution,” Applied Mathematics and Computation, vol. 91, no. 1, pp. 83–90, 1998.
- 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.
- K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evolutionary Computation, vol. 10, no. 2, pp. 99–127, 2002.
- 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.
- 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.
- 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.
- M. C. Moze and P. Smolensky, “Using relevance to reduce network size automatically,” Connection Science, vol. 1, no. 1, pp. 3–16, 1989.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Z.-H. Tan, “Hybrid evolutionary approach for designing neural networks for classification,” Electronics Letters, vol. 40, no. 15, pp. 955–957, 2004.
- X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999.
- X. Yao, Evolutionary Optimization, Kluwer Academic Publishers, Norwell, Mass, USA, 2002.
- 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.
- 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.
- 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.
- H. Schwefel, Numerical Optimization for Computer Models, John Wiley & Sons, Chichester, UK, 1981.
- R. Colby, The Encyclopedia of Technical Market Indicators, McGraw-Hill, New York, NY, USA, 2nd edition, 2002.
- 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.
- 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.
- 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.
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.