Abstract
Automated Trading is the activity of buying and selling financial instruments for the purpose of gaining a profit, through the use of automated trading rules. This work presents an evolutionary approach for the design and optimization of artificial neural networks to the discovery of profitable automated trading rules. 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.
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References
Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51, 245 (1999)
Azzini, A., Tettamanzi, A.: A neural evolutionary approach to financial modeling. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006, vol. 2, pp. 1605–1612. Morgan Kaufmann, San Francisco (2006)
Azzini, A., Tettamanzi, A.: Neuro-genetic single position day trading. In: Workshop Italiano di Vita Artificiale e Computazione Evolutiva, WIVACE 2007 (2007)
Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Heidelberg (2006)
Cliff, D.: Explorations in evolutionary design of online auction market mechanisms. Electronic Commerce Research and Applications 2, 162 (2003)
Colby, R.: The Encyclopedia Of Technical Market Indicators, 2nd edn. McGraw-Hill, New York (2002)
Dempster, M., Jones, C.: A real-time adaptive trading system using genetic programming. Quantitative Finance 1(4), 397–413 (2001)
Dempster, M., Jones, C., Romahi, Y., Thompson, G.: Computational learning techniques for intraday fx trading using popular technical indicators. IEEE Transactions on Neural Networks 12(4) (2001)
Harris, L.: Trading and Exchanges, Market Microstructure for Practitioners. Oxford University Press, New York (2003)
Hayward, S.: Evolutionary artificial neural network optimisation in financial engineering. In: Proceedings of the Fourth International Conference on Hybrid Intelligent Systems, HIS 2004, IEEE Computer Society Press, Los Alamitos (2004)
Sharpe, W.: The Sharpe ratio. Journal of Portfolio Management 1, 49–58 (1994)
Skabar, A., Cloete, I.: Neural networks, financial trading and the efficient markets hypothesis. In: Proceedings of the Australian Computer Science Conference 2002, vol. 4, pp. 241–249. Australian Computer Science Inc. (2002)
Sortino, F., der Meer, R.V.: Downside risk – capturing what’s stake in investment situations. Journal of Portfolio Management 17, 27–31 (1991)
Subramanian, H., Ramamoorthy, S., Stone, P., Kuipers, B.: Designing safe, profitable automated stock trading agents using evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006, vol. 1, pp. 1777–1784. Morgan Kaufmann, San Francisco (2006)
Yao, X.: Evolutionary Optimization. Kluwer Academic Publishers, Norwell, Massachusetts (2002)
Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks 8(3), 694–713 (1997)
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Azzini, A., Tettamanzi, A.G.B. (2008). Evolutionary Single-Position Automated Trading. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_7
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DOI: https://doi.org/10.1007/978-3-540-78761-7_7
Publisher Name: Springer, Berlin, Heidelberg
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