Abstract
In previous works a methodology was defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. The GAP algorithm implements the automatic search for crisp trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. This work probes that the proposed methodology is valid for different assets in a different market than previous work.
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References
Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics (51), 245–271 (1999)
Potvin, J.Y., Soriano, P., Vallée, M.: Generating trading rules on the stock markets with genetic programming. Computers and Operations Research (31), 1033–1047 (2004)
Chavarnakul, T., Enke, D.: A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing 72(16-18), 3517–3528 (2009); Financial Engineering; Computational and Ambient Intelligence (IWANN 2007)
Neely, C.J.: Risk-adjusted, ex ante, optimal, technical trading rules in equity markets. Technical report, Federal Reserve Bank of St. Louis (2001)
O’Neill, M., Brabazon, A., Ryan, C.: Forecasting market indices using evolutionary automatic programming. a case study. In: Genetic Algorithms and Genetic Programming in Computational Finance. University of Limerick, University College Dublin, Ireland (2002)
Fernandez, M.E., de la Cal, E.A., Quiroga, R.: Improving return using risk-return adjustment and incremental training in technical trading rules with gaps. Applied Intelligence, 1–14 (2009)
Howard, L., D’Angelo, D.: The ga-p: a genetic algorithm and genetic programming hybrid. IEEE Expert, 11–15 (1995)
Koza, J.: Genetic Programming: On the programming of computers by means of Natural Selection and Genetic. MIT Press, Cambridge (1992)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)
Xufre Casqueiro, P., Rodrigues, A.: Neuro-dynamic trading methods. European Journal of Operational Research (175), 1400–1412 (2006)
Sharpe, W.F.: Mutual fund performance. Journal of Business. Supplement on Security Prices (39), 119–38 (1966)
Coello-Coello, C., Veldhuizen, V., Lamont, G.B.: Evolutionary Algorithms for solving Multi-objective Problems. Kluwer, Dordrecht (2002)
Elaoud, S., Loukil, T., Teghem, J.: The pareto fitness genetic algorithm: Test function study. European Journal of Operational Research (177), 1703–1719 (2007)
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., Herrera, F.: Keel: A software tool to assess evolutionary algorithms to data mining problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications (2008) (Online)
Ng, H.S., Lam, K.P., Lam, S.S.: Incremental genetic fuzzy expert trading system for derivatives market timing. In: IEEE International Conference on Computational Intelligence for Financial Engineering, Hong-Kong, pp. 421–428 (2003)
Cordón, O., Jesus, M.J.D., Herrera, F., Lozano, M.: Mogul: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. International Journal of Intelligent Systems 14, 1123–1153 (1998)
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de la Cal, E.A., Fernández, E.M., Quiroga, R., Villar, J.R., Sedano, J. (2010). Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_18
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DOI: https://doi.org/10.1007/978-3-642-13803-4_18
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