A GA Combining Technical and Fundamental Analysis for Trading the Stock Market

作者: Iván Contreras , José Ignacio Hidalgo , Laura Núñez-Letamendia

DOI: 10.1007/978-3-642-29178-4_18

关键词:

摘要: Nowadays, there are two types of financial analysis oriented to design trading systems: fundamental and technical. Fundamental consists in the study all information (both nonfinancial) available on market, with aim carrying out efficient investments. By contrast, technical works under assumption that when we analyze price action a specific (indirectly) analyzing factors related market. In this paper propose use an Evolutionary Algorithm optimize parameters system which combines Technical (indicators). The algorithm takes advantage new operator called Filling Operator avoids problems premature convergence reduce number evaluations needed. experimental results promising, since methodology is applied values 100 companies year, they show possible return 830% compared 180% Buy Hold strategy.

参考文章(15)
J. David Schaffer, Proceedings of the third international conference on Genetic algorithms international conference on genetic algorithms. ,(1989)
Iván Contreras, Yiyi Jiang, J. Ignacio Hidalgo, Laura Núñez-Letamendia, Using a GPU-CPU architecture to speed up a GA-based real-time system for trading the stock market Soft Computing. ,vol. 16, pp. 203- 215 ,(2012) , 10.1007/S00500-011-0714-3
Diego J. Bodas-Sagi, Pablo Fernández, J. Ignacio Hidalgo, Francisco J. Soltero, José L. Risco-Martín, Multiobjective optimization of technical market indicators Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09. pp. 1999- 2004 ,(2009) , 10.1145/1570256.1570266
Laura Núñez‐Letamendia, Trading systems designed by genetic algorithms Managerial Finance. ,vol. 28, pp. 87- 106 ,(2002) , 10.1108/03074350210768022
Dome Lohpetch, David Corne, Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold nature and biologically inspired computing. pp. 439- 444 ,(2009) , 10.1109/NABIC.2009.5393324
Dome Lohpetch, David Corne, Multiobjective algorithms for financial trading: Multiobjective out-trades single-objective congress on evolutionary computation. pp. 192- 199 ,(2011) , 10.1109/CEC.2011.5949618
Eugene F. Fama, Kenneth R. French, Business conditions and expected returns on stocks and bonds Journal of Financial Economics. ,vol. 25, pp. 23- 49 ,(1989) , 10.1016/0304-405X(89)90095-0
Franklin Allen, Risto Karjalainen, Using genetic algorithms to find technical trading rules1 Journal of Financial Economics. ,vol. 51, pp. 245- 271 ,(1999) , 10.1016/S0304-405X(98)00052-X
Gilbert Syswerda, Uniform crossover in genetic algorithms international conference on genetic algorithms. pp. 2- 9 ,(1989)