AlphaB3- Expert Advisor usando Redes Neurais Artificiais e Algoritmos Genéticos para Predizer as Tendências do Mercado de Ações
Resumo
A compreensão da relação entre a situação do mercado de ações e a economia de um país é uma parte essencial dos componentes de qualquer sistema de tomada de decisão financeira. Este artigo descreve as etapas para criar um Expert Advisor (EA) chamado AlphaB3, especializado em negociação de ações no mercado financeiro brasileiro. AlphaB3 usa redes neurais e algoritmos genéticos para comprar ou vender ativos financeiros dinamicamente com base na variação do valor das ações. De acordo com as avaliações do AlphaB3, quando uma rede neural é treinada com dados suficientes, o mecanismo criado guia o investidor a comprar ou vender com maior lucro do que se usasse um EA baseado em uma regra alternativa.
Referências
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Goldberg, D. E. and Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2):95–99.
Karlik, B. and Olgac, A. V. (2011). Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4):111–122.
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Lam, M. (2004). Neural network techniques for financial performance prediction: inte_x0002_grating fundamental and technical analysis. Decision support systems, 37(4):567–581.
Lin, S.-W., Ying, K.-C., Chen, S.-C., and Lee, Z.-J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert systems with applications, 35(4):1817–1824.
Lund, M., de Souza, C. P., and de Carvalho, L. C. S. (2012). Mercado de capitais. Editora FGV, first edition. Rio de Janeiro – RJ.
McEwan, P., Bergenheim, K., Yuan, Y., Tetlow, A. P., and Gordon, J. P. (2010). Assessing the relationship between computational speed and precision. Pharmacoeconomics, 28(8):665–674.
MetaQuotes Softwares (2018). Metatrader 5 trading platform.http://www.metatrader5.com/.
Samanta, B., Al-Balushi, K., and Al-Araimi, S. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineer_x0002_ing Applications of Artificial Intelligence, 16(7):657–665.
Tang, K.-S., Man, K.-F., Kwong, S., and He, Q. (1996). Genetic algorithms and their applications. IEEE signal processing magazine, 13(6):22–37.
Ticknor, J. L. (2013). A bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14):5501–5506.
Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11):1225–1231.
Wang, L. and Wang, Q. (2011). Stock market prediction using artificial neural networks based on hlp. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2011 International Conference on, volume 1, pages 116–119. IEEE.
Whitley, D., Starkweather, T., and Bogart, C. (1990). Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel computing, 14(3):347–361.
Yazdi, S. H. M. and Lashkari, Z. H. (2013). Technical analysis of forex by macd indicator. International Journal of Humanities and Management Sciences (IJHMS), 1(2):159–165.
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77–84.
Zhang, Y. and Wu, L. (2009). Stock market prediction of s&p 500 via combination of improved bco approach and bp neural network. Expert systems with applications, 36(5):8849–8854
Chaturvedi, D. K. (2008). Soft computing: techniques and its applications in electrical engineering, volume 103. Springer.
de Souza, G. A. (2005). Utilizando redes neurais artificiais para modelar a confiabilidade de software. Monografia Graduacao Digital.
Egmont-Petersen, M., de Ridder, D., and Handels, H. (2002). Image processing with neural networks review. Pattern recognition, 35(10):2279–2301.
Goldberg, D. E. and Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2):95–99.
Karlik, B. and Olgac, A. V. (2011). Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4):111–122.
Klerfors, D. (1998). Artificial neural networks. St. Louis University, St. Louis, Mo. Kuo, R. J., Chen, C., and Hwang, Y. (2001). An intelligent stock trading decision sup_x0002_port system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy sets and systems, 118(1):21–45.
Lam, M. (2004). Neural network techniques for financial performance prediction: inte_x0002_grating fundamental and technical analysis. Decision support systems, 37(4):567–581.
Lin, S.-W., Ying, K.-C., Chen, S.-C., and Lee, Z.-J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert systems with applications, 35(4):1817–1824.
Lund, M., de Souza, C. P., and de Carvalho, L. C. S. (2012). Mercado de capitais. Editora FGV, first edition. Rio de Janeiro – RJ.
McEwan, P., Bergenheim, K., Yuan, Y., Tetlow, A. P., and Gordon, J. P. (2010). Assessing the relationship between computational speed and precision. Pharmacoeconomics, 28(8):665–674.
MetaQuotes Softwares (2018). Metatrader 5 trading platform.http://www.metatrader5.com/.
Samanta, B., Al-Balushi, K., and Al-Araimi, S. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineer_x0002_ing Applications of Artificial Intelligence, 16(7):657–665.
Tang, K.-S., Man, K.-F., Kwong, S., and He, Q. (1996). Genetic algorithms and their applications. IEEE signal processing magazine, 13(6):22–37.
Ticknor, J. L. (2013). A bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14):5501–5506.
Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11):1225–1231.
Wang, L. and Wang, Q. (2011). Stock market prediction using artificial neural networks based on hlp. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2011 International Conference on, volume 1, pages 116–119. IEEE.
Whitley, D., Starkweather, T., and Bogart, C. (1990). Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel computing, 14(3):347–361.
Yazdi, S. H. M. and Lashkari, Z. H. (2013). Technical analysis of forex by macd indicator. International Journal of Humanities and Management Sciences (IJHMS), 1(2):159–165.
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77–84.
Zhang, Y. and Wu, L. (2009). Stock market prediction of s&p 500 via combination of improved bco approach and bp neural network. Expert systems with applications, 36(5):8849–8854
Publicado
22/08/2018
Como Citar
MACHADO, Aline; CARVALHO, Caio; SOUZA, Antônio; MACEDO, Márcio.
AlphaB3- Expert Advisor usando Redes Neurais Artificiais e Algoritmos Genéticos para Predizer as Tendências do Mercado de Ações. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 18. , 2018, Aracaju.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 258-267.
