Avaliação de estratégias de operação em swing trade baseadas em aprendizado de máquina
Resumo
Realizar operações na bolsa de valores é uma tarefa complexa, uma vez que alterações nos mais diversos setores acabam impactando o mercado acionário e, por isso, diversos estudos na área de inteligência artificial abordam esse tema com o propósito de facilitar operações. Esse artigo visa apresentar diferentes estratégias apoiadas pelo uso de algoritmos baseados em aprendizado supervisionado com o intuito de criar um ambiente propício à apreciação do capital. Foram trabalhadas três estratégias para quatro ativos de segmentos diferentes listados na B3 entre 2009 e 2021. Foi adotado o modelo LSTM como o algoritmo para previsão de valores futuros a partir das séries temporais do mercado e seus indicadores. Os resultados apontam que a estratégia proposta pode gerar lucro em mais de 70% das operações, obtendo um retorno geral maior que outros investimentos utilizados como comparação, considerando aportes entre os anos entre os anos de 2012 e 2021.
Palavras-chave:
Estratégias de Swing-Trade, Aprendizado supervisionado, Bolsa de valores, LSTM
Referências
Tensorflow. https://www.tensorflow.org/. Acessado em: 02 de fev. de 2021.
Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M., Mahmoodi, A., and Jamd, P. (2018). New efficient hybrid candlestick technical analysis model for stockmarket timing on the basis of the support vector machine andheuristic algorithms of imperialist competition and genetic. Expert Systems with Applications.
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., and Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of altman’s z-score model. Journal of International Financial Management & Accounting, 28(2):131–171.
Anbalagan, T. and Maheswari, S. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science 47.
Beattie, A. (2020). The birth of stock exchanges. Investopedia. Acessado em: 10 de mar. de 2021.
Brownlee, J. (2018). Predict the Future with MLPs,CNNs and LSTMs in Python. Deep Learning for Time Series Forecasting.
Edwards, R. D., Magee, J., and Bassetti, W. C. (2018). Technical analysis of stock trends. CRC press.
Ferreira, F., Gandomi, A., and Cardoso, R. (2020). Financial time-series analysis of brazilian stockmarket using machine learning. IEEE Symposium Series on Computational Intelligence (SSCI).
Ferreira, F., Gandomi, A., and Cardoso, R. (2021). Artificial intelligence applied to stockmarket trading: A review. IEEE Access (Volume: 9).
Graham, B. (2017). The Intelligent Investor (4.ed. rev.).
Guijarro, F. (2020). Forecasting stock market trend: a comparison ofmachine learning algorithms. Finance, Markets and Valuation.
Kumar, I. (2018). A comparative study of supervised machine learning algorithms for stock market trend prediction. IEEE Xplore Compliant.
Menon, A., Singh, S., and Parekh, H. (2019). A review of stock market prediction using neural networks. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pages 1–6. IEEE.
Nelson, D., Pereira, A., and Oliveira, R. (2017). Stock market’s price movement prediction with lstm neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN).
Nti, I., Adekoya, A., and Weyori, B. (2019). A systematic review of fundamental and technical analysis of stock market predictions. Springer Nature B.V.
Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M., Mahmoodi, A., and Jamd, P. (2018). New efficient hybrid candlestick technical analysis model for stockmarket timing on the basis of the support vector machine andheuristic algorithms of imperialist competition and genetic. Expert Systems with Applications.
Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., and Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of altman’s z-score model. Journal of International Financial Management & Accounting, 28(2):131–171.
Anbalagan, T. and Maheswari, S. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science 47.
Beattie, A. (2020). The birth of stock exchanges. Investopedia. Acessado em: 10 de mar. de 2021.
Brownlee, J. (2018). Predict the Future with MLPs,CNNs and LSTMs in Python. Deep Learning for Time Series Forecasting.
Edwards, R. D., Magee, J., and Bassetti, W. C. (2018). Technical analysis of stock trends. CRC press.
Ferreira, F., Gandomi, A., and Cardoso, R. (2020). Financial time-series analysis of brazilian stockmarket using machine learning. IEEE Symposium Series on Computational Intelligence (SSCI).
Ferreira, F., Gandomi, A., and Cardoso, R. (2021). Artificial intelligence applied to stockmarket trading: A review. IEEE Access (Volume: 9).
Graham, B. (2017). The Intelligent Investor (4.ed. rev.).
Guijarro, F. (2020). Forecasting stock market trend: a comparison ofmachine learning algorithms. Finance, Markets and Valuation.
Kumar, I. (2018). A comparative study of supervised machine learning algorithms for stock market trend prediction. IEEE Xplore Compliant.
Menon, A., Singh, S., and Parekh, H. (2019). A review of stock market prediction using neural networks. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pages 1–6. IEEE.
Nelson, D., Pereira, A., and Oliveira, R. (2017). Stock market’s price movement prediction with lstm neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN).
Nti, I., Adekoya, A., and Weyori, B. (2019). A systematic review of fundamental and technical analysis of stock market predictions. Springer Nature B.V.
Publicado
31/07/2022
Como Citar
MACHADO, Arthur E. S.; VIANA, Rubio T. C.; DALIP, Daniel H.; CARDOSO, Rodrigo T. N.; CRUZ, André da.
Avaliação de estratégias de operação em swing trade baseadas em aprendizado de máquina. In: BRAZILIAN WORKSHOP ON ARTIFICIAL INTELLIGENCE IN FINANCE (BWAIF), 1. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 69-80.
DOI: https://doi.org/10.5753/bwaif.2022.223230.
