Algoritmo de negociação utilizando Deep Learning
Creators: name: Ferreira Dias, Gilson Urbano
Publisher: name: Zenodo
Year: 2019
Subjects: Algorithmic Trading
Redes Neurais Artificiais
Series Temporais
Mercado financeiro

description: A utilização de computadores para executar regras matemáticas com o objetivo de realizar negociações automatizadas segue em crescimento. Entre os principais motivos para que os <em>Algorithmic Tradings</em> (ATs) sejam tão populares atualmente, estão o fato de serem muito mais rápidos, atentos e capazes de processar grandes quantidades de informações que os seres humanos, além de serem desprovidos de emoção. No Brasil o número de investidores recentemente ultrapassou a marca de 1 milhão de cadastros únicos. Um número que ainda é pequeno, se comparado aos mercados internacionais, mas que apresenta um grande potencial de crescimento. As estratégias automatizadas podem ser aplicadas para fomentar a entrada de novos investidores, aumentando o número de investidores e, consequentemente, melhorando a qualidade do mercado financeiro nacional. A solução proposta é a utilização de novos métodos para amostrar as séries históricas e redes neurais profundas para criação de estratégias de investimento, capazes de realizar a previsão de séries temporais financeiras e tomada de decisão para pessoas com pouco conhecimento no mercado financeiro. O tratamento aplicado nos dados resultou em recuperação parcial da normalidade, o que podem melhorar a qualidade dos dados aplicados em modelos que assumem normalidade. A arquitetura de rede neural CLDNN usada, obteve bons resultados na fase de teste e foi utilizada nos 192 testes com ações negociadas na bolsa brasileira. Finalmente, houve resultados financeiros positivos, indicando a possibilidade de algoritmos de negociação que usam redes neurais artificias obterem lucros.
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