Otimização de um Portfólio de Algoritmos de Negociações Automatizadas utilizando Reinforcement Learning para o controle de risco

• Ramon de Cerqueira Silva UEFS
• Carlos Alberto Rodrigues UEFS

DOI: https://doi.org/10.5753/erbase.2024.4398

Resumo

Este trabalho apresenta uma abordagem inovadora para otimizar portfólios de sistemas de negociação automatizados (ATS) utilizando técnicas avançadas de Deep Reinforcement Learning (DRL). São analisados os algoritmos A2C, DDPG, PPO, SAC e TD3, visando avaliar suas performances em mercados voláteis. O principal objetivo é aprimorar o controle de risco e a eficiência operacional dos ATS com dados do mercado de ações brasileiro. Os modelos de DRL superaram os benchmarks, proporcionando melhor gestão de risco e retornos ajustados. Os resultados destacam o potencial dos algoritmos DRL em ambientes financeiros complexos e abrem caminhos para pesquisas futuras na integração do aprendizado de máquina em finanças quantitativas.

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