Dissertation (December 8, 2025): Fernando Henrique de Jesus Fraga da Silva
Student: Fernando Henrique de Jesus Fraga da Silva
Title: Aprendizado por Reforço Profundo Aplicado à Negociação Intradiária de Múltiplas Ações
Advisors: Eduardo Bezerra da Silva (advisor) e Pedro Henrique González Silva (co-advisor)
Committee: Eduardo Bezerra da Silva (Cefet/RJ), Pedro Henrique González Silva (UFRJ), Aline Marins Paes Carvalho (UFF) e Glauco Fiorott Amorim (Cefet/RJ)
Day/Hour: December 8, 2025 / 15 p.m.
Abstract: The stock market is a dynamic and volatile environment in which publicly traded companies negotiate fractions of their value, subject to continuous price fluctuations influenced by economic, political, and social factors. Anticipating these fluctuations is a complex task, especially in the context of intraday trading, where buy and sell decisions must be made within very short time intervals based on rapidly changing data. In this scenario, Reinforcement Learning (RL) emerges as a promising paradigm capable of developing adaptive strategies through the continuous interaction between agent and environment. This dissertation investigates the use of Deep Reinforcement Learning (DRL) techniques in financial trading, focusing on intraday scenarios involving multiple stocks. It proposes a DRL-based approach to estimate buy and sell actions simultaneously across various assets, using high-granularity market data to better approximate real trading conditions. Experimental analyses were conducted using the Proximal Policy Optimization (PPO) algorithm. The results indicate that the proposed agent outperformed traditional benchmark strategies, achieving gains exceeding 10 percentage points in certain cases.