Pattern searcher for decision making of trading agents using genetic algorithm

• Ano de defesa: 2020
• Autor(a) principal: Felipe Vital Cacique
• Orientador(a): Adriano César Machado Pereira
• Banca de defesa: Cristiano Arbex Valle, Gisele Lobo Pappa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Minas Gerais
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
• Departamento: ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
• País: Brasil
• Palavras-chave em Português: Genetic Algorithm; Trading agent; Stock market; Portfolio; Finance; Strategies
• Link de acesso: http://hdl.handle.net/1843/45981

In the last few years, there was a growth regarding the use of computational methods in the field of finance, especially to negotiations in the stock market. Investors have been using computational tools to automate investment strategies with the goal of maximizing profits and reducing risks. In this work, we aim to bring new ideas and approaches to the development of automated trading robots based on historical data of financial series. Our model, named Pattern Searcher, was inspired in machine learning methods and evolutionary optimization. Given a trading agent with its predefined parameters, the method uses the power of Genetic Algorithm (GA) to search, within a set of financial indicators, for the region that provides a higher positive return. This implementation exhibited desirable properties compared to some Machine Learning methods, such as the simplification of the system flow and the generation of rules that humans can clearly understand. Besides, we have generated strategy portfolios, composed by the strategies derived from the Pattern Searcher method, that were also optimized via GA. The system was able to generate very profitable trading agents and portfolios on the Brazilian stock market, surpassing important benchmarks.