Estratégia automatizada de decisão multicritério no mercado financeiro

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Paulo Azevedo Meijon Campolina
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
Brasil
ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
Programa de Pós-Graduação em Engenharia Elétrica
UFMG
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/1843/61255
Resumo: Investment automation has been a challenge since the beginning of stock markets. With the evolution of computational power, this dream is getting closer to reality. In this context, this paper investigates, develops and applies some computational techniques to automate investments. It was proposed a backtesting tool, with strategy based on multiple technical indicators. The goal with the indicators was to create a committee, responsible for the decision-making using signals as output, such as buy, hold or sell. This approach is made in a way that removes some psychological aspects of human traders that have significant impact on the decision making process under uncertainty conditions. Similar to other approaches it uses technical indicators, however it is different from usual approaches, this method focuses on usage for day trade operation of mini future contracts of index Bovespa, with candles of 5 minutes frequency. Considering the data used, from 2015 to 2021, the strategy used was not able to overcome the reference model used, the classic buy & hold. Even though, the proposed tool seems to be relevant (i) to improve the data acquisition process, that can be challenging depending of the equity and frequency; (ii) for data analysis, with customs metrics and visualizations; (iii) and for optimization and validation of custom and complex strategies, using python, that could be more challenging to implement using MQL5 on Metatrader.