Utilização de aprendizado por reforço para operações em bolsa de valores

Trabalho de Conclusão Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.Este trabalho tem como objetivo mostrar como um agente inteligente, treinado por meio do algoritmo Q-Learning, pode obter resultados consideráveis ao operar na bolsa de va- lores, usando dados financeiros ruidosos, não-lineares e não-estacionários. Para avaliar o desempenho do agente, construiu-se um simulador da bolsa de valores. Utilizando quatro indicadores técnicos como parâmetros de entrada e uma adaptação do retorno diário como função de recompensa, o agente obteve desempenho superior ao Buy & Hold em metade dos casos. Além disso, abre-se espaço para a discussão acerca do comportamento de ações de diferentes setores da economia norte-americana e das possíveis limitações da análise técnica, de acordo com os resultados obtidos.This work aims to show how an intelligent agent trained by the Q-Learning algorithm can achieve intesting results, using non-linear, non-stationary and noisy financial data. In order to evaluate the system perfomance, a stock market simulator was developed. The agent outperformed the Buy & Hold strategy in half of cases, using technical indicators as input data and a modified version of daily return as the reward function. Furthermore, the stock market behavior of different economy sectors is discussed along with possible limitations of technical analysis

Cooperative coevolution of feed forward neural networks for financial time series problem

Intelligent financial prediction systems guide investors in making good investments. Investors are continuously on the hunt for better financial prediction systems. Neural networks have shown good results in the area of financial prediction. Cooperative coevolution is an evolutionary computation method that decomposes the problem into subcomponents and has shown promising results for training neural networks. This paper presents a computational intelligence framework for financial prediction where cooperative coevolutionary feedforward neural networks are used for predicting closing market prices for companies listed on the NASDAQ stock exchange. Problem decomposition is an important step in cooperative coevolution that affects its performance. Synapse and Neuron level are the main problem decomposition methods in cooperative coevolution. These two methods are used for training neural networks on the given financial prediction problem. The results show that Neuron level problem decomposition gives better performance in general. A prototype of a mobile application is also given for investors that can be used on their Android devices