SOURCE RECORD · semantic_scholar
Pattern searcher for decision making of trading agents using Genetic Algorithm
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. In this paper, we aim to bring new ideas and approaches to the development of automated trading or bots based on historical data of financial series. Our model, named Pattern Searcher, was inspired in unsupervised 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 financial 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, which 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.