Application of a machine learning model to maximize the success rate in day trade operations on the American Stock Exchange

Abstract

Daytrading has been showing a growing popularity in the world due to easy access via technology, the possibility of additional earnings and a large increase in courses and several mentors available on social networks. This scenario causes many people to be unprepared to enter this market that has a high risk and that end up causing many people to lose their savings. Considering this situation, this study proposes the analysis of the data of a daytrade strategy, applying a machine learning model to help the investor make better decisions. Data from November 2020 to July 2023 was used within the US market based on the company [AMD]. The method used was the supervised machine learning technique known as the decision tree model, which seeks to identify the probability of event and non-event within the scenarios proposed in this work. The results were analyzed using the confusion matrix, gauging the accuracy in the training and test base, applying several decision tree models in order to find the best model and accuracy in the test base. In this sense, an improvement in the assertiveness rate was observed with the application of the supervised machine learning model based on a decision tree.