Title: Combining machine learning and effective feature selection for real-time stock trading in variable time-frames

Authors: A.K.M. Amanat Ullah; Fahim Imtiaz; Miftah Uddin Md Ihsan; Md. Golam Rabiul Alam; Mahbub Majumdar

Addresses: Department of Computer Science, University of British Columbia, Canada ' Department of Computer Science and Engineering, BRAC University, Bangladesh ' Department of Computer Science and Engineering, BRAC University, Bangladesh ' Department of Computer Science and Engineering, BRAC University, Bangladesh ' Department of Computer Science and Engineering, BRAC University, Bangladesh

Abstract: The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalised scheme. Many previous studies tried different techniques to build a machine learning model, which can make a significant profit in the US stock market by performing live trading. However, very few studies have focused on the importance of finding the best features for a particular period for trading. Our top approach used the performance to narrow down the features from a total of 148 to about 30. Furthermore, the top 25 features were dynamically selected before each time training our machine learning model. It uses ensemble learning with four classifiers: Gaussian naive Bayes, decision tree, logistic regression with L1 regularisation and stochastic gradient descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor about making trading decisions in the stock market.

Keywords: feature selection; feature extraction; stock trading; ensemble learning.

DOI: 10.1504/IJCSE.2023.129152

International Journal of Computational Science and Engineering, 2023 Vol.26 No.1, pp.28 - 44

Received: 22 May 2021
Accepted: 16 Sep 2021
Published online: 23 Feb 2023 *