Ullah, A.K.M. AmanatImtiaz, FahimIhsan, Miftah Uddin MdAlam, Md. Golam RabiulMajumdar, Mahbub2026-08-252026-08-252023-01-01Ullah, A. K. M. A., Imtiaz, F., Ihsan, M. U. M., Alam, Md. G. R., & Majumdar, M. (2023). Combining machine learning and effective feature selection for real-time stock trading in variable time-frames. International Journal of Computational Science and Engineering, 26(1), 28. https://doi.org/10.1504/IJCSE.2023.129152174271852-s2.0-85153520083https://hdl.handle.net/10361/29507The 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. Copyright © 2023 Inderscience Enterprises Ltd.28 - 44en-USEnsemble learningFeature extractionFeature selectionStock tradingMachine learning.Stock exchanges--Data processing.Stock exchanges--Communication systems.Investments--Data processing.Real-time data processing.Data mining.Article10.1504/IJCSE.2023.129152