Alam, Md. Golam RabiulUllah, A. K. M. Amanat2021-10-182021-10-1820212021-08ID 20166016http://hdl.handle.net/10361/15326Cataloged from PDF version of internship report.Includes bibliographical references (pages 67-74).This project report is submitted in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Science and Engineering, 2021.The unpredictability and volatility of the stock market render it challenging to make a substantial pro t using any generalized scheme. Many previous studies tried di erent techniques to build a machine learning model, which can make a signi cant pro t in the US stock market by performing live trading. However, very few studies have focused on the importance of nding 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 classi ers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization 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% pro t. We further propose a novel model which uses Ada-boost to nd the weights of each of the features and then apply TOPSIS to select the best stocks. Lastly, we survey the machine learning techniques used for ethical decision-making in stock trading, which will bene t any further research work on Responsible AI in Finance.74 pagesenBrac University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Feature selectionMulti criteria decision theoryComputational financeAutomated tradingResponsible AIFinance -- Data processingProject Report