The unpredictability and volatility of the stock market render it challenging
to make a substantial profit using any generalized scheme. This paper intends
to discuss our machine learning model, which can make a significant amount of
profit in the US stock market by performing live trading in the Quantopian
platform while using resources free of cost. Our top approach was to use
ensemble learning with four classifiers: 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%
profit. Finally, our work showcased that mixtures of weighted classifiers
perform better than any individual predictor about making trading decisions in
the stock market