  In this paper, we implement a combination of technical analysis and
machine/deep learning-based analysis to build a trend classification model. The
goal of the paper is to apprehend short-term market movement, and incorporate
it to improve the underlying stochastic model. Also, the analysis presented in
this paper can be implemented in a \emph{model-independent} fashion. We execute
a data-science-driven technique that makes short-term forecasts dependent on
the price trends of current stock market data. Based on the analysis, three
different labels are generated for a data set: $+1$ (buy signal), $0$ (hold
signal), or $-1$ (sell signal). We propose a detailed analysis of four major
stocks- Amazon, Apple, Google, and Microsoft. We implement various technical
indicators to label the data set according to the trend and train various
models for trend estimation. Statistical analysis of the outputs and
classification results are obtained.
