The objective of my capstone project was to explore data-driven methods for the purpose of finding profitable insights for trading stocks in the stock market. For many years analysts have attempted to use technical analysis to try to make decisions in the stock market. There are mixed opinions on the validity of technical analysis and its ability to produce profitable results. I wanted to test some of these traditional technical methods on actual stock data to see if profitable results could be obtained. In addition to testing traditional technical methods, I also wanted to leverage the use the latest data science tools to gather insights with stock market trading. Some of the tools and methods that I used in this project were python, linear programming, time series analysis, ordinary least squares, support vector machines, and random forests.