WHDL - 00013550

Stock Screener

Kyle L. Duncan

Description / Abstract

Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science

The stock market is rapidly changing, most stock screeners or services allow for visualization of stock tickers using charts. These charts are used by stock traders to analyze tickers they are interested in; the issue is that a human can only adequately track a few charts at a time. The purpose of this application is to address this issue. Specifically, this project utilizes machine learning to aid in increasing a stock trader’s ability to analyze the stock market. The machine learning model used in the application was trained using a random forest classifier that utilizes historical data. Using an application programming interface from Polygon, the stock screener receives real-time stock quotes which are then prepared and classified by the probability of increasing or decreasing in value. The classified data is then displayed on the user interface in a heatmap, and updates as soon as new data is classified. Displaying the data as a heatmap allows for an intuitive way to quickly analyze many stock tickers. The result of this application is a realtime stock screener tool that can be used to quickly visualize tickers and the probability of each ticker’s movement.

Stock Screener

Description / Abstract

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About the Authors

Kyle L. Duncan