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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.Download this Resource
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File Size Small - 852 KB
Copyright NotesNon-exclusive right to publish on file at NNU Library. Available on request.
KeywordsMachine learning, Stock market, Stock screener
Degree AwardedBachelor of Science
Awarding InstitutionNorthwest Nazarene University
Committee MembersJennifer DePew, Barry L. Myers
Supervisor or ChairKevin S. McCarty
Peer Reviewed?No
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NNU Computer Science Theses
74 Resources
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- Physical Sciences and Mathematics
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