Kyle L. Duncan

Works by this author

Language: English

Stock Screener

Kyle L. Duncan
Thesis title page

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.
English
Type: 
Thesis
WHDL ID: 
WHDL-00021268