Finalization and Deployment of a Stock Trading Model

Student: Kuzmin Maksim

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2024

Stock market prediction has been a hot research topic recently. Many companies have been profiting from high frequency trading from the 2010s on. However, high frequency trading relies on computational power and network speed vastly outside a private investor’s reach. Both issues can be solved by moving from high frequency trading to intraday trading, where it makes sense to perform fundamental analysis of stocks in addition to technical analysis. This project aims to enhance the candle classifier developed over the last year with news analysis and deploy the new model on an external server. News analysis will be used to attempt to factor events happening in the real world into the prediction of the stock price of a company.

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