Date of Award:
12-2020
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Nicholas Flann
Committee
Nicholas Flann
Committee
John Edwards
Committee
Curtis Dyreson
Abstract
Developing a strategy for stock trading is a vital task for investors. However, it is challenging to obtain an optimal strategy, given the complex and dynamic nature of the stock market. This thesis aims to explore the applications of Reinforcement Learning with the goal of maximizing returns from market investment, keeping in mind the human aspect of trading by utilizing stock prices represented as candlestick graphs. Furthermore, the algorithm studies public interest patterns in form of graphs extracted from Google Trends to make predictions. Deep Q learning has been used to train an agent based on fused images of stock data and Google trends data via a convolution neural network (CNN).
Checksum
c5dc6e06e625c9001c7472def9bf60de
Recommended Citation
Dasgupta, Agnibh, "Deep Q Learning Applied to Stock Trading" (2020). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 7983.
https://digitalcommons.usu.edu/etd/7983
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