Date of Award:
Master of Science (MS)
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).
Dasgupta, Agnibh, "Deep Q Learning Applied to Stock Trading" (2020). All Graduate Theses and Dissertations. 7983.
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