Date of Award
12-2019
Degree Type
Creative Project
Degree Name
Master of Science (MS)
Department
Economics and Finance
Committee Chair(s)
Tyler Brough
Committee
Tyler Brough
Committee
Vicki Allan
Abstract
This research applies a deep reinforcement learning technique, Deep Q-network, to a stock market pairs trading strategy for profit. Artificial intelligent methods have long since been applied to optimize trading strategies. This work trains and tests a DQN to trade co-integrated stock market prices, in a pairs trading strategy. The results demonstrate the DQN is able to consistently produce positive returns when executing a pairs trading strategy.
Recommended Citation
Brim, Andrew, "Deep Reinforcement Learning Pairs Trading" (2019). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1425.
https://digitalcommons.usu.edu/gradreports/1425
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .