Abstract
We optimized the Sure-Fire statistical arbitrage policy, set three different actions, encoded the continuous price over some time into a heat-map view of the Gramian Angular Field (GAF), and compared the Deep Q Learning (DQN) and Proximal Policy Optimization (PPO) algorithms. To test feasibility, we analyzed three currency pairs, namely EUR/USD, GBP/USD, and AUD/USD. We trained the data in units of four hours from 1 August 2018 to 30 November 2018 and tested model performance using data between 1 December 2018 and 31 December 2018. The test results of the various models indicated that favorable investment performance achieves as long as the model can handle complex and random processes, and the state can describe the environment, validating the feasibility of reinforcement learning in the development of trading strategies.
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Tsai, YC., Wang, CC., Szu, FM., Wang, KJ. (2020). Deep Reinforcement Learning for Foreign Exchange Trading. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_34
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DOI: https://doi.org/10.1007/978-3-030-55789-8_34
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