Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms

Yi Feng Chen*, Wen-Yueh Shih, Hsu Chao Lai, Hao Chun Chang, Jiun Long Huang

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Pairs trading is a market-neutral quantitative trading strategy which exploits historically correlated stock prices by forming pairs with weighted long and short positions. A pair of opened offsetting positions can profit regardless of positive or negative price trend. Positions are opened when the spread exceeds the trading boundary, and closed when the spread reverts back to the historical mean. In this paper, we adopt proximal policy optimization, which is a deep reinforcement learning algorithm, to determine the trading boundaries as well as stop loss boundaries for maximizing the profit in pairs trading. Besides, we propose to utilize a demonstration butter to pre-train the model for better training efficacy. The experimental results manifest that the proposed method outperforms state-of-the-art strategies in terms of investment return and investment risk in both the Taiwan stock market and the United States stock market.

原文English
主出版物標題Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
編輯Hyeran Byun, Beng Chin Ooi, Katsumi Tanaka, Sang-Won Lee, Zhixu Li, Akiyo Nadamoto, Giltae Song, Young-guk Ha, Kazutoshi Sumiya, Wu Yuncheng, Hyuk-Yoon Kwon, Takehiro Yamamoto
發行者Institute of Electrical and Electronics Engineers Inc.
頁面40-47
頁數8
ISBN(電子)9781665475785
DOIs
出版狀態Published - 2023
事件2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 - Jeju, Korea, Republic of
持續時間: 13 2月 202316 2月 2023

出版系列

名字Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

Conference

Conference2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
國家/地區Korea, Republic of
城市Jeju
期間13/02/2316/02/23

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