Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms

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

doi:10.1109/BigComp57234.2023.00015

Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Editors	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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	40-47
Number of pages	8
ISBN (Electronic)	9781665475785
DOIs	https://doi.org/10.1109/BigComp57234.2023.00015
State	Published - 2023
Event	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 - Jeju, Korea, Republic of Duration: 13 Feb 2023 → 16 Feb 2023

Publication series

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

Conference

Conference	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Country/Territory	Korea, Republic of
City	Jeju
Period	13/02/23 → 16/02/23

Keywords

Deep reinforcement learning
pairs trading
proximal policy optimization
quantitative trading

Access to Document

10.1109/BigComp57234.2023.00015

Cite this

Chen, Y. F., Shih, W.-Y., Lai, H. C., Chang, H. C., & Huang, J. L. (2023). Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms. In H. Byun, B. C. Ooi, K. Tanaka, S.-W. Lee, Z. Li, A. Nadamoto, G. Song, Y. Ha, K. Sumiya, W. Yuncheng, H.-Y. Kwon, & T. Yamamoto (Eds.), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 (pp. 40-47). (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp57234.2023.00015

Chen, Yi Feng ; Shih, Wen-Yueh ; Lai, Hsu Chao et al. / Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. editor / 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. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 40-47 (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023).

@inproceedings{329f03d37dee40c3ae442f2ea78a6c16,

title = "Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms",

abstract = "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.",

keywords = "Deep reinforcement learning, pairs trading, proximal policy optimization, quantitative trading",

author = "Chen, {Yi Feng} and Wen-Yueh Shih and Lai, {Hsu Chao} and Chang, {Hao Chun} and Huang, {Jiun Long}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",

year = "2023",

doi = "10.1109/BigComp57234.2023.00015",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "40--47",

editor = "Hyeran Byun and Ooi, {Beng Chin} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

address = "美國",

}

Chen, YF, Shih, W-Y, Lai, HC, Chang, HC & Huang, JL 2023, Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms. in H Byun, BC Ooi, K Tanaka, S-W Lee, Z Li, A Nadamoto, G Song, Y Ha, K Sumiya, W Yuncheng, H-Y Kwon & T Yamamoto (eds), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Institute of Electrical and Electronics Engineers Inc., pp. 40-47, 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Jeju, Korea, Republic of, 13/02/23. https://doi.org/10.1109/BigComp57234.2023.00015

Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms. / Chen, Yi Feng; Shih, Wen-Yueh; Lai, Hsu Chao et al.
Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. ed. / 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. Institute of Electrical and Electronics Engineers Inc., 2023. p. 40-47 (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms

AU - Chen, Yi Feng

AU - Shih, Wen-Yueh

AU - Lai, Hsu Chao

AU - Chang, Hao Chun

AU - Huang, Jiun Long

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Deep reinforcement learning

KW - pairs trading

KW - proximal policy optimization

KW - quantitative trading

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DO - 10.1109/BigComp57234.2023.00015

M3 - Conference contribution

AN - SCOPUS:85151567982

T3 - Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

SP - 40

EP - 47

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

A2 - Byun, Hyeran

A2 - Ooi, Beng Chin

A2 - Tanaka, Katsumi

A2 - Lee, Sang-Won

A2 - Li, Zhixu

A2 - Nadamoto, Akiyo

A2 - Song, Giltae

A2 - Ha, Young-guk

A2 - Sumiya, Kazutoshi

A2 - Yuncheng, Wu

A2 - Kwon, Hyuk-Yoon

A2 - Yamamoto, Takehiro

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

Y2 - 13 February 2023 through 16 February 2023

ER -

Chen YF, Shih WY, Lai HC, Chang HC, Huang JL. Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms. In Byun H, Ooi BC, Tanaka K, Lee SW, Li Z, Nadamoto A, Song G, Ha Y, Sumiya K, Yuncheng W, Kwon HY, Yamamoto T, editors, Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 40-47. (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023). doi: 10.1109/BigComp57234.2023.00015

Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms

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