Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates
With the development of artificial intelligence, there have been many attempts to incorporate artificial intelligence into algorithmic trading. In particular, reinforcement learning, which aims to solve dynamic decision-making problems, is attracting attention because of its high utilization in algo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10419157/ |
| _version_ | 1797316095300861952 |
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| author | Ji-Heon Park Jae-Hwan Kim Jun-Ho Huh |
| author_facet | Ji-Heon Park Jae-Hwan Kim Jun-Ho Huh |
| author_sort | Ji-Heon Park |
| collection | DOAJ |
| description | With the development of artificial intelligence, there have been many attempts to incorporate artificial intelligence into algorithmic trading. In particular, reinforcement learning, which aims to solve dynamic decision-making problems, is attracting attention because of its high utilization in algorithmic trading. In this paper, we will implement a simple Deep Reinforcement Learning (DRL) trading robot to check the performance of DRL. In addition, we tried to find out how much performance improvement can be achieved by comparing a robot that learned a single stock data with a robot that learned stock data, market index, and interest rate data. This paper aims to develop a stock investment robot using a Proximal Policy Optimization (PPO) reinforcement learning algorithm and analyze the performance of the robot. The first robot used only the stock data of APPL INC, a single stock, as input, and the second robot used stock data of APPL INC and the S&P 500 index together with US interest rate data as input data. Afterward, the stock investment performance of the two robots for APPL INC was comparatively analyzed using the test data. |
| first_indexed | 2024-03-08T03:13:17Z |
| format | Article |
| id | doaj.art-e9ceac1cfddc44c9bf2a1d6cbb7848c9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T03:13:17Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e9ceac1cfddc44c9bf2a1d6cbb7848c92024-02-13T00:01:33ZengIEEEIEEE Access2169-35362024-01-0112207052072510.1109/ACCESS.2024.336103510419157Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest RatesJi-Heon Park0Jae-Hwan Kim1https://orcid.org/0000-0002-8248-6352Jun-Ho Huh2https://orcid.org/0000-0002-8248-6352Department of Business Administration, Graduate School of Business, Seoul National University, Seoul, Republic of KoreaDepartment of Data Science, (National) Korea Maritime and Ocean University, Busan, Republic of KoreaDepartment of Data Science, (National) Korea Maritime and Ocean University, Busan, Republic of KoreaWith the development of artificial intelligence, there have been many attempts to incorporate artificial intelligence into algorithmic trading. In particular, reinforcement learning, which aims to solve dynamic decision-making problems, is attracting attention because of its high utilization in algorithmic trading. In this paper, we will implement a simple Deep Reinforcement Learning (DRL) trading robot to check the performance of DRL. In addition, we tried to find out how much performance improvement can be achieved by comparing a robot that learned a single stock data with a robot that learned stock data, market index, and interest rate data. This paper aims to develop a stock investment robot using a Proximal Policy Optimization (PPO) reinforcement learning algorithm and analyze the performance of the robot. The first robot used only the stock data of APPL INC, a single stock, as input, and the second robot used stock data of APPL INC and the S&P 500 index together with US interest rate data as input data. Afterward, the stock investment performance of the two robots for APPL INC was comparatively analyzed using the test data.https://ieeexplore.ieee.org/document/10419157/Machine learningdeep learningreinforcement learningartificial intelligencedeep reinforcement learningquantitative trading |
| spellingShingle | Ji-Heon Park Jae-Hwan Kim Jun-Ho Huh Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates IEEE Access Machine learning deep learning reinforcement learning artificial intelligence deep reinforcement learning quantitative trading |
| title | Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates |
| title_full | Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates |
| title_fullStr | Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates |
| title_full_unstemmed | Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates |
| title_short | Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates |
| title_sort | deep reinforcement learning robots for algorithmic trading considering stock market conditions and u s interest rates |
| topic | Machine learning deep learning reinforcement learning artificial intelligence deep reinforcement learning quantitative trading |
| url | https://ieeexplore.ieee.org/document/10419157/ |
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