Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and US Interest Rates

被引：2

作者：

Park, Ji-Heon ^{[1
]}

Kim, Jae-Hwan ^{[2
]}

Huh, Jun-Ho ^{[2
,3
]}

机构：

[1] Seoul Natl Univ, Grad Sch Business, Dept Business Adm, Seoul 08826, South Korea

[2] Natl Korea Maritime & Ocean Univ, Dept Data Sci, Busan 49112, South Korea

[3] Natl Korea Maritime & Ocean Univ, Interdisciplinary Major Ocean Renewable Energy Eng, Busan 49112, South Korea

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Machine learning; deep learning; reinforcement learning; artificial intelligence; deep reinforcement learning; quantitative trading; algorithmic trading; robo-advisors; assets under management; FUZZY-LOGIC;

D O I：

10.1109/ACCESS.2024.3361035

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：20705 / 20725

页数：21