Quantitative Trading on Stock Market Based on Deep Reinforcement Learning
Abstract
With the development of computer science technology and artificial intelligence, quantitative trading attracts more investors due to its efficiency and stable performance. In this paper, we explore the potential of deep reinforcement learning in quantitative trading. A LSTM-based agent is proposed to learn the temporal pattern in data and automatically trades according to the current market condition and the historical data. The input to the agent is the raw financial data and the output of the agent is decision of trading. The goal of the agent is to maximize the ultimate profit. Besides, to reduce the influence of noise in the market and to improve the performance of the agent, we use several technical indicators as an extra input. The proposed system has been back-tested on the stock market. The results demonstrate that our method performs well in most conditions.
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For DRL strategy design, existing works studied algorithmic trading on a daily time-frame or hourly time-frame, which is hard to fully capture the dynamics of a highly volatile market.
“For DRL strategy design, existing works studied algorithmic trading on a daily time-frame [2, 25, 26, 42-45] or hourly time-frame [14], which is hard to fully capture the dynamics of a highly volatile market.”
From FinRL-podracer: high performance and scalable deep reinforcement learning for quantitative finance (2021) · cites this paper as [42]
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FinRL-podracer: high performance and scalable deep reinforcement learning for quantitative finance
Li, Z., Liu, X.-Y., Zheng, J., Wang, Z., Walid, A., & Guo, J. (2021) · Proceedings of the Second ACM International Conference on AI in Finance · doi:10.1145/3490354.3494413 · Cited by 28 · see citing works
- Authors
- WU, J., WANG, C., XIONG, L., & SUN, H.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- ORCID
- Venue
- 2019 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 1-8
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2977551188
- Cited by
- 42
- References
- 31
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2023: 7 · 2022: 11 · 2021: 5 · 2020: 5
- FWCI
- 7.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Finance › Stochastic processes and financial applications
- Keywords
- quantitative trading, deep reinforcement learning, stock market, technical indicators, trading decision-making, backtesting, market noise
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