The DOI matches its Crossref record; 30 of the 33 references it cites were found in a registry or an index.
Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach
Open accessAbstract
In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and finding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Specifically, considering the noisy financial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real financial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.
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Cites
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AI in Finance: Challenges, Techniques, and Opportunities
Cao, L. (2022) · ACM Computing Surveys · doi:10.1145/3502289 · Cited by 567
Sahu, S. K., Mokhade, A., & Bokde, N. D. (2023) · Applied Sciences · doi:10.3390/app13031956 · Cited by 238
Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects
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Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach
Sawhney, R., Agarwal, S., Wadhwa, A., Derr, T., & Shah, R. R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16127 · Cited by 171
Wang, Z., Huang, B., Tu, S., Zhang, K., & Xu, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16144 · Cited by 150
Metadata
- Authors
- Liu, Y., Liu, Q., Zhao, H., Pan, Z., & Liu, C.
- Affiliations
- University of Science and Technology of China (CN)
- Tianjin University (CN)
- University of Tennessee at Knoxville (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 02
- Pages
- 2128-2135
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997874851
- Cited by
- 141
- References
- 33
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 39 · 2023: 26 · 2022: 25 · 2021: 19 · 2020: 1
- FWCI
- 24.10
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61922073, 71790594, U1605251, 61672483)
- Youth Innovation Promotion Association
- 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 › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Keywords
- quantitative trading, deep reinforcement learning, imitation learning, partially observable Markov decision processes, exploration-exploitation balance, high-frequency financial data
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