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Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning
- Source :
- IEEE Access, Vol 9, Pp 152310-152321 (2021)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Algorithmic trading allows investors to avoid emotional and irrational trading decisions and helps them make profits using modern computer technology. In recent years, reinforcement learning has yielded promising results for algorithmic trading. Two prominent challenges in algorithmic trading with reinforcement learning are (1) extracting robust features and (2) learning a profitable trading policy. Another challenge is that it was previously often assumed that both long and short positions are always possible in stock trading; however, taking a short position is risky or sometimes impossible in practice. We propose a practical algorithmic trading method, SIRL-Trader, which achieves good profit using only long positions. SIRL-Trader uses offline/online state representation learning (SRL) and imitative reinforcement learning. In offline SRL, we apply dimensionality reduction and clustering to extract robust features whereas, in online SRL, we co-train a regression model with a reinforcement learning model to provide accurate state information for decision-making. In imitative reinforcement learning, we incorporate a behavior cloning technique with the twin-delayed deep deterministic policy gradient (TD3) algorithm and apply multistep learning and dynamic delay to TD3. The experimental results show that SIRL-Trader yields higher profits and offers superior generalization ability compared with state-of-the-art methods.
- Subjects :
- reinforcement learning
imitation learning
General Computer Science
business.industry
Computer science
Dimensionality reduction
Feature extraction
General Engineering
deep learning
Machine learning
computer.software_genre
TK1-9971
Generalization (learning)
Position (finance)
Reinforcement learning
General Materials Science
Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
Algorithmic trading
state representation learning
business
Cluster analysis
computer
Computer technology
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....d4757cf07942051d2dba353f4f2ad040