A Reinforcement Learning-based Encoder-Decoder Framework for Learning Stock Trading Rules
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JADM-11-1_009
تاریخ نمایه سازی: 20 فروردین 1402
Abstract:
The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.
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Authors
M. Taghian
Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran
A. Asadi
Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran
R. Safabakhsh
Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran
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