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Application of deep reinforcement learning for Indian stock trading automation

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  • Supriya Bajpai

Abstract

In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards. In the present paper the theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets. The experiments are performed systematically with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten Indian stock datasets. The performance of the models are evaluated and comparison is made.

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  • Supriya Bajpai, 2021. "Application of deep reinforcement learning for Indian stock trading automation," Papers 2106.16088, arXiv.org.
    • Handle: RePEc:arx:papers:2106.16088
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    File URL: http://arxiv.org/pdf/2106.16088
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    References listed on IDEAS

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    1. Ioannis Boukas & Damien Ernst & Thibaut Th'eate & Adrien Bolland & Alexandre Huynen & Martin Buchwald & Christelle Wynants & Bertrand Corn'elusse, 2020. "A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding," Papers 2004.05940, arXiv.org.
    2. Jinho Lee & Raehyun Kim & Yookyung Koh & Jaewoo Kang, 2019. "Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network," Papers 1902.10948, arXiv.org.
    3. Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.
    4. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    5. S. G. M. Fifield & D. M. Power & D. G. S. Knipe, 2008. "The performance of moving average rules in emerging stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 18(19), pages 1515-1532.
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    Cited by:

    1. Yuling Huang & Xiaoping Lu & Chujin Zhou & Yunlin Song, 2023. "DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy," Mathematics, MDPI, vol. 11(17), pages 1-27, August.

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