[1] T. Theate, and D. Ernst, “An application of deep reinforcement learning to algorithmic trading,” Expert Systems with Applications, vol. 173, p. 114632, Jul. 2021.
[2] Y. Huang, X. Wan, L. Zhang, and X. Lu, “A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading,” Expert Systems with
[3] X. Cheng, J. Zhang, Y. Zeng, and W. Xue, “MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading,” Lecture Notes in Computer Science, Springer, Singapore, 2024, vol. 14648, pp. 30-42.
[4] Z. Huang, N. Li, W. Mei, and W. Gong, “Algorithmic trading using combinational rule vector and deep reinforcement learning,” Applied Soft Computing, vol. 147, pp. 110802–110802, No. 2023.
[5] Shavandi and M. Khedmati, “A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets,” Expert Systems with Applications, p. 118124, Jul. 2022.
[6] B. Hirchoua, B. Ouhbi, and B. Frikh, “Deep reinforcement learning based trading agents: Risk curiosity driven learning for financial rules-based policy,” Expert Systems with Applications, vol. 170, p. 114553, May 2021, doi: https://doi.org/10.1016/j.eswa.2020.114553.
[7] M. Taghian, A. Asadi, and R. Safabakhsh, “A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules,” pp. 1–39, 2021, [Online]. Available: http://arxiv.org/abs/2101.03867.
[8] S. Carta, A. Ferreira, A. S. Podda, D. Reforgiato Recupero, and A. Sanna, “Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting,” Expert Systems with Applications, vol. 164, p. 113820, Feb. 2021, doi: https://doi.org/10.1016/j.eswa.2020.113820.
[9] D. Kanzari and Y. Ridha Ben Said, “A complex adaptive agent modeling to predict the stock market prices,” Expert Systems with Applications, vol. 222, p. 119783, Jul. 2023, doi: https://doi.org/10.1016/j.eswa.2023.119783.
[10] B. Yang, T. Liang, J. Xiong, and C. Zhong, “Deep reinforcement learning based on transformer and U-Net framework for stock trading,” Knowledge-Based Systems, vol. 262, p. 110211, Feb. 2023.
[11] Z. Zhang, S. Zohren, and S. Roberts, “Deep Reinforcement Learning for Trading,” The Journal of Financial Data Science, vol. 2, no. 2, pp. 25–40, Mar. 2020, doi: https://doi.org/10.3905/jfds.2020.1.030.
[12] N. majidi, M. Shamsi, and F. Marvasti, “Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning,” SSRN Electronic Journal, 2022, doi: https://doi.org/10.2139/ssrn.4276310.
[13] Mahdi Massahi, and Masoud Mahootchi, “A deep Q-learning based algorithmic trading system for commodity futures markets,” Expert systems with applications, vol. 237, pp. 121711–121711, Mar. 2024. doi: https://doi.org/10.1016/j.eswa.2023.121711.
[14] R. S. Sutton, Reinforcement Learning: An Introduction, Second Edition, Cambridge, Massachusetts: The Mit Press, 2018.
[15] E. F. Fama, “The Behavior of Stock-Market Prices,” The Journal of Business, vol. 38, no. 1, pp. 34–105, Jan. 1965, doi: https://doi.org/10.1086/294743.
[16] H. Van Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q-Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, Mar. 2016, doi: https://doi.org/10.1609/aaai.v30i1.10295.
[17] O. B. Sezer and A. M. Ozbayoglu, “Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach,” Applied Soft Computing, vol. 70, pp. 525–538, Sep. 2018, doi: https://doi.org/10.1016/j.asoc.2018.04.024.
[18] T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, Oct. 2018.
[19] M. Wiese, R. Knobloch, R. Korn, and P. Kretschmer, “Quant GANs: deep generation of financial time series,” Quantitative Finance, vol. 20, no. 9, pp. 1419–1440, Apr. 2020.
[20] J. Moody, L. Wu, Y. Liao, and M. Saffell, “Performance functions and reinforcement learning for trading systems and portfolios,” Journal of Forecasting, vol. 17, no. 56, pp. 441–470, Sep. 1998.
[21] V. Mnih, K. Kavukcuoglu, D. Silver, et al., “Playing Atari with Deep Reinforcement Learning,” pp. 1–9, 2013, [Online]. Available: http://arxiv.org/abs/1312.5602.
[22] D. Silver, et al., “Mastering the game of Go without human knowledge,” Nature, vol. 550, no. 7676, pp. 354–359, Oct. 2017, doi: https://doi.org/10.1038/nature24270.
[23] H. Van Hasselt, “Double Q-learning,” in Conf. Neural Inf. Process Syst, NIPS, 2010, pp. 1–9.
[24] Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep Direct Reinforcement Learning for Financial Signal Representation and Trading,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 653–664, Mar. 2017. doi:http://doi.org/10.1109/tnnls.2016.252240
[25] M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting: A literature review,” Expert Systems with Applications, vol. 197, p. 116659, Jul. 2022, doi: https://doi.org/10.1016/j.eswa.2022.116659.
[26] S. Fallahpour, H. Hakimian, “Paired Trading Strategy Optimization Using the Reinforcement Learning Method: Intraday Data of Tehran Stock Exchange” Financial Research Journal, vol. 21, no. 1, pp. 19-34, May 2019. [In Persian]
[27] B. Sabahi, “Designing Trading Strategies Based on Deep Reinforcement Leaerning Methodes” M.S thesis, Department of Financial Management, Tehran Univ. Technol., Tehran, 2022. [In Persian]