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Salimi-Badr, Armin, Sarlakifar, Faezeh, Mohammadzadeh Asl, Mohammadreza, Rezvani Khaledi, Sajjad. (1403). A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks. , 2(Special Issues 2), 1-7. doi: 10.48308/jicse.2025.239844.1077
Armin Salimi-Badr; Faezeh Sarlakifar; Mohammadreza Mohammadzadeh Asl; Sajjad Rezvani Khaledi. "A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks". , 2, Special Issues 2, 1403, 1-7. doi: 10.48308/jicse.2025.239844.1077
Salimi-Badr, Armin, Sarlakifar, Faezeh, Mohammadzadeh Asl, Mohammadreza, Rezvani Khaledi, Sajjad. (1403). 'A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks', , 2(Special Issues 2), pp. 1-7. doi: 10.48308/jicse.2025.239844.1077
Salimi-Badr, Armin, Sarlakifar, Faezeh, Mohammadzadeh Asl, Mohammadreza, Rezvani Khaledi, Sajjad. A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks. , 1403; 2(Special Issues 2): 1-7. doi: 10.48308/jicse.2025.239844.1077

A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks

Journal of Innovations in Computer Science and Engineering (JICSE)
مقاله 12، دوره 2، Special Issues 2 - شماره پیاپی 4، فروردین 2025، صفحه 1-7    اصل مقاله (926.52 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.48308/jicse.2025.239844.1077
نویسندگان
Armin Salimi-Badr* 1؛ Faezeh Sarlakifar2؛ Mohammadreza Mohammadzadeh Asl2؛ Sajjad Rezvani Khaledi3
1دانشگاه شهید بهشتی
2Faculty of Computer Science and Engineering Shahid Beheshti University Tehran, Iran
3Faculty of Electrical Engineering Shahid Beheshti University Tehran, Iran
چکیده
Abstract—Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short-Term Memory (xLSTM) network in combination with a deep reinforcement learning (DRL) approach for automated stock trading. Our proposed method utilizes xLSTM networks in both actor and critic components, enabling effective handling of time series data and dynamic market environments. Proximal Policy Optimization (PPO), with its ability to balance exploration and exploitation, is employed to optimize the trading strategy. Experiments were conducted using financial data from major tech companies over a comprehensive timeline, demonstrating that the xLSTM-based model outperforms LSTM-based methods in key trading evaluation metrics, including cumulative return, average profitability per trade, maximum earning rate, maximum pullback, and Sharpe ratio. These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems.
کلیدواژه‌ها
Keywords—Extended Long Short-Term Memory (xLSTM)؛ Proximal Policy Optimization (PPO)؛ Automated Stock Trading؛ Actor-Critic Reinforcement Learning
آمار
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