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Pourahmadi, Zahra, Fareed, Dariush, Mirzaei, Hamid. (1402). A Novel Financial Trading System Based on Reinforcement Learning and Technical Analysis Applied on the Tehran Securities Exchange Market. سامانه مدیریت نشریات علمی, (), -. doi: 10.22054/jmmf.2023.74166.1088
Zahra Pourahmadi; Dariush Fareed; Hamid Reza Mirzaei. "A Novel Financial Trading System Based on Reinforcement Learning and Technical Analysis Applied on the Tehran Securities Exchange Market". سامانه مدیریت نشریات علمی, , , 1402, -. doi: 10.22054/jmmf.2023.74166.1088
Pourahmadi, Zahra, Fareed, Dariush, Mirzaei, Hamid. (1402). 'A Novel Financial Trading System Based on Reinforcement Learning and Technical Analysis Applied on the Tehran Securities Exchange Market', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22054/jmmf.2023.74166.1088
Pourahmadi, Zahra, Fareed, Dariush, Mirzaei, Hamid. A Novel Financial Trading System Based on Reinforcement Learning and Technical Analysis Applied on the Tehran Securities Exchange Market. سامانه مدیریت نشریات علمی, 1402; (): -. doi: 10.22054/jmmf.2023.74166.1088

A Novel Financial Trading System Based on Reinforcement Learning and Technical Analysis Applied on the Tehran Securities Exchange Market

Journal of Mathematics and Modeling in Finance
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 16 مرداد 1402
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22054/jmmf.2023.74166.1088
نویسندگان
Zahra Pourahmadi؛ Dariush Fareed* ؛ Hamid Reza Mirzaei
Faculty of Management, Economics and Accounting, Yazd University, Yazd, Iran
چکیده
Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in financial markets, the FTS utilizes reinforcement learning (RL), a subset of AI, for training. The model incorporates technical analysis and a constrained policy to enhance decision-making capabilities. The proposed algorithm is applied to the Tehran Securities Exchange, evaluating its efficiency across 45 periods using three different stock market indices. Performance comparisons are made against common strategies such as buy and hold, randomly selected actions, and maintaining the initial stock portfolio, with and without transaction costs. The results indicate that the FTS outperforms these methods, exhibiting excellent performance metrics including Sharp ratio, PP, PF, and MDD. Consequently, the findings suggest that the FTS serves as a valuable asset management tool in the Iranian financial market.
کلیدواژه‌ها
Algorithmic trading؛ Investment portfolio؛ Machine learning؛ Reinforcement learning؛ Stock exchange
آمار
تعداد مشاهده مقاله: 8


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