Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market
<strong>Objective</strong><strong>:</strong> Presence of the considerable gap between the time of receiving the buy/sell signals and the beginning of the price change trend provides an appropriate situation for implementation of algorithmic trading systems. Tehran stock excha...
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| Format: | Article |
| Language: | fas |
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University of Tehran
2018-03-01
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| Series: | تحقیقات مالی |
| Subjects: | |
| Online Access: | https://jfr.ut.ac.ir/article_67351_41eeef47f0b95ff625619b273813882c.pdf |
| _version_ | 1818283780341760000 |
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| author | Mohammad Ali Rastegar Mohsen Dastpak |
| author_facet | Mohammad Ali Rastegar Mohsen Dastpak |
| author_sort | Mohammad Ali Rastegar |
| collection | DOAJ |
| description | <strong>Objective</strong><strong>:</strong> Presence of the considerable gap between the time of receiving the buy/sell signals and the beginning of the price change trend provides an appropriate situation for implementation of algorithmic trading systems. Tehran stock exchange is one of these markets. A high-frequency trading system has some advantages (exploiting intraday stock market volatility) and disadvantages (high amounts of transaction cost due to the high transaction volume), thus we can augment the advantages and control the disadvantages by designing the system elaborately and modifying the trading regulations.
<strong>Methods</strong><strong>:</strong> In this research, the “Local Traders” approach has been utilized to predict the future trend of stock and Reinforcement Learning has been used for dynamic portfolio management. According to the “Local Traders” approach, there is a local trader (an agent) for each stock that is expert at it. It predicts the future trend of its own stock based on stock’s intraday data and their technical indicators by determining how beneficial it is to buy, sell or hold. In this research, 2 models will be proposed based on Local Traders. Based on the first one, trades with fixed lot size were sought by exploiting the local traders’ recommendations. In the second model which is an extension of first model, one can dynamically manages the portfolio using reinforcement learning and local traders’ recommendations.
<strong>Results</strong><strong>:</strong>Results showed that, the proposed models outperformed the Buy and Hold strategy in Normal and Descending markets. Furthermore, in all kinds of markets, the second model outperformed the first one.
<strong>Conclusion</strong><strong>:</strong> Generally, the Buy and Hold strategy works the best in an Ascending market, hence the proposed algorithms are not expected to outperform this strategy. However, the performance of the proposed approach along with Neural Network method to anticipate the future trend of stocks was considerable in Normal and Descending markets. In addition, the implementation of Reinforcement Learning model to dynamically manage the portfolio has improved the results. |
| first_indexed | 2024-12-13T00:42:21Z |
| format | Article |
| id | doaj.art-519955b9af154fbab6066cd0d91bd64f |
| institution | Directory Open Access Journal |
| issn | 1024-8153 2423-5377 |
| language | fas |
| last_indexed | 2024-12-13T00:42:21Z |
| publishDate | 2018-03-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | تحقیقات مالی |
| spelling | doaj.art-519955b9af154fbab6066cd0d91bd64f2022-12-22T00:05:06ZfasUniversity of Tehranتحقیقات مالی1024-81532423-53772018-03-0120111610.22059/jfr.2017.230613.100641567351Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock MarketMohammad Ali Rastegar0Mohsen Dastpak1Assistant Prof., Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, IranM.Sc. in Financial Engineering, Faculty of Financial Science, Kharazmi University, Tehran, Iran<strong>Objective</strong><strong>:</strong> Presence of the considerable gap between the time of receiving the buy/sell signals and the beginning of the price change trend provides an appropriate situation for implementation of algorithmic trading systems. Tehran stock exchange is one of these markets. A high-frequency trading system has some advantages (exploiting intraday stock market volatility) and disadvantages (high amounts of transaction cost due to the high transaction volume), thus we can augment the advantages and control the disadvantages by designing the system elaborately and modifying the trading regulations. <strong>Methods</strong><strong>:</strong> In this research, the “Local Traders” approach has been utilized to predict the future trend of stock and Reinforcement Learning has been used for dynamic portfolio management. According to the “Local Traders” approach, there is a local trader (an agent) for each stock that is expert at it. It predicts the future trend of its own stock based on stock’s intraday data and their technical indicators by determining how beneficial it is to buy, sell or hold. In this research, 2 models will be proposed based on Local Traders. Based on the first one, trades with fixed lot size were sought by exploiting the local traders’ recommendations. In the second model which is an extension of first model, one can dynamically manages the portfolio using reinforcement learning and local traders’ recommendations. <strong>Results</strong><strong>:</strong>Results showed that, the proposed models outperformed the Buy and Hold strategy in Normal and Descending markets. Furthermore, in all kinds of markets, the second model outperformed the first one. <strong>Conclusion</strong><strong>:</strong> Generally, the Buy and Hold strategy works the best in an Ascending market, hence the proposed algorithms are not expected to outperform this strategy. However, the performance of the proposed approach along with Neural Network method to anticipate the future trend of stocks was considerable in Normal and Descending markets. In addition, the implementation of Reinforcement Learning model to dynamically manage the portfolio has improved the results.https://jfr.ut.ac.ir/article_67351_41eeef47f0b95ff625619b273813882c.pdfalgorithmic tradingdynamic portfolio managementhigh-frequency tradingintra-day datareinforcement learning |
| spellingShingle | Mohammad Ali Rastegar Mohsen Dastpak Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market تحقیقات مالی algorithmic trading dynamic portfolio management high-frequency trading intra-day data reinforcement learning |
| title | Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market |
| title_full | Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market |
| title_fullStr | Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market |
| title_full_unstemmed | Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market |
| title_short | Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market |
| title_sort | developing a high frequency trading system with dynamic portfolio management using reinforcement learning in iran stock market |
| topic | algorithmic trading dynamic portfolio management high-frequency trading intra-day data reinforcement learning |
| url | https://jfr.ut.ac.ir/article_67351_41eeef47f0b95ff625619b273813882c.pdf |
| work_keys_str_mv | AT mohammadalirastegar developingahighfrequencytradingsystemwithdynamicportfoliomanagementusingreinforcementlearninginiranstockmarket AT mohsendastpak developingahighfrequencytradingsystemwithdynamicportfoliomanagementusingreinforcementlearninginiranstockmarket |