A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm
Heuristic methods or evolutionary algorithms (such as genetic algorithms and genetic programs) are common approaches applied in financial applications, such as trading systems. Determining the best time to buy or sell stocks in a stock market, and thereby maximizing profit with low risks, is an impo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2014-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/6883114/ |
| _version_ | 1818618665071804416 |
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| author | Yao-Hsin Chou Shu-Yu Kuo Chi-Yuan Chen Han-Chieh Chao |
| author_facet | Yao-Hsin Chou Shu-Yu Kuo Chi-Yuan Chen Han-Chieh Chao |
| author_sort | Yao-Hsin Chou |
| collection | DOAJ |
| description | Heuristic methods or evolutionary algorithms (such as genetic algorithms and genetic programs) are common approaches applied in financial applications, such as trading systems. Determining the best time to buy or sell stocks in a stock market, and thereby maximizing profit with low risks, is an important issue in financial research. Recent studies have used trading rules based on technique analysis to address this problem. This method can determine trading times by analyzing the value of technical indicators. In other words, we can make trading rules by finding the trading value of technique indicators. An example of a trading rule would be, if one technical indicator's value achieves the setting value, then either buy or sell. A combination of trading rules would become a trading strategy. The process of making trading strategies can be formulated as a combinational optimization problem. In this paper, we propose a novel method for applying a trading system. First, the proposed method uses the quantum-inspired Tabu search algorithm to find the optimal composition and combination of trading strategies. Second, this method uses a sliding window to avoid the major problem of over-fitting. The experiment results of earning money show much better performance than other approaches, and the proposed method outperforms the buy and hold method (which is a benchmark in this field). |
| first_indexed | 2024-12-16T17:25:12Z |
| format | Article |
| id | doaj.art-53b8dcb4d6764339a22f3030c56aa9f3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:25:12Z |
| publishDate | 2014-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-53b8dcb4d6764339a22f3030c56aa9f32022-12-21T22:23:04ZengIEEEIEEE Access2169-35362014-01-01288389610.1109/ACCESS.2014.23522616883114A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search AlgorithmYao-Hsin Chou0Shu-Yu Kuo1Chi-Yuan Chen2Han-Chieh Chao3Department of Computer Science and Information Engineering, National Chi Nan University, Nantou, TaiwanDepartment of Computer Science and Information Engineering, National Chi Nan University, Nantou, TaiwanDepartment of Computer Science and Information Engineering, National Ilan University, Yilan, TaiwanDepartment of Computer Science and Information Engineering, National Ilan University, Yilan, TaiwanHeuristic methods or evolutionary algorithms (such as genetic algorithms and genetic programs) are common approaches applied in financial applications, such as trading systems. Determining the best time to buy or sell stocks in a stock market, and thereby maximizing profit with low risks, is an important issue in financial research. Recent studies have used trading rules based on technique analysis to address this problem. This method can determine trading times by analyzing the value of technical indicators. In other words, we can make trading rules by finding the trading value of technique indicators. An example of a trading rule would be, if one technical indicator's value achieves the setting value, then either buy or sell. A combination of trading rules would become a trading strategy. The process of making trading strategies can be formulated as a combinational optimization problem. In this paper, we propose a novel method for applying a trading system. First, the proposed method uses the quantum-inspired Tabu search algorithm to find the optimal composition and combination of trading strategies. Second, this method uses a sliding window to avoid the major problem of over-fitting. The experiment results of earning money show much better performance than other approaches, and the proposed method outperforms the buy and hold method (which is a benchmark in this field).https://ieeexplore.ieee.org/document/6883114/ |
| spellingShingle | Yao-Hsin Chou Shu-Yu Kuo Chi-Yuan Chen Han-Chieh Chao A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm IEEE Access |
| title | A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm |
| title_full | A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm |
| title_fullStr | A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm |
| title_full_unstemmed | A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm |
| title_short | A Rule-Based Dynamic Decision-Making Stock Trading System Based on Quantum-Inspired Tabu Search Algorithm |
| title_sort | rule based dynamic decision making stock trading system based on quantum inspired tabu search algorithm |
| url | https://ieeexplore.ieee.org/document/6883114/ |
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