RETRACTED ARTICLE: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Chun-Hao Chen (), Yu-Hsuan Chen, Vicente Garcia Diaz () and Jerry Chun-Wei Lin ()
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Chun-Hao Chen: National Taipei University of Technology
Yu-Hsuan Chen: Tamkang University
Vicente Garcia Diaz: University of Oviedo
Jerry Chun-Wei Lin: Western Norway University of Applied Sciences

Electronic Commerce Research, 2023, vol. 23, issue 1, No 2, 3-42

Abstract: Abstract It is always difficult and challenge to obtain suitable trading signals for the desired securities in financial markets. The popular way to deal with it is through the use of trading strategies (TSs) made up of technical or fundamental indicators. Due to the different properties of TSs, an algorithm was proposed to find trading signals by obtaining the group trading strategy portfolio (GTSP), which is composed of strategy groups that can be employed to generate various TS portfolios (TSP) instead of a single TS. The stop-loss and take-profit points (SLTP) are widely utilized by shareholders to avoid massive losses. However, the appropriate SLTP is hard to set by users. Therefore, in this paper, the algorithm, namely GTSP-SLTP algorithm, is proposed to not only obtain a reliable GTSP but also find appropriate SLTP using the grouping genetic algorithm. A chromosome is encoded by the generated SLTP and GTSP along with the weights for strategy groups that are the SLTP, grouping, weight, and strategy parts. To assess the goodness of a chromosome, the evaluation function that consists of the group balance, weight balance, risk factor, and profit factor, is employed. Genetic operators are then performed to produce new solutions for next population. The genetic process is performed iteratively until the stop conditions have achieved. Last but not the least, empirical experiments were conducted on three financial datasets with different trends and a case study is also given to reveal the effectiveness and robustness of the designed GTSP-SLTP algorithm.

Keywords: Grouping problem; Grouping genetic algorithm; Portfolio optimization; Stop-loss; Take-profit; Trading strategy (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10660-021-09467-y

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