An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm
Journal article, Peer reviewed
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OriginalversjonChen, C.-H., Chen, Y.-H., Lin, J. C.-W., & Wu, M.-E. (2019). An effective approach for obtaining a group trading strategy portfolio using grouping genetic algorithm. IEEE Access, 7, 7313-7325. 10.1109/ACCESS.2018.2889737
To determine an appropriate trading time for buying or selling stocks is always a difficult task. The common way to deal with it is using trading strategies formed by technical or fundamental indicators. Lots of approaches have been presented on how to form trading strategies and how to set suitable parameters for those strategies. Furthermore, some approaches were also designed to optimize a trading strategy portfolio, which is a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively. To provide a more useful trading strategy portfolio, we first define a group trading strategy portfolio (GTSP). Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem. In the chromosome representation, the grouping, strategy, and weight parts are employed to encode a possible GTSP. The fitness value of a chromosome is calculated by the group balance, weight balance, portfolio return, and risk to assess the quality of every possible solution. Genetic operators, including crossover, mutation, and inversion, are applied on the population to form a new offspring. Evolution is continued until the stop conditions are reached. Lastly, experiments were conducted on two real datasets with different trends to show that the advantages and the effectiveness of the presented approach.