Grouping Genetic Algorithm (GGA) is a concept in the algorithm of computer science. In the paper An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm(2019), the author first combined GGA and trading and defines a group trading strategy portfolio (GTSP). It is designed to optimize a trading strategy portfolio, a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively.
This paper applies the concepts of the GGA algorithm to building portfolios. In the chromosome representation, the grouping, strategy, and weight parts are employed to encode a possible GTSP. In addition, the fitness value of a chromosome is calculated by the group balance (GB), weight balance (WB), portfolio return, and risk. The rest of the concepts are same to traditional genetic algorithm.
In this report, we try to use the GGA strategy on SPY and NASDAQ-100. We used the indicators provided by the authors to build 100 strategies for experimentation and extended them. The first is to change the method of chromosome selection and introduce the concept of Epsilon Greedy. Then adjust the way to calculate the fitness value, and observe how the group balance and weighted balance mentioned in the paper affect the final result.
Overall, in this experiment, we did not find the advantage of using GGA in the portfolio. If strategies are used as investment portfolios, we find that strategies will not be triggered most of the time, and the result is like putting capital directly in cash. Besides, the calculation method of GB and WB mentioned by the author will only make the result similar to put capital directly in each strategy equally weighted. The result is shown in Figure 1.