## 4 Results
### Results and Discussion  
First, we want to study the benefits of each proposed approach. In both cases, the algorithms used are PESA-II, PAES, NSGA-II, SPEA-II, and MoCell. _Approach A_ comprises non-dominated solutions of the Pareto front of the set of all selected evolutionary algorithms, while _Approach B_ is formed by those non-dominated solutions belonging to the Pareto front of any of the above algorithms. In both cases, all solutions are used to operate. Figures 5-7 show the performance obtained for the compared methods and datasets. is favored by maximizing the number of trade opeartions. SPEA-II is now the only one of the individual algorithms that comes close to the result of SET MOEAS, with PAES again being the worst performer, at around 5.6%. Finally, Figure 7 shows the results for Dataset 3, which corresponds to 675,000 data from 22 March to 5 May 2023. This dataset includes data from a mixed environment, in which there are periods of greater dynamism or high variability (bullish) and periods of greater tranquillity (neutral). In this case, the results are very similar to those of Dataset 2, probably due to the influence of the periods of lower stability that affect the algorithms that do not collaborate in the search for the Pareto front. In all cases, we can appreciate the superiority of the approach proposed in this paper. The results show that the best values are obtained by Method B (the collaborative SET MOEAS) compared to any of the isolated evolutionary algorithms in _Approach A_. Although a comprehensive analysis of the importance of the different algorithms in the SET MOEAS collaboration is needed, the results clearly indicate that this inter-algorithm collaboration obtains a larger Pareto front and thus provides a more robust set of solutions in terms of its applicability. Therefore, it is concluded that the proposed scheme represents a good model that can be applied to use different investment strategies in the stock market. Figure 5: Total yield of the different approaches, in percentage, for Dataset 1. Figure 6: Total yield of the different approaches, in percentage, for Dataset 2. Figures 8-10 show the number of solutions obtained in a complete experiment. The graphs show a comparison between the number of solutions obtained and those that actually represent market transactions, i.e., an entry or an exit. The bar marks the mean value line for 30 runs and the segment indicates the standard deviation. Figure 8 shows these values for Dataset 1. We can see that there is no direct relationship between the number of solutions obtained and the number of solutions that perform operations, beyond the fact that, obviously, the number of operations is lower than the number of solutions. There is also no clear trend between the number of solutions generated and the profit obtained, nor between the number of solutions causing operations and the profit. We can see that the number of non-dominated solutions generated by SET MOEAS is slightly higher than that of those generated by the other algorithms alone. However, it should be noted that this figure does not indicate the width of the final Pareto front, but the number of non-dominated solutions that were generated during the evolutionary process. Similar conclusions can be obtained from Figures 9 and 10. Also, we see that for _Approach B_, the number of _business operations_is higher than that of the other approaches. This is a feature that makes the proposed method better than others. Figure 11 shows this fact in more detail. Moreover, the influence of different datasets in terms of variation of the solutions obtained is irrelevant. Figure 12 shows the average profit a solution has for each buy/sell operation made while it is active. There are variations in the results obtained by different evolutionary algorithms, although not statistically significant. However, the results obtained by the two proposed methods are similar. Let us compare the benefits obtained by _Approach B_ with those of other techniques.