## 4 Empirical Results and Analyses
### Empirical Results for Numerous Outcomes with Heatmap Visualization  
To offer a thorough overview of the vast range of outcomes, we have chosen to utilize a heatmap matrix to effectively communicate our overall findings, particularly the GARs. Within the matrix, the initial column contains a range of values for n2, starting from 10 at the lower bound and extending to 180 at the upper bound. In contrast, the final row contains the range of values for n1, ranging from 5 on the leftmost end to 60 on the rightmost end. Through the analysis of the interactions between different combinations of n1 and n2, valuable insights can be obtained regarding the performance outcomes associated with the utilization of diverse VMA trading rules. Consequently, investors will be able to identify superior GARs among the multitude of GARs generated by the use of diverse VMA trading rules if they implement our novel approach. The outcomes are depicted in Table 4, which contains a 6 \(\times\) 23 heatmap matrix of VMA (n1, n2) outcomes. Here, n1 (SMA) ranges from 5 to 30 days, while n2 (LMA) is between 10 and 120 days, with a 5-day interval. Figure 2 depicts the same information visually by displaying multiple GARs in a heatmap diagram. In this diagram, dark blue represents lower GARs and brilliant yellow represents higher ones to vividly depict performance differences. In this visual representation, boosted GARs are recognizable through the presence of red colors in the heatmap matrix (Table 4) or the emergence of bright colors in the heatmap visualization (Figure 2), providing investors with an easily distinguishable overview of enhanced performance. Table 4 further provides insight into the performance, specifically the GAR, obtained by implementing the VMA (5, 160) trading rule. Notably, the GAR is 37.45%, which has been rounded to 37.5%. This corresponds to the precise GAR of 37.5% shown in Column (4) of Table 3, which corresponds to the use of the VMA (5, 160) trading rule. It is worth noting that, whereas Table 3 offers just seven findings in terms of GARs, our new approach allows us to see multiple outcomes derived from diverse VMA regulations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline
**VMA** & **No. of** & **CR (\%)** & **GAR (\%)** & **CV** & **Avg.** & **Max.** \\
**Trading** & **Trades** & **CR (\%)** & **GAR (\%)** & **CV** & **Duration** & **Duration** \\  & **Rules** & & & & **Day** & **Day** \\ \hline (5, 20) & 90 & 76,491.51 & 7.66 & 9.880 & 20 & 94 \\ (5, 40) & 48 & 78,318.44 & 14.89 & 31.574 & 37 & 181 \\ (5, 60) & 42 & 24,24.45 & 7.99 & 34.317 & 42 & 186 \\ (5, 80) & 40 & 202.58 & 2.81 & 104.214 & 43 & 209 \\ (5, 100) & 30 & 3283.26 & 12.46 & 29.391 & 57 & 271 \\ (5, 120) & 22 & 8439.74 & 22.40 & 21.583 & 75 & 287 \\ (5, 140) & 24 & 10,418.40 & 21.41 & 59.823 & 69 & 415 \\ (5, 160) & 16 & 16,134.81 & 37.45 & 37.204 & 101 & 417 \\ (5, 180) & 18 & 9862.88 & 29.13 & 40.544 & 90 & 409 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: VMA trading approaches for ETH. with a greater amount of information to help them be more profitable in ETH futures trading but also shows that the GARs within specific segments (indicated in red) consistently outperform the highest GAR yielded by the traditional design. As a result, we feel that our innovative approach is a valuable tool for investors trading ETH futures. What is more, we argue that the following disparities should be illustrated more prominently in results tables. First, the study mentions presenting results in a \"heatmap matrix\" (Table 4) and a \"heatmap diagram\" (Figure 2), both of which use color gradients to illustrate performance differences. Clarifying the discrepancies between the data presented in these two formats could make our results more impressive to those who invest in ETH futures.