## 1 Introduction
Additionally, this innovative method employs a distinct short MA (SMA) and long MA (LMA), represented as VMA (n1, n2), with n1 representing the n1 days for the SMA and n2 representing the n2 days for the LMA, where n2 \(>\) n1. By adopting this approach, we are able to present the results in Table 4 (comprising a variety of outcomes from the application of a number of VMA trading rules to trading ETH index futures) or Figure 2 (illustrating a variety of outcomes in a heatmap diagram with distinct colors) in Section 3. Therefore, we can achieve some superior outcomes, denoted by the color red in Table 4, that surpass the highest outcome in Table 3 using the conventional approach. As previously stated, we move our focus from stock markets to cryptocurrency markets as we investigate cryptocurrency dynamics. Following that, we raise the research questions of whether utilizing technical trading rules will increase profitability, whether we can derive as many outcomes as feasible by using technical trading rules via big data analytics, and whether we can screen these outcomes in a short time. Unlike the previous relevant literature that focused on popular cryptocurrencies (e.g., Bitcoin) and traditional technical trading rules (e.g., MA trading rule), we present a flexible VMA trading approach for trading ETH's potential, revealing numerous outcomes and highlighting the potential of big data analytics and heatmap visualization in shaping profitable trading strategies. This divergence from established methodologies, along with a thorough analysis of alternative cryptocurrency (ETH), positions our research to significantly contribute to the financial and cryptocurrency literature. In other words, this study emphasizes the substantial use of VMA trading rules and heatmap visualization [48; 49]. The study not only highlights the importance of evaluating the adjustability of the VMA rule, which has been neglected by previous research, but also employs the heatmap visualization technique, a technique that has been used infrequently in finance, to provide investors with useful information for choosing profitable trading strategies, which could potentially benefit the current body of literature in the fields of finance and cryptocurrency research. Moreover, the study investigates an alternative cryptocurrency, namely, Ethereum, providing a new perspective on the applicability of technical trading rules beyond the traditional focus on Bitcoin by emphasizing the significance of big data analytics in making informed investment decisions. This study has the potential to make significant contributions to the existing body of literature on multiple fronts. First, we present a multitude of results, specifically geometric average returns (GARs), resulting from the use of a flexible VMA trading approach, a facet often disregarded in the contemporary financial literature, due to its adaptability and the use of big data analytics. Second, we provide investors with a comprehensive perspective on trading ETH index futures by presenting numerous results through an informative heatmap diagram. This useful instrument can assist investors in identifying the appropriate VMA trading rules, thereby enhancing their potential for attaining better returns. As a result, investors may realize increased returns, possibly deriving profitable trading performance, as evidenced by the wide variety of outcomes derived from diverse VMA trading rules. Third, we contend that our research framework is likely to attract the attention of a large number of investors trading ETH index futures, given our findings that investors adopting our novel approach have the potential to achieve significantly higher returns than investors adhering to the conventional design.