Using Big Data Analytics and Heatmap Matrix Visualization to Enhance Cryptocurrency Trading Decisions

Abstract

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.

Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives

“…Bollinger Bands assess volatility and identify overbought and oversold conditions by utilizing a moving average and two standard deviations above and below it. Traders can then use these bands to generate buy or sell signals based on price movements about the bands [57,58].…”

“…The BB consists of an MA and two outer bands set at standard deviations from the MA, aiding traders in spotting volatility and potential overbought or oversold scenarios. Movement beyond these bands could hint at either the continuation or reversal of the present trend [57,58,80].…”

“…Traders use momentum oscillators like the RSI and SOI to recognize overbuying and overselling [29,30]. Bollinger Bands consist of volatility bands positioned above and below a moving average, with price shifts outside these bands potentially signaling trend reversals [57,80]. Contrarian strategies are valuable for traders seeking to profit from short-term price corrections or trend reversals in energy and financial markets.…”

“…For instance, candlestick charts are used in technical analysis to show price trends and reversals [71]. Similarly, heatmaps can visually depict market volatility and price changes across various periods and asset categories since heatmap visualization may help traders make proper decisions [39,42,52,57]. Furthermore, advancements in data visualization technologies, such as augmented reality (AR) and virtual reality (VR), hold promise for enhancing traders' ability to analyze and interpret complex market data [174,175].…”

“…In addition, some technical trading rules would use two parameters. For example, variable lag moving average (VMA) trading rules such as VMA (5,20) using five days as short-term MA and 20 days as long-term MA [42,119] and Bollinger Band (BB) trading strategies such as BBTS (20, 2) that uses 20-day moving average and two standard deviations [57]. Following the wisdom mentioned above, we may use diverse VMA (n1, n2) using different n1 and n2 and varied BBTS (m1, m2) using various m1 and m2 and then present their overall performance (cumulative holding performance and average holding performance) in heatmap matrix based on mixed combinations of these two parameters.…”