English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62830/95882 (66%)
Visitors : 4063201      Online Users : 430
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125238


    Title: Using Big Data Analytics and Heatmap Matrix Visualization to Enhance Cryptocurrency Trading Decisions
    Authors: Ni, Yen-sen
    Keywords: big data analytics;heatmap visualization;Bollinger Bands;contrarian strategies;round-turn trading;cryptocurrency spot prices
    Date: 2023-12-23
    Issue Date: 2024-03-08 12:07:56 (UTC+8)
    Publisher: MDPI
    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.
    Relation: Applied Sciences 14(1), 154
    DOI: 10.3390/app14010154
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML7View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback