Authors : Aayush Chaurasia; Somangshu Chakraborty; Aayush Sinha; Yadnesh Govalkar

Volume/Issue : Volume 9 - 2024, Issue 6 - June

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Google Scholar : https://tinyurl.com/mw5j3hm4

DOI : https://doi.org/10.38124/ijisrt/24jun323

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This research illuminates the dynamic landscape of cryptocurrency markets through a comprehensive exploration of moving averages and support/resistance levels. Moving averages, both Simple (SMA) and Exponential (EMA), emerge as sophisticated tools, smoothing market volatility and revealing trends crucial for strategic decision- making. Meanwhile, the recognition and interpretation of support and resistance levels provide vital reference points, acting as stabilizing forces in the ever-changing crypto symphony.

Keywords : Data Science Pipeline, Cryptofinance, Algorithmic Trading Techniques, Trend Lines.

References :

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2. Lorenzo, L., Arroyo, J. Analysis of the cryptocurre ncy market using different prototype- based clustering techniques. Financ Innov 8, 7 (2022). https://doi.org/10.1186/s40854-021-00310-9
3. Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on Blockchain technology? - A systematic review. PLoS ONE, 11(10), e0163477. https://doi.org/10.1371/ journal.pone.0163477
4. Cryptocurrency: A Critical Analysis ofEmbedded Big Data Analytics Kamakshi Mehta1, Renu Jain*2, Prabhat Mittal3, Shikha Sharma4