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The Complexity of Cryptocurrencies Algorithmic Trading

Author

Listed:
  • Gil Cohen

    (Department of Management, Western Galilee Academic College, Acre 2412101, Israel)

  • Mahmoud Qadan

    (School of Business Administration, University of Haifa, Haifa 3498838, Israel)

Abstract

In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.

Suggested Citation

  • Gil Cohen & Mahmoud Qadan, 2022. "The Complexity of Cryptocurrencies Algorithmic Trading," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2037-:d:837057
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    References listed on IDEAS

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    2. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    3. Andreas Karathanasopoulos & Christian Dunis & Samer Khalil, 2016. "Modelling, forecasting and trading with a new sliding window approach: the crack spread example," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1875-1886, December.
    4. Brandvold, Morten & Molnár, Peter & Vagstad, Kristian & Andreas Valstad, Ole Christian, 2015. "Price discovery on Bitcoin exchanges," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 36(C), pages 18-35.
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    Cited by:

    1. Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.

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