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Intraday algorithmic trading strategies for cryptocurrencies

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  • Gil Cohen

    (Western Galilee Academic College)

Abstract

This research is the first attempt to create Machine Learning (ML) algorithmic systems that would be able to intraday trade automatically popular cryptocurrencies using oscillators that are commonly used to trade other financial assets. It uses intraday price data of Bitcoin, Ethereum, Binance Coin, Cardano, and XRP with different trading time frames that vary from 5 to 180 min. Our results show that the RSI system is the best algorithmic trading system for cryptocurrency intraday trading. The RSI-based system has out beat the B&H strategy for all five cryptocurrencies. Moreover, it has proven its ability to improve trading performances under any market conditions up or down trends. The other two trading systems that were based on the MACD and Keltner Channels oscillators were found to outperform the B&H strategy for Bitcoin, Binance Coin, and XRP while it was beaten by the B&H strategy for Ethereum and Cardano. Although cryptocurrencies are known for their high volatility, this research has proven that longer time frames such as 60 and 120 min have produced better trading results than shorter time frames such as 5 and 15 min.

Suggested Citation

  • Gil Cohen, 2023. "Intraday algorithmic trading strategies for cryptocurrencies," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 395-409, July.
    • Handle: RePEc:kap:rqfnac:v:61:y:2023:i:1:d:10.1007_s11156-023-01139-2
    DOI: 10.1007/s11156-023-01139-2
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    References listed on IDEAS

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    1. John M. Griffin & Amin Shams, 2020. "Is Bitcoin Really Untethered?," Journal of Finance, American Finance Association, vol. 75(4), pages 1913-1964, August.
    2. Caporale, Guglielmo Maria & Plastun, Alex, 2019. "The day of the week effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 31(C).
    3. Blau, Benjamin M., 2018. "Price dynamics and speculative trading in Bitcoin," Research in International Business and Finance, Elsevier, vol. 43(C), pages 15-21.
    4. Yue Liu & Aijun Yang & Jijian Zhang & Jingjing Yao, 2020. "An Optimal Stopping Problem of Detecting Entry Points for Trading Modeled by Geometric Brownian Motion," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 827-843, March.
    5. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    6. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    7. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    8. 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.
    9. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
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