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Optimizing Algorithmic Strategies for Trading Bitcoin

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  • Gil Cohen

    (Western Galilee Academic College)

Abstract

This research tries to establish to what extent three popular algorithmic systems for trading financial assets: the relative strength index, the moving average convergence diversion (MACD) and the pivot reversal (PR), are suitable for Bitcoin trading. Using data about daily Bitcoin prices from the beginning of April 2013 until the end of October 2018, we explored these strategies through particle swarm optimization. Our results demonstrate that the relative strength index produced poorer results than the buy and hold strategy. In contrast, the MACD and PR strategies dramatically outperformed the buy and hold strategy. However, our optimizing process produced even better results.

Suggested Citation

  • Gil Cohen, 2021. "Optimizing Algorithmic Strategies for Trading Bitcoin," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 639-654, February.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:2:d:10.1007_s10614-020-09972-6
    DOI: 10.1007/s10614-020-09972-6
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    References listed on IDEAS

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    Cited by:

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