Optimizing Algorithmic Strategies for Trading Bitcoin

Gil Cohen ()
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Gil Cohen: Western Galilee Academic College

Computational Economics, 2021, vol. 57, issue 2, No 11, 639-654

Abstract: 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.

Keywords: Algorithmic trading; Oscillators; Trading strategies; Optimizations (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-020-09972-6

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