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Optimizing Algorithmic Strategies for Trading Bitcoin.

Authors :
Cohen, Gil
Source :
Computational Economics; Feb2021, Vol. 57 Issue 2, p639-654, 16p
Publication Year :
2021

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09277099
Volume :
57
Issue :
2
Database :
Complementary Index
Journal :
Computational Economics
Publication Type :
Academic Journal
Accession number :
149172722
Full Text :
https://doi.org/10.1007/s10614-020-09972-6