Optimized pairs-trading strategies in the cryptocurrencies market using genetic algorithms and cointegration

Lorette Danilo, Fayssal Jamhamed and Franck Martin
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Lorette Danilo: PhD Student, Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes France
Fayssal Jamhamed: Quantitative Portfolio Manager, Federal Finance Gestion

Economics Working Paper Archive (University of Rennes & University of Caen) from Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS

Abstract: Cryptoassets market is notoriously volatile and risky. In this context, marketneutral type strategies, such as pair-trading, may be relevant. In this article, we focus on the implementation of pair-trading strategies with a wide range of cryptoassets (209) over a period halved from 2021-08-01 to 2024-01-31. To carry out this study, we combine econometric and machine learning techniques to stand out from the existing literature. By using cointegration tests and error correction models, we identify a final sample of 229 pairs suitable for pair-trading strategies. Using a genetic algorithm and pair clustering, we test four strategies employing standard and optimized thresholds. The results highlight the existence of profitable cointegrating relationships and therefore short-term market inefficiencies in the cryptoassets market. Indeed, the best strategy identified in terms of risk-return couple, although it remains risky with a median maxdrawdown of 29%, delivers an average annual Sharpe ratio per pair of 1.53 over the backtesting period.

Keywords: Cryptoassets; Pair-trading; Cointegration; Error-correction models; Genetic algorithm; Bollinger bands; Short-term market inefficiencies (search for similar items in EconPapers)
JEL-codes: C22 C61 G11 G12 G14 (search for similar items in EconPapers)
Date: 2024-11
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