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Memetic Computing
Erschienen in: Memetic Computing 3/2018

09.03.2018 | Regular Research Paper

Finding attractive technical patterns in cryptocurrency markets

verfasst von: Sungjoo Ha, Byung-Ro Moon

Erschienen in: Memetic Computing | Ausgabe 3/2018

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Abstract

The cryptographic currency market is an emerging venue for traders looking to diversify their investments. We investigate the use of genetic programming (GP) for finding attractive technical patterns in a cryptocurrency market. We decompose the problem of automatic trading into two parts, mining useful signals and applying them to trading strategies, and focus our attention on the former. Extensive experiments are performed to analyze the factors that affect the quality of the solutions found by the proposed GP system. With the introduction of domain knowledge through extended function sets and the inclusion of diversity preserving mechanism, we show that the proposed GP system successfully finds attractive technical patterns. Out-of-sample performance of the patterns indicates that the GP consistently finds signals that are profitable and frequent. A trading simulation with the generated patterns suggests that the captured signals are indeed useful for portfolio optimization.
Vorheriger Artikel PSO with surrogate models for feature selection: static and dynamic clustering-based methods
Nächster Artikel A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows

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Fußnoten
2
The authors are aware that multiple hypothesis testing can be handled more rigorously. We’ve decided to keep it simple and provide the result of the t test as a weak evidence.
 
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Metadaten
Titel
Finding attractive technical patterns in cryptocurrency markets
verfasst von
Sungjoo Ha
Byung-Ro Moon
Publikationsdatum
09.03.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 3/2018
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-018-0252-y

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