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.
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Notes
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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This work was supported by Optus Investment Inc. The ICT at Seoul National University provided some research facilities for this study.
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Ha, S., Moon, BR. Finding attractive technical patterns in cryptocurrency markets. Memetic Comp. 10, 301–306 (2018). https://doi.org/10.1007/s12293-018-0252-y
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DOI: https://doi.org/10.1007/s12293-018-0252-y