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Cryptocurrency Quantitative Trading Strategy Based On Machine Learning Approach

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dc.contributor.authorFu, Dingyu
dc.date.accessioned2026-04-03T01:22:15Z
dc.date.available2026-04-03T01:22:15Z
dc.date.issued2025-06
dc.description.abstractThis thesis adopts the LSTM model to predict the price trends of cryptocurrencies by combining their historical prices with various technical indicators as features for research. We designed six control experiments to compare the impact of different technical indicators as features on the model. The final results indicate that the LSTM model combined with technical indicators can effectively improve prediction accuracy, but not all technical indicators contribute to the improvement of the model.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/23857
dc.language.isoen
dc.subjectCryptocurrencies
dc.subjectAlgorithms
dc.titleCryptocurrency Quantitative Trading Strategy Based On Machine Learning Approach
dc.typeResource Types::text::thesis::master thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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Pusat Pengajian Sains Matematik - Tesis