Cryptocurrency Quantitative Trading Strategy Based On Machine Learning Approach

Fu, Dingyu (2025) Cryptocurrency Quantitative Trading Strategy Based On Machine Learning Approach. Masters thesis, Universiti Sains Malaysia.

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Abstract

This 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.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA1 Mathematics (General)
Divisions: Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis
Depositing User: Mr Aizat Asmawi Abdul Rahim
Date Deposited: 03 Apr 2026 01:29
Last Modified: 03 Apr 2026 01:29
URI: http://eprints.usm.my/id/eprint/63845

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