A high performance bitcoin trading strategy prediction model

YunJing Zhang; JiaXu Sun; HengBin Liu; ShaoQing Shi; QiuFeng Wang

doi:10.1117/12.2667409

28 March 2023 A high performance bitcoin trading strategy prediction model

YunJing Zhang, JiaXu Sun, HengBin Liu, ShaoQing Shi, QiuFeng Wang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125661Q (2023) https://doi.org/10.1117/12.2667409
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

First released as open source in 2009 by the pseudonym Satoshi Nakamoto, Bitcoin is the longest running and best known cryptocurrency. Bitcoin's transaction history is characterised by openness and transparency, and Bitcoin has become an important part of financial transactions. Therefore, it is increasingly important to be able to make accurate predictions about the development of the Bitcoin market. In this study, we construct a prediction model for bitcoin trading strategies based on the LightGBM algorithm, and show that our model has an accuracy of over 95.1%. The results show that our model achieves an accuracy of over 95.1% and has a higher performance compared to popular machine learning models.

Citation Download Citation

YunJing Zhang, JiaXu Sun, HengBin Liu, ShaoQing Shi, and QiuFeng Wang "A high performance bitcoin trading strategy prediction model", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125661Q (28 March 2023); https://doi.org/10.1117/12.2667409

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