Zenan Ji1, Yihong Zhang2
1School of Data Science, Zhejiang University of Finance & Economics, Hangzhou, 310018, China
2School of Science, North China Institute of Science & Technology, Langfang, 065201, China
Cryptocurrencies have taken an increasingly important place in the international financial markets. However, due to the complexity of the factors affecting cryptocurrencies and the high volatility of cryptocurrency prices, predicting the future trend of cryptocurrencies has been a difficult topic. In this paper, we establish a prediction model based on LightGBM, aiming to find the optimal machine learning algorithm to predict different cryptocurrencies. Firstly, we preprocessed the data to ensure data integrity and availability. Secondly, we trained the training set using the LighGBM algorithm. Then, to find the optimal LightGBM algorithm for different cryptocurrencies, we optimized the algorithm using Grid Search. Finally, we evaluated the results by Root Mean Squared Error (RMSE). It shows that the Root Mean Squared Error of the prediction results for all cryptocurrencies is below 8%, which means LightGBM can be a good choice for monitoring cryptocurrencies.
Cryptocurrency; Residual log-returns; LightGBM; Grid Search
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