Optimal trade strategy based on RNN-LSTM deep learning method

Chengjin Sun; Songyu Wang; Qianrui Song; Shuai Zhu; Jun Heng; Lijuan Lei

doi:10.1117/12.2638942

17 May 2022 Optimal trade strategy based on RNN-LSTM deep learning method

Chengjin Sun, Songyu Wang, Qianrui Song, Shuai Zhu, Jun Heng, Lijuan Lei

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 1225956 (2022) https://doi.org/10.1117/12.2638942
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

With the development of economy, financial market has gradually become an important part of economic development. In this paper, by incorporating the financial time series data dependencies and the local correlation characteristics of the time series data of the two financial markets of gold and Bitcoin into the same model, the LSTM neural network price prediction model is constructed and the parameters are optimized to make it more accurate to predict gold, Bitcoin price trend. At the same time, using the integrated empirical mode decomposition and run-length determination method, the time series data of gold and bitcoin are divided into cycles, and the LSTM neural network model is trained and verified, and finally the gold bitcoin price prediction model is obtained, the mean absolute error and root mean square error of Bitcoin are significantly larger than those of gold, and the price volatility of Bitcoin is known to be higher than that of gold, so the instability of Bitcoin is weaker than that of gold.

Citation Download Citation

Chengjin Sun, Songyu Wang, Qianrui Song, Shuai Zhu, Jun Heng, and Lijuan Lei "Optimal trade strategy based on RNN-LSTM deep learning method", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 1225956 (17 May 2022); https://doi.org/10.1117/12.2638942

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