Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050614.
Jingreng Lei1, Qianhui Lin2
1School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China
2School of Mathematics(Zhuhai), Sun Yat-sen University, Guangzhou, 510006, China
As a new investment method, quantitative investment is expanding its market scale and share due to its stable investment performance. In this paper we propose a prediction model based on LSTM. It is helpful for the traders to predict the future price to formulate the best trading strategy. Using this model, we can precisely forecast each price separately to determine when the asset should be traded based on future price fluctuations. Simulation results show that our model can successfully predict the future price trend of the two assets within the acceptable range of error, which helps us to better optimize our portfolio. In addition, the RMSE (root mean square error) is selected as the loss function to describe the accuracy of our prediction model.
Trading Strategy; Long Short Term Memory(LSTM); Deep Learning
Jingreng Lei, Qianhui Lin. Analysis of gold and bitcoin price prediction based on LSTM model. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 95-100. https://doi.org/10.25236/AJCIS.2022.050614.
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