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.
 C. -H. Wu, C. -C. Lu, Y. -F. Ma and R. -S. Lu, "A New Forecasting Framework for Bitcoin Price with LSTM," 2018 IEEE International Conference on Data Mining Workshops (ICDMW)
 Y. Zhou and C. Zhao, "Quantitative fund transaction research based on fractional order neural network and Deep-Q Network," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2020, pp. 257-260, doi: 10.1109/MLBDBI51377.2020.00054.
 Goel, H., and Singh, N.P. (2022), "Dynamic prediction of Indian stock market: an artificial neural network approach", International Journal of Ethics and Systems, 38(1), 35-46.
 Fenu, Gianni and Simone Surcis. “A Cloud Computing Based Real Time Financial System.” 2009 Eighth International Conference on Networks (2009): 374-379.
 Mcnally, Sean et al. “Predicting the Price of Bitcoin Using Machine Learning.” 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (2018): 339-343.