Research Article
LSTM-Based MACD Strategy Parameter Restructuring
@INPROCEEDINGS{10.1007/978-3-031-04245-4_24, author={Huan Deng and Jiali Liu and Yu Tang and Di Lin and Bo Chen}, title={LSTM-Based MACD Strategy Parameter Restructuring}, proceedings={6GN for Future Wireless Networks. 4th EAI International Conference, 6GN 2021, Huizhou, China, October 30--31, 2021, Proceedings}, proceedings_a={6GN}, year={2022}, month={5}, keywords={MACD Parameters optimization LSTM China A-Shares}, doi={10.1007/978-3-031-04245-4_24} }- Huan Deng
Jiali Liu
Yu Tang
Di Lin
Bo Chen
Year: 2022
LSTM-Based MACD Strategy Parameter Restructuring
6GN
Springer
DOI: 10.1007/978-3-031-04245-4_24
Abstract
Moving average convergence divergence (MACD) strategy has been applied in much research in financial area. Studies has demonstrated the excellent performance of the MACD strategy in quantitative investment. However, traditional parameter set (12, 26, 9) performs differently in various regions and market environments. Hence, we propose a LSTM-based method to optimize MACD strategy parameters. The proposed method offers the ability to predict advanced MACD strategy parameters in any time interval. We use all stocks from China A-Shares over the period of 2015–2020 as experiment data. We find that after applying different MACD parameter sets produced by our model, balance outperforms than the non-optimized parameter set. Our model provides an easy-to-use investment tool that discovers potential positive returns.
