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.
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Acknowledgement
Partially Funded by Science and Technology Program of Sichuan Province (2021YFG0330), partially funded by Grant SCITLAB-0001 of Intelligent Terminal Key Laboratory of SiChuan Province and partially Funded by Fundamental Research Funds for the Central Universities (ZYGX2019J076).
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Deng, H., Liu, J., Tang, Y., Lin, D., Chen, B. (2022). LSTM-Based MACD Strategy Parameter Restructuring. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_24
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DOI: https://doi.org/10.1007/978-3-031-04245-4_24
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