A Comprehensive Trading Strategy Model for Forecasting and Scheme-Planning

DOI

10.2991/978-94-6463-005-3_30How to use a DOI?

Keywords

CNN; LSTM; Actor-Critic; DDPG; Mean semi-variance model

Abstract

Due to the complexity and variability of the financial market, the current model can not fully cover all aspects of investment trends, and there is room for improvement in forecasting accuracy and decision-making. Based on this, we propose a CNN-LSTM prediction method combining RMSE loss, and use DDPG algorithm based on Actor-Critic framework to make decision. This method innovatively combines RMSE loss functions of two models. In order to prove the rationality of this model, we choose several models to compare with it, and finally come to the conclusion that our model has a good feasibility and universality in prediction and decision-making.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xinyu Zhang
AU  - Yuhang Wu
AU  - Zexuan Li
PY  - 2022
DA  - 2022/11/10
TI  - A Comprehensive Trading Strategy Model for Forecasting and Scheme-Planning
BT  - Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
PB  - Atlantis Press
SP  - 303
EP  - 315
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-005-3_30
DO  - 10.2991/978-94-6463-005-3_30
ID  - Zhang2022
ER  -