Version 1
: Received: 9 October 2024 / Approved: 9 October 2024 / Online: 9 October 2024 (10:40:18 CEST)
How to cite:
Zhao, F.; Zhang, M.; Zhou, S.; Lou, Q. Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management. Preprints2024, 2024100682. https://doi.org/10.20944/preprints202410.0682.v1
Zhao, F.; Zhang, M.; Zhou, S.; Lou, Q. Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management. Preprints 2024, 2024100682. https://doi.org/10.20944/preprints202410.0682.v1
Zhao, F.; Zhang, M.; Zhou, S.; Lou, Q. Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management. Preprints2024, 2024100682. https://doi.org/10.20944/preprints202410.0682.v1
APA Style
Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management. Preprints. https://doi.org/10.20944/preprints202410.0682.v1
Chicago/Turabian Style
Zhao, F., Shiji Zhou and Qi Lou. 2024 "Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management" Preprints. https://doi.org/10.20944/preprints202410.0682.v1
Abstract
With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.
Keywords
Deep reinforcement learning (DRL); Long short-term memory Network (LSTM); The cryptocurrency market; Risk management time series forecasting
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
