Research on analysis and application of quantitative investment strategies based on deep learning

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Abstract

Due to the dynamics and complexity of the stock market, stock prediction models may encounter some challenges in predicting future stock movements, resulting in their poor generalisation ability. This paper discusses the application and effectiveness of deep learning technology in the financial field by studying the quantitative investment strategy based on deep learning. First, theoretical foundations of deep learning are introduced. Then, the methods for constructing quantitative investment strategies based on Long Short-Term Memory Network (LSTM) are elaborated, including data preprocessing, model selection and training, and strategy execution. Next, the performance and stability of the strategy are evaluated through backtesting and empirical analysis of historical data. Finally, the research results are summarized, and the direction of further research and application is prospected.
基于深度学习的量化投资策略分析与应用研究
由于股票市场的动态性和复杂性,股票预测模型在预测未来股票走势时可能会遇到一些挑战,导致其泛化能力较差。本文通过对基于深度学习的量化投资策略的研究,探讨了深度学习技术在金融领域的应用及其有效性。首先,介绍了深度学习的理论基础。然后阐述了基于长短期记忆网络(LSTM)构建量化投资策略的方法,包括数据预处理、模型选择与训练以及策略执行。其次,通过回溯测试和历史数据的实证分析来评估策略的绩效和稳定性。最后,对研究成果进行了总结,并对进一步研究和应用的方向进行了展望。
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