Development and Design of an Intelligent Financial Asset Management System Based on Big Data Analysis and Kubernetes

The rise of deep learning in the financial field has led to the integration of artificial intelligence and investment, providing users with intelligent investment decisions. However, the data volume of financial market continues to expand, traditional data processing methods can no longer meet the needs of efficiency and accuracy. This article focuses on deep reinforcement learning algorithms and delves into key issues such as stock price prediction, investment portfolios, and algorithmic trading. By comparing and analyzing the experimental results, not only was the performance of the model evaluated, but also the actual effect of the algorithm output was deeply explored. At the same time, drawing on Kubernetes container orchestration and microservice technology, a high concurrency and high-performance distributed financial data analysis system was constructed. This system not only meets the needs of users for real-time data analysis and deep learning, but also provides more reasonable investment suggestions for users. The contribution of this article lies in introducing deep reinforcement learning to solve nonlinear data problems in the financial field, proposing intelligent asset management methods, and designing a feasible intelligent financial asset management system, providing new ideas and practical experience for the further development of financial data analysis platforms.