用大模型实现实时股票分析，零成本部署且开源可查。
A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study
- 基于Gemini API做定性分析，通过GitHub Actions自动处理数据。
- 系统近零成本运行，支持实时股票分析并公开可用。
- 详细记录调试过程，适合想学AI金融工具开发的人参考。
强大的通用大语言模型（如Google的Gemini）为普及金融数据分析带来了新机遇。本文记录了一个新型无服务器架构的实时股票分析系统的构建与迭代调试全过程。系统利用Gemini API进行定性评估，通过GitHub Actions实现数据自动采集与处理，并以解耦的静态前端展示结果。文章详述了系统从初步构想到成熟事件驱动流水线的演进过程，重点分析了部署中遇到的实际挑战。论文大量篇幅聚焦于调试案例，涵盖常见软件错误、平台权限问题及罕见的环境级平台漏洞。最终架构实现近乎零成本运行，证明个人可构建复杂的AI金融工具。该应用已公开可访问，源代码全部开放。文末讨论了大模型在金融分析中的角色、稳健调试方法的重要性，以及人机协同开发的新范式。
原文摘要 · Abstract (English)
The advent of powerful, accessible Large Language Models (LLMs) like Google's Gemini presents new opportunities for democratizing financial data analysis. This paper documents the design, implementation, and iterative debugging of a novel, serverless system for real-time stock analysis. The system leverages the Gemini API for qualitative assessment, automates data ingestion and processing via GitHub Actions, and presents the findings through a decoupled, static frontend. We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline, highlighting the practical challenges encountered during deployment. A significant portion of this paper is dedicated to a case study on the debugging process, covering common software errors, platform-specific permission issues, and rare, environment-level platform bugs. The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools. The operational application is publicly accessible, and the complete source code is available for review. We conclude by discussing the role of LLMs in financial analysis, the importance of robust debugging methodologies, and the emerging paradigm of human-AI collaboration in software development.
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