Scholarlink

This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed model’s user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Manoswita Bose,
  • Moumita Chatterjee,
  • Dhrubasish Sarkar

摘要

This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed model’s user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications.