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StockScreener
Performs detailed stock analysis utilizing a local AI assistant and web scraping for financial data.
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This project offers a fully local AI assistant designed for in-depth stock analysis. It leverages the Model Context Protocol (MCP) for structured tool usage, Ollama to run local large language models (specifically Qwen3), and LangChain to orchestrate the AI application. By integrating BeautifulSoup, the assistant scrapes real-time financial data from Screener.in, providing comprehensive insights into company details, profit trends, and shareholding patterns.
Data Science & ML
Analytics & Monitoring
Web Scraping & Data Collection
