Stock Trend Analysis and Report Generation Assistant for Investors

Added Jun 2025 3 design docs

Retail investors and junior analysts spend evenings doing what institutional research desks do with entire teams: tracking price movements, reading company news, and assembling the picture into a view they can act on. The raw information is abundant; the time to structure it is not. A tool that automates trend analysis and drafts readable research reports would close much of that gap. The intern builds that tool as a Python web service on Flask. Market data and company documents are ingested and indexed with LlamaIndex so the system can retrieve relevant context on demand, and OpenAI models generate the analytical layer: trend summaries for selected tickers, explanations of notable movements grounded in the indexed material, and full report drafts combining the two. A user profile system personalizes the output, letting each user register their watchlist, risk tolerance, and preferred report depth so the same engine produces meaningfully different briefings for different investors. The intern implements the data ingestion, the retrieval and generation pipeline, and the profile-driven report customization. Completing the project demonstrates retrieval-augmented generation over financial data, document indexing with LlamaIndex, personalization architecture, and the judgment required to present AI-generated financial analysis responsibly.

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