I already hold a ChatGPT Pro subscription and several paid equity-market data feeds. What I need now is a Python-based framework that can run on autopilot—regularly pulling fresh market data, doing light preprocessing, and handing it off to large-language-model tools such as LangChain for summarisation or simple analytics. Scope (first milestone) • Build a minimal yet modular pipeline that: – Connects to one chosen API, one sample CSV/Excel file and one local database table. – Normalises the pulled data to a common schema and stores it in a lightweight repository (SQLite or similar). – Exposes a single command (or scheduled script) to trigger the full fetch-clean-summarise loop without manual intervention. – Returns concise, human-readable output (e.g., daily market recap) to the console or a text file. Key requirements • Written entirely in Python. • Clear folder structure so I can bolt on extra sources such as PDF filings, company websites or social-media sentiment later. • Config file or ENV variables for credentials—no hard-coding. • Inline comments and a short README that explains how to extend each module. Nice to have • Hooks for LangChain agents or OpenAI functions so the summarizer can be swapped or expanded easily. • Simple logging so I can monitor failures and successes. Deliverables 1. Well-documented source code repository. 2. README covering setup, scheduled execution, and how to plug in new data connectors. 3. A brief walk-through video or call to ensure hand-off is smooth.