A web app that fetches the latest 10-K from SEC EDGAR for a given stock ticker, then uses Item 7 (MD&A) and Item 8 (Financial Statements) to produce a CFA-style analysis and key metrics. Powered by Google Gemini.

Tech Stack

UI: Streamlit
Data: sec-edgar-downloader (SEC EDGAR)
AI: Google Gemini (google-generativeai)

Technical Challenge: Handling Large-Scale Financial Filings

During the initial development, I encountered a 429 Resource Exhausted error due to the massive size of 10-K filings exceeding the LLM's token quota and rate limits.

Consultation & Architectural Pivot:
After consulting with a senior software engineer, I re-architected the application to optimize token usage. Instead of processing the entire document, I implemented a "Selective Section Extraction" strategy.

Implemented Solution:

Targeted Parsing: Developed a regex-based parser to isolate only critical sections: Item 7 (MD&A) and Item 8 (Financial Statements).
Token Optimization: Integrated a "Chunking & Filtering" logic to remove boilerplate legal text, sending only high-signal data to the Gemini API.
Efficiency: This reduced token consumption by over 80%, ensuring stable performance within free-tier limits while maintaining analytical depth.

For full technical notes and code references, see TECHNICAL_NOTES.md.

Requirements

Python 3.9+
Google API Key (Gemini)
An email address for SEC EDGAR (required for programmatic access)
Optional: .env with GOOGLE_API_KEY and SEC_EDGAR_EMAIL

How to Run

cd "/path/to/FQDC Project"
python3 -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt
streamlit run app.py

Open the sidebar to set Google API Key and SEC EDGAR Email, then enter a ticker (e.g. AAPL, MSFT) and click Run Analysis. Use Analysis only (1 API call) if you hit rate limits.

Project Structure

├── app.py              # Streamlit app (Gemini)
├── requirements.txt    # Python dependencies
├── .env.example        # Example env vars (copy to .env)
├── README.md           # This file
└── TECHNICAL_NOTES.md  # Technical challenge & solution (for reference)

License and Disclaimer

This project is for learning and portfolio use. Comply with SEC policy when using SEC data and with Google's terms for the Gemini API.