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.

Features

Selective section extraction: Only Item 7 (MD&A) and Item 8 (Financial Statements) are sent to the API; PART I and Items 1–6 are pre-filtered to reduce tokens.
Smart chunking: Long sections are trimmed to head + tail to stay within token limits while keeping high-signal content.
Two-step progress: The UI shows Step 1 (download + extract) and Step 2 (Gemini analysis) so you can see where time is spent.
Analysis only mode: Optional single API call (summary + CFA report only) to reduce rate-limit issues.
S&P 500 reference list: A table of company names and tickers (sample) is shown at the bottom of the page for quick lookup.

Typical run time: About 1–2 minutes (roughly 1 minute with “Analysis only” enabled; up to 2 minutes with metrics). If the API is rate-limited, the app waits 60 seconds and retries automatically.

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)

Recent updates

Selective extraction & pre-filtering: Regex-based extraction of Item 7 and Item 8 only; content before Item 7 is dropped to cut token use.
Smart chunking: Sections over ~30k characters are reduced to head + tail before sending to Gemini.
Progress steps: Step 1 (download + extract) and Step 2 (Gemini analysis, ~30–90s) with clear spinner messages.
S&P 500 list: Bottom of the page now includes a sample table of S&P 500 companies with company name and ticker for easy reference.
Run time: Results usually appear within 1–2 minutes under normal conditions.

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.

README.md Unescape Escape

10-K Financial Analyzer