10-K Financial Analyzer

A web app that fetches the latest 10-K from SEC EDGAR for a given stock ticker and uses a hybrid architecture: qualitative analysis (Item 7 MD&A only) via Google Gemini, and quantitative metrics (Revenue, Net Income, Operating Cash Flow) from yfinance. CFA-style report and key financials in one place.

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Features

• Hybrid processing (qualitative + quantitative):
  • Qualitative: Only Item 7 (MD&A) is sent to Gemini for analysis of management’s strategy, market risks, and sentiment—no Item 8 (financial statements) to the AI, which cuts token use and avoids number hallucination.
  • Quantitative: Financial metrics (Revenue, Net Income, Operating Cash Flow) are fetched directly from yfinance—fast, accurate, and no extra API tokens.
• HTML cleansing: Before sending Item 7 to the LLM, the app strips remaining HTML tags, collapses whitespace, and removes page numbers to compress tokens.
• Selective extraction: The 10-K is parsed with regex; only content from Item 7 onward is used for AI; PART I and Items 1–6 are dropped.
• Smart chunking: Long Item 7 text is trimmed to head + tail to stay within token limits.
• Two-step progress: Step 1 (download + extract Item 7), Step 2 (Gemini analysis + yfinance metrics).
• Analysis only mode: Optional hide for the metrics table (Gemini still runs once on Item 7).
• S&P 500 reference list: Sample table of company names and tickers at the bottom for quick lookup.

Typical run time: About 1–2 minutes (one Gemini call; yfinance metrics are near-instant). If the API is rate-limited, the app waits 60 seconds and retries automatically.

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Tech Stack

• UI: Streamlit
• Data: sec-edgar-downloader (SEC EDGAR), yfinance (financial metrics)
• AI: Google Gemini (google-generativeai)
• Parsing / cleansing: BeautifulSoup, regex

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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. The current design uses a hybrid architecture that separates qualitative and quantitative work.

Implemented Solution:

• Selective section extraction: A regex-based parser isolates Item 7 (MD&A) only for the AI; Item 8 is no longer sent to the LLM.
• Hybrid processing:
  • Qualitative (Gemini): Item 7 only—strategy, risks, and sentiment. This drastically reduces tokens and avoids AI errors on exact figures.
  • Quantitative (yfinance): Revenue, Net Income, and Operating Cash Flow are pulled from yfinance, so numbers are accurate and no tokens are spent on financial tables.
• HTML cleansing: Before sending Item 7 to Gemini, the app runs a cleansing step (BeautifulSoup + regex) to strip tags, collapse whitespace, and drop page numbers, further compressing tokens.
• Chunking: Long Item 7 text is trimmed to head + tail to stay within token limits.
• Efficiency: Token consumption is greatly reduced (one API call; no Item 8 in the prompt), and numeric accuracy is guaranteed via yfinance.

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

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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

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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.

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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)

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Recent updates

• Hybrid architecture: Item 7 (MD&A) only is sent to Gemini for qualitative analysis (strategy, risks, sentiment). Financial metrics (Revenue, Net Income, Operating Cash Flow) come from yfinance—no Item 8 to the AI, fewer tokens, and accurate numbers.
• HTML cleansing: Pre-LLM step strips HTML remnants, extra whitespace, and page numbers to compress Item 7 text before sending to Gemini.
• Selective extraction: Regex-based extraction of Item 7 only for the API; content before Item 7 is dropped.
• Smart chunking: Item 7 over ~20k characters is reduced to head + tail before sending to Gemini.
• Progress steps: Step 1 (download + extract Item 7), Step 2 (Gemini qualitative analysis + yfinance metrics) with clear spinner messages.
• S&P 500 list: Sample table of S&P 500 companies (company name and ticker) at the bottom for quick reference.
• Run time: One Gemini call plus instant yfinance data; results typically within 1–2 minutes under normal conditions.

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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.