Case Study: Stock Analyzer – Building an independent financial analysis workstation
Fundamental analysis of US public companies listed on NYSE/NASDAQ requires access to advanced financial data and hundreds of hours spent reading annual (Form 10-K) and quarterly (10-Q) reports. Subscriptions to professional analytical services cost from several hundred to several thousand dollars per year.
Project Goal: Building a complete, autonomous, and ultra-fast analytical workstation with zero subscription costs for market data, reducing the verification time of a new company from 3 hours to under 45 seconds using AI language models.
1. Proprietary GPS Scoring Model (Growth Potential Scale)
To structure the analysis, I designed and implemented an algorithm evaluating each company against 6 strategic, weighted factors:
- Revenue Dynamics and Margins (25% weight): Verifying revenue CAGR and stability of operating margins and Free Cash Flow (FCF).
- Backlog / Pipeline Coverage (20% weight): Analyzing contracted revenues and sales pipeline potential for subsequent quarters.
- TAM Market Expansion (20% weight): Investigating Total Addressable Market and geographical or product expansion paths.
- Growth Catalyst Strength (15% weight): Identifying key factors stimulating demand for products/services in the near term.
- Financial Liquidity and Runway (10% weight): Assessing cash reserves, net debt levels, and financial stability during economic downturns.
- Moat Competitive Advantage (10% weight): Identifying barriers to entry for competitors (e.g. switching costs, patents, economies of scale).
2. yfinance and Claude AI Integration
The engine fetches real-time financial statements (balance sheet, income statement, cash flows) directly from Yahoo Finance servers using the `yfinance` library.
Then, when the user provides a ticker (e.g. "MU.US" for Micron Technology), the system starts an AI agent connected to the Anthropic API. The agent retrieves the latest Earnings Call transcripts, analyzes management statements for risks, and automatically populates the GPS scorecard along with the investment thesis and risk notes.
3. Retro TUI (Terminal User Interface)
Instead of building heavy, slow web pages, I designed a lightweight, responsive console interface supporting the 256-color ANSI palette. This guarantees instant loading, zero latency, and the ability to run the terminal smoothly from any VPS. Data on analyzed companies is stored in a flat, customized JSON file (`stocks_extra.json`), which allows for easy exports and building a global investment leaderboard.
4. Impact and Project Return
Financial analysis time fell by 95%, and the tool lets me spot market anomalies and investment opportunities before they hit mainstream financial media. The program's results won over other individual investors, which translated into full project monetization.
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