📊 Capital Compounder Strategy

An automated stock scanner and ranking engine built to identify the most capital-efficient public companies based on fundamental analysis. Inspired by Joel Greenblatt’s “The Little Book That Still Beats the Market,” this tool evaluates companies using two key metrics: Earnings Yield (EY) and Return on Capital (ROC).

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🧠 What It Does

This project fetches real-time financial statements from the Polygon.io API and:

• Calculates Earnings Yield = EBIT / Enterprise Value
• Calculates Return on Capital = EBIT / (Net Fixed Assets + Working Capital)
• Assigns accuracy levels to each data point (🟢 accurate, 🟠 estimated, 🔴 rough)
• Groups results by fiscal period
• Saves raw financials locally for transparency
• Ranks all companies from best to worst based on capital efficiency

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🔎 Who It's For

• Long-term investors who want a disciplined, data-driven way to evaluate stocks
• Fans of Joel Greenblatt’s Magic Formula strategy
• Developers looking to automate equity screening and valuation
• Quant-minded individuals who prefer to own shares of great businesses with solid fundamentals

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📁 Project Structure

.
├── company_data/                 # Saved JSONs of financial snapshots
│   ├── partial/                 # Companies missing key financials
├── tickers.txt                  # Tickers to evaluate (one per line)
├── scan.py                      # Main execution script
├── utils.py                     # Core logic (fetching, parsing, calculations)
├── config_example.py            # Template for your Polygon API key
├── requirements.txt             # Python packages required
├── .gitignore                   # Ignores API key and saved financials
└── README.md                    # This file

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📈 Key Metrics

Earnings Yield (EY)

How much a company earns (EBIT) relative to what it would cost to buy it (Enterprise Value).

Return on Capital (ROC)

How efficiently a company generates profits relative to capital employed.

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✅ Output Example

📊 Capital Efficiency Scan:

❌ GTLB: Missing 'income_statement'
❌ ITA: No data found

| Data Period: Jan 01 - Mar 31, 2025 |
🟢 HOOD: EY=1.73% | ROC=2.51% | Accurate Calculations
🟠 PLTR: EY=0.13% | ROC=3.09% | Estimated → cash, debt

🏆 All Stocks Ranked (Best to Worst):
🟢 NVDA: EY=2.51% | ROC=57.6%
🟠 PLTR: EY=0.13% | ROC=3.09%
🟢 HOOD: EY=1.73% | ROC=2.51%

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⚙️ How to Use

1. Install dependencies

   pip install -r requirements.txt

2. Add your Polygon API key
   Rename config_example.py to config.py and add your API key:

   POLYGON_API_KEY = "your_api_key"
   BASE_URL = "https://api.polygon.io/vX/reference/financials"  # change `vX` to correct version

3. Add tickers
   Populate tickers.txt with one stock ticker per line.

4. Run the scanner

   python scan.py

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🚀 Future Plans

This system is being expanded into a full long-term investment bot that will:

• Auto-discover and filter quality stocks weekly
• Deposit funds and allocate into top-ranked stocks
• Integrate with Interactive Brokers (IBKR) for automatic trades
• Send trade alerts and summaries to Discord via bot
• Track performance, risk, cash available, and portfolio value

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🙏 Inspiration

This project is deeply inspired by:

• Joel Greenblatt’s Magic Formula
• Quantitative value investing practices
• The idea that consistent, rational investing beats prediction-based trading

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🛡️ Disclaimer

This is a personal project for research and educational purposes. It is not financial advice. Use at your own risk.

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🧠 Author

Built with discipline, automation, and a long-term mindset by Merced Gonzales.

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⭐️ Contributions Welcome

Have ideas, features, or bug fixes? Fork this repo, make your changes, and open a pull request!