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It processes CSV financial data for multiple companies and outputs a classification (`Strong`, `Medium`, or `Weak`) for each company.\n\n---\n\n## Features 🚀\n- Computes **Piotroski F-Score** based on profitability, leverage, and efficiency metrics.\n- Calculates **Stock Valuation** using **P/E Ratio** and **P/B Ratio**.\n- Classifies companies into `Strong`, `Medium`, or `Weak` based on their metrics.\n- Handles missing or invalid financial data gracefully.\n- Saves the results as a CSV file.\n\n---\n\n## How It Works 🛠️\n1. Loads financial data from CSV files for specified company tickers.\n2. Computes the following metrics:\n   - **Piotroski F-Score**: Combines 9 financial metrics to score financial health.\n   - **Stock Valuation**: The sum of P/E Ratio and P/B Ratio.\n3. Classifies companies based on the metrics:\n   - `Strong`: F-Score ≥ 7 and Valuation \u003c 20\n   - `Medium`: F-Score ≥ 4 and Valuation \u003c 30\n   - `Weak`: All others\n4. Outputs a CSV file with the results.\n\n---\n\n## Usage 🖥️\n### **1. Install Dependencies**\nEnsure you have **Python 3.7+** and the following Python libraries installed:\n```bash\npip install pandas\n. Directory Setup\nOrganize your data in the following structure:\n\nCopy\nEdit\nfinancial_data/\n│\n├── GM_ratios.csv\n├── GM_cash_flow.csv\n├── GM_balance_sheet.csv\n├── GM_income_statement.csv\n├── TSLA_ratios.csv\n├── TSLA_cash_flow.csv\n├── TSLA_balance_sheet.csv\n├── TSLA_income_statement.csv\n├── AAPL_ratios.csv\n├── AAPL_cash_flow.csv\n├── AAPL_balance_sheet.csv\n├── AAPL_income_statement.csv\n3. Run the Script\nSave the script as stock_picker.py, and execute it:\n\n\n\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcaperella29%2Fstock_evaluation_python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcaperella29%2Fstock_evaluation_python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcaperella29%2Fstock_evaluation_python/lists"}