Stock_evaluation_Python 🏦📊

A Python script to classify companies based on financial metrics like the Piotroski F-Score and Stock Valuation. It processes CSV financial data for multiple companies and outputs a classification (Strong, Medium, or Weak) for each company.

Features 🚀

• Computes Piotroski F-Score based on profitability, leverage, and efficiency metrics.
• Calculates Stock Valuation using P/E Ratio and P/B Ratio.
• Classifies companies into Strong, Medium, or Weak based on their metrics.
• Handles missing or invalid financial data gracefully.
• Saves the results as a CSV file.

How It Works 🛠️

1. Loads financial data from CSV files for specified company tickers.
2. Computes the following metrics:
   • Piotroski F-Score: Combines 9 financial metrics to score financial health.
   • Stock Valuation: The sum of P/E Ratio and P/B Ratio.
3. Classifies companies based on the metrics:
   • Strong: F-Score ≥ 7 and Valuation < 20
   • Medium: F-Score ≥ 4 and Valuation < 30
   • Weak: All others
4. Outputs a CSV file with the results.

Usage 🖥️

1. Install Dependencies

Ensure you have Python 3.7+ and the following Python libraries installed:

pip install pandas
. Directory Setup
Organize your data in the following structure:

Copy
Edit
financial_data/
│
├── GM_ratios.csv
├── GM_cash_flow.csv
├── GM_balance_sheet.csv
├── GM_income_statement.csv
├── TSLA_ratios.csv
├── TSLA_cash_flow.csv
├── TSLA_balance_sheet.csv
├── TSLA_income_statement.csv
├── AAPL_ratios.csv
├── AAPL_cash_flow.csv
├── AAPL_balance_sheet.csv
├── AAPL_income_statement.csv
3. Run the Script
Save the script as stock_picker.py, and execute it: