Python Portfolio Optimization Tool | Efficient Frontier & Sharpe Ratio

🚀 Automate Your Stock Analysis with Python! This powerful script performs professional-level portfolio optimization, calculating the Efficient Frontier, Sharpe Ratio, and Value at Risk (VaR) to help you build a smarter investment portfolio. Perfect for learning Data Science & Python.

✨ INSTANT DOWNLOAD | 🐍 BEGINNER-FRIENDLY | 📈 PROFESSIONAL TECHNIQUES

What You Get:

A ready-to-run Jupyter Notebook (.ipynb) & Python script.

A step-by-step PDF guide for absolute beginners.

Batch files (*.bat) for one-click installation & execution.

Outputs: Optimal Portfolio Weights, Correlation Heatmap, Efficient Frontier Graph, and Risk Reports.

What It Is NOT:

NOT financial advice. The creator is not a licensed financial advisor.

NOT a prediction tool. It analyzes past data, which does not guarantee future results.

NOT a profit guarantee. All investing involves risk, including loss of principal.

Users are solely responsible for their own investment decisions.

Section 2: Installation Guide
Aimed at absolute beginners, the installation process involves two simple steps:

Install Python: Download the latest version from python.org. Crucially, users must check the "Add Python to PATH" box during installation.

Install Dependencies: Double-click the install_dependencies.bat file. A terminal window will open and install the necessary libraries. The user closes the window once it says "Successfully installed."

Section 3: How To Use The Tool
Add Your Stocks:

Edit the main.py file with a text editor like Notepad or Notepad++.

Locate the line: TICKERS = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']

Replace the example ticker symbols with your own, keeping the formatting with quotes and commas (e.g., TICKERS = ['NVDA', 'V', 'JPM']).

Save the file.

Run the Analysis:

Double-click the run_analysis.bat file.

A terminal window will open, show a progress bar for downloading data, and print results.

Two graphs will automatically appear and the results will be saved as CSV and PNG files in the folder.

Section 4: Understanding The Results
The tool generates four key outputs:

Correlation Heatmap: A chart showing how similarly stocks move together.

Dark Red: Highly correlated (move together).

Dark Blue: Uncorrelated or move opposite (good for diversification