Efficient Frontier - Monte Carlo Portfolio Optimization

This repository demonstrates how to simulate and optimize a portfolio of assets using a Monte Carlo approach and a mathematical Efficient Frontier calculation. It identifies the portfolio with the highest Sharpe Ratio and compares its performance to a market benchmark (e.g., SPY).

Table of Contents

1. Project Description
2. Features
3. Installation
4. Usage
5. File Structure
6. Configuration
7. Disclaimer
8. License
9. Contributing

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Project Description

This project leverages Modern Portfolio Theory and Monte Carlo Simulation to explore different portfolio allocations and find an optimal mix of assets (i.e., the highest Sharpe ratio). Additionally, it plots the Efficient Frontier both from random portfolios and a mathematical optimizer:

• Monte Carlo Simulation: Randomly allocates weights among assets thousands of times to observe a broad range of risk-return outcomes.
• Analytical Efficient Frontier: Uses an optimizer (via scipy.optimize) to systematically compute the theoretical minimum volatility portfolio for each level of return.

By comparing the random results and the analytical frontier, you get a clearer picture of your portfolio’s potential risk and return.

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Features

1. Data Retrieval: Automatically fetches historical data from Yahoo Finance.
2. Monte Carlo Simulation: Generates up to hundreds of thousands of random portfolio allocations.
3. Efficient Frontier: An analytical solution using constrained optimization.
4. Benchmark Comparison: Compares the optimal portfolio to a chosen market index (e.g., SPY).
5. Visualization: Plots the Monte Carlo cloud, the Efficient Frontier, and highlights key portfolios (Market, Optimal, etc.).
6. Export: Saves the final plot as an image (.png) and can export data to CSV.

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Installation

1. Clone the repository:

git clone https://github.com/JordiCorbilla/efficient-frontier-monte-carlo-portfolio-optimization.git
cd monte-carlo-portfolio-optimization

Usage

You can directly run the script from the command line:

python monte_carlo_portfolio.py \
    --assets AAPL MSFT TSLA \
    --start 2020-01-01 \
    --end 2025-01-01 \
    --market SPY \
    --risk_free_rate 0.05 \
    --portfolios 100000

Command-Line Arguments: --assets: List of tickers (space-separated). If not specified, the script defaults to a set of Dividend Kings (source: DividendGrowthInvestor.com). --start: Start date for historical data (YYYY-MM-DD). Default: 2020-01-01. --end: End date for historical data (YYYY-MM-DD). Default: 2025-01-01. --market: Benchmark market ticker. Default: SPY. --risk_free_rate: Risk-free rate (annualized). Default: 0.05. --portfolios: Number of random portfolio simulations. Default: 100000.

When the script finishes:

• A PNG file named efficient_frontier_monter_carlo.png is saved locally.
• A CSV file called market_data.csv is saved with the downloaded prices.

File Structure

.
├── monte_carlo_portfolio.py     # Main Python script (example name)
├── riskoptima                   # Supporting code for plotting EF
├── requirements.txt             # Dependencies list
├── README.md                    # This file
└── ...

• monte_carlo_portfolio.py: Primary script that handles data download, Monte Carlo simulation, efficient frontier plotting, and saves the results.
• riskoptima.py: Contains helper functions (plot_ef_ax, etc.) to handle the efficient frontier calculation. (https://github.com/JordiCorbilla/RiskOptima)
• requirements.txt: Python dependencies.
• README.md: Documentation.

Configuration

• Default Assets: By default, uses 10 dividend-paying stocks referred to as Dividend Kings from this list. You can override them by passing custom tickers via --assets.
• Risk-Free Rate: Default set to 0.05 (5%). Adjust this to a realistic short-term interest rate if you prefer.
• Trading Days: Set to 252, typical for US markets. Change if modeling a different market or a custom schedule.

Disclaimer

• This project is for educational purposes only and is not financial advice.
• Past performance does not guarantee future results. All investments carry risk, and you could lose money.

License

• MIT License

Contributing

• Fork this repository.
• Create a new branch for your feature (git checkout -b feature/some-feature).
• Commit your changes (git commit -m "Add some feature").
• Push to your branch (git push origin feature/some-feature).
• Open a Pull Request.
• Contributions and suggestions are always welcome!

Happy Investing!