💰 Investment Portfolio Optimizer

"Risk comes from not knowing what you're doing." – Warren Buffett

A Python-powered tool that helps you optimize investment portfolios using Modern Portfolio Theory (MPT) and Monte Carlo simulations. Analyze, simulate, and visualize risk-return trade-offs with confidence. 📊📈

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Table of Contents

• Features
• Installation
• Usage
• Visuals
• Concepts
• Contributing
• License

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Features

• Fetch historical stock data using yfinance.
• Perform Monte Carlo simulations to explore portfolio risk/return combinations.
• Optimize portfolios for:
• Maximum Sharpe Ratio
• Minimum Volatility
• Plot the Efficient Frontier.
• Export allocation data and visuals for reporting.

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Installation

1. Clone the repository

   git clone https://github.com/your-username/investment-portfolio-optimizer.git
   cd investment-portfolio-optimizer

2. Install dependencies

   pip install -r requirements.txt

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Usage

1. Fetch Historical Data

python fetch_data.py --ticker AAPL --start 2020-01-01 --end 2021-01-01

2. Run Monte Carlo Simulations

python monte_carlo.py --iterations 1000

3. Optimize Portfolio

python optimize.py --objective sharpe_ratio

4. Plot Efficient Frontier

python plot_efficient_frontier.py

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Visuals

Simulated portfolios, color-coded by Sharpe ratio. Red Star = Max Sharpe 📍 | Green Star = Min Volatility ✅

Concepts

Modern Portfolio Theory (MPT)

• A strategy to construct a portfolio that maximizes return for a given level of risk.
• Introduced by Harry Markowitz, focuses on diversification and the Efficient Frontier.

Monte Carlo Simulation

• Generates thousands of portfolio combinations with random weights.
• Helps estimate distribution of returns and identify optimal portfolios.

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Contributing

We welcome contributions! 🛠️

1. Fork the repo
2. Create a branch: git checkout -b feature-branch
3. Commit your changes: git commit -am 'Add new feature'
4. Push the branch: git push origin feature-branch
5. Create a Pull Request!

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

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