📈 Portfolio Optimization Toolkit

A lightweight and modular Python toolkit for portfolio optimization, backtesting, and data visualization. Built with Streamlit for a simple and interactive user interface.

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🚀 Features

• Portfolio Optimization:
  • Maximize Sharpe Ratio
  • Minimize Portfolio Volatility
  • Risk Parity Optimization
• Backtesting:
  • Simulate and compare portfolio strategies against benchmarks
• Data Handling:
  • Fetches historical prices using yfinance (Yahoo Finance API)
• Visualization:
  • Interactive performance plots and risk-return charts
• Streamlit App:
  • Web-based UI for easy experimentation

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📂 Project Structure

  Portfolio Optimization Toolkit/
  ├── app/
  │   └── streamlit_app.py
  ├── portfolio_optimizer/
  │   ├── __init__.py
  │   └── optimizer.py
  ├── src/
  │   ├── backtest.py
  │   ├── data_loader.py
  │   ├── plotter.py
  │   └── utils.py
  ├── requirements.txt
  ├── README.md
  └── LICENSE

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/diegotistical/portfolio-optimization-toolkit.git
   cd portfolio-optimization-toolkit

2. Install required packages:

   pip install -r requirements.txt
   Run the Streamlit app:
   streamlit run app/streamlit_app.py
   

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📊 Technologies Used

Python 3.10+

Streamlit for the web app

pandas, NumPy for data manipulation

matplotlib, seaborn for visualization

scipy.optimize for portfolio optimization

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✨ Future Improvements

Add more robust backtesting framework (transaction costs, slippage)

Integrate live data from APIs (Yahoo Finance, Alpha Vantage)

Add machine learning-based portfolio selection models

Extend to multi-period optimization

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Authors

Diego Urdaneta — @Diegotistical