🚀 Portfolio Optimization Project

📌 Overview

The Portfolio Optimization Project is designed to help investors allocate their assets efficiently by minimizing risk and maximizing returns using various optimization techniques. This project utilizes statistical and mathematical models to determine the best asset allocation strategies.

🎯 Features

• Efficient Frontier Calculation: Identifies the optimal portfolio combination.
• Mean-Variance Optimization: Uses Modern Portfolio Theory (MPT) to allocate assets.
• Monte Carlo Simulation: Evaluates different portfolio allocations using random sampling.
• Sharpe Ratio Optimization: Maximizes the risk-adjusted return.
• Data Visualization: Displays portfolio performance metrics using interactive charts.

🛠️ Technologies Used

• Python
  • NumPy
  • Pandas
  • Matplotlib
  • CVXPY
  • Flask

📥 Installation

1. Clone the repository:

   git clone https://github.com/ShreyasDankhade/Portfolio_Optimatization_Project.git

2. Navigate to the project directory:

   cd Portfolio_Optimatization_Project

3. Install dependencies:

   pip install -r requirements.txt

🚀 Usage

1. Run the provided Python scripts to analyze and optimize a sample portfolio.
2. Modify the dataset to test with different asset allocations.

📂 Project Structure

Portfolio_Optimatization_Project/
│── static/
│  │── css/
│  │  │── main.css      # CSS file
│  │── img/
│  │  │── logo.png      # Logo image
│── templates/
│  │── base.html
│── main.py             # Python scripts for optimization models
│── requirements.txt    # Dependencies
│── README.md           # Project documentation

📊 Examples

Efficient Frontier Plot

• Generates an efficient frontier showing risk vs. return trade-offs.

Monte Carlo Simulation

• Runs random portfolio allocations and visualizes the results.

Sharpe Ratio Optimization

• Finds the portfolio with the highest Sharpe ratio to maximize returns per unit of risk.

👥 Contributors

• Shreyas Dankhade (Repository Owner)
• Contributions are welcome! Feel free to fork and submit pull requests.

🙌 Acknowledgments

• Inspired by Markowitz’s Modern Portfolio Theory (MPT)
• Uses concepts from quantitative finance and investment strategy development.

📧 Contact

For questions or support, contact Shreyas Dankhade at shreyasdankhade75@gmail.com.