📊 Portfolio Optimization using Mean-Variance Analysis

Welcome to the Portfolio Optimization project! This repository implements Mean-Variance Optimization based on Markowitz’s Modern Portfolio Theory (MPT) to determine optimal asset allocation for a portfolio.

🔗 Live Notebook (GitHub Pages):
View Portfolio Optimization Methods

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📌 Project Overview

🔹 What is Mean-Variance Optimization?

• Mean-Variance Optimization (MVO) helps investors maximize expected return while minimizing risk.
• It is based on the expected return (μ), volatility (σ), and correlation between assets.

🔹 Key Features Implemented

✅ Market Data Retrieval: Fetching stock price data over a recent period.
✅ Expected Return & Risk Estimation: Computing μ and σ for each stock.
✅ Covariance Matrix Calculation: Estimating risk relationships between assets.
✅ Optimization Using MVO: Mean-Variance Optimization.
✅ Comparison of Equal vs. Optimized Weights with Visualizations.
✅ GitHub Pages Integration to display the notebook online.

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Steps to set up the environment and install the required libraries

1. Installing uv
   pip install uv

2. Create a virtual environment with Python 3.10 Note: Use a virtual environment with Python 3.10 for this program. uv venv --python 3.10

3. Activate the virtual environment source .venv/bin/activate .venv\Scripts\activate for Windows

4. Specify required python libraries in requirements.txt

5. Install required python libraries uv pip install -r requirements.txt