Portfolio Optimizer

This project implements a portfolio optimization tool based on Markowitz's Modern Portfolio Theory and advanced methods like the Black-Litterman model. It provides functionalities for data loading, portfolio optimization, risk assessment, and visualization of results.

Features

• Data Loading: Load and preprocess market data using the DataLoader class.
• Markowitz Optimization: Calculate optimal portfolio weights and visualize the efficient frontier using the MarkowitzOptimizer.
• Black-Litterman Model: Adjust views and calculate weights with the BlackLittermanModel.
• Risk Metrics: Assess portfolio performance with functions to calculate Sharpe ratio and volatility.
• Visualization: Plot efficient frontiers and performance charts for better insights into portfolio performance.

Installation

To install the required dependencies, run:

pip install -r requirements.txt

Usage

Example of Markowitz Optimization

from src.optimization.markowitz import MarkowitzOptimizer

optimizer = MarkowitzOptimizer()
optimal_weights = optimizer.calculate_optimal_weights()

Example of Black-Litterman Model

from src.optimization.black_litterman import BlackLittermanModel

bl_model = BlackLittermanModel()
adjusted_weights = bl_model.adjust_views()

Running Tests

To run the unit tests, use:

pytest tests/

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.