Quantitative Asset Allocation is a crucial aspect of modern portfolio management. This Python project aims to implement and compare four popular portfolio optimization techniques: Minimum Variance, Mean Variance, Semi-Variance, and Omega, to help investors make informed decisions in allocating their investments across various assets. The project will utilize historical financial data and advanced mathematical concepts to find the optimal allocation strategies for risk-averse investors seeking to maximize returns while minimizing risk.

Key Techniques:

Minimum Variance:

• The Minimum Variance technique seeks to minimize the portfolio's overall variance, which represents the spread of returns across the assets. By optimizing the weights of each asset, the algorithm aims to construct a portfolio that offers the lowest possible risk, making it suitable for conservative investors.

Mean Variance:

• The Mean Variance technique takes into account both the expected returns and the risk (variance) of the portfolio. It seeks to find the trade-off between maximizing returns and minimizing risk by allocating investments based on the assets' expected returns and their covariance. The project will employ optimization algorithms to find the efficient frontier, which represents the best possible risk-return combinations.

Semi-Variance:

• Semi-Variance is a risk measure that only considers negative returns, focusing on the downside risk. This technique is especially useful for risk-averse investors who are primarily concerned about protecting their capital from losses. The project will employ mathematical techniques to calculate semi-variance and use optimization to create portfolios with the lowest downside risk.

Omega:

• The Omega technique is a risk-adjusted performance measure that incorporates the entire return distribution of a portfolio. Unlike traditional measures like Sharpe Ratio, which only considers the mean and standard deviation, Omega considers the entire distribution and assesses how well the portfolio performs at different levels of risk. This project will calculate the Omega ratio for various portfolios and use it as an alternative risk metric.

Project Workflow:

1.- Data Collection: Collect historical financial data for the chosen assets, such as stocks, bonds, commodities, etc., from reliable sources like financial APIs or databases.

2.- Data Preprocessing: Clean and preprocess the data to ensure consistency and handle missing values, if any. Calculate asset returns and other relevant financial metrics.

3.- Portfolio Optimization: Implement the four portfolio optimization techniques using Python.

4.- Portfolio Evaluation: Evaluate and compare the performance of portfolios generated by each technique using relevant performance metrics, such as Sharpe Ratio, Omega Ratio, and average returns.

5.- Backtesting: Backtest the selected portfolios on historical data to assess their performance over time. Analyze risk-adjusted returns and compare them against benchmarks.