Summary: Designed and executed a comprehensive global asset allocation strategy, testing a diverse mix of assets, including S&P 500 equities, U.S. Treasuries, corporate bonds, emerging market equities (MSCI Emerging Markets Index), gold, and commodities like crude oil. The aim was to build diversified portfolios optimized for risk-adjusted returns.

Using Python (Pandas, NumPy, Matplotlib) and Excel VBA, I developed models to calculate efficient frontiers, balancing risk and return. Macro-level data such as GDP growth, inflation rates, interest rate changes, and commodity price trends were incorporated to inform allocation decisions. Monte Carlo simulations and correlation analysis were applied to evaluate portfolio resilience across different market conditions, while stress tests assessed performance under extreme scenarios.

This project combined quantitative analysis with practical insights into global markets, delivering actionable allocation strategies tailored to maximize returns while minimizing risk in dynamic financial environments. It also strengthened my expertise in portfolio design and global market analysis.