Conducted an in-depth portfolio backtesting analysis to evaluate historical performance, assess risk-adjusted returns, and optimize allocation strategies using the S&P 500 and a diverse mix of developing market assets. Leveraging advanced tools such as Python (Pandas, NumPy, Matplotlib) and Excel VBA, I developed a robust backtesting framework capable of simulating portfolio performance under varying market conditions.

The analysis employed key financial models, including CAPM, Sharpe Ratio, and Value at Risk (VaR), to benchmark the S&P 500 against developing market equities, fixed income, and alternative assets. Stress testing and scenario analysis were applied to account for global market volatility, allowing for a comprehensive understanding of portfolio dynamics. Python’s machine learning libraries like matplotlib, yfinance were utilized to identify trends and enhance predictive insights for future allocations.

This project showcased my expertise in quantitative analysis, statistical modeling, and global market research, delivering actionable insights to optimize portfolio strategies, enhance diversification, and maximize risk-adjusted returns.