I sit at the intersection of quantitative finance and machine learning — designing data-driven systems that extract signal from noise, price risk, and build edge in financial markets.

A full quant research pipeline — from raw data to risk-adjusted returns — with Monte Carlo optimization, efficient frontier construction, and rigorous out-of-sample validation.

What's inside:

• 🎲 Monte Carlo simulation — 2,000 portfolios per rebalance cycle
• 📉 Efficient Frontier visualization with optimal Sharpe portfolio
• 🔄 Rolling backtest — 5-year train · 1-year test · annual rebalance
• 📐 Full risk suite — VaR, CVaR, Sharpe, Sortino, drawdown analysis
• 📊 Covariance & correlation matrix analysis

✦ Stack: Python · Pandas · NumPy · SciPy · Plotly · Statsmodels
✦ Optimization: Maximize Sharpe Ratio via Monte Carlo · Walk-forward validated