🎓 CS @ Georgia Tech · M.S. Quantitative & Computational Finance (incoming)
📊 Aspiring Quant Researcher / Trader / Developer
💻 Experience: Morgan Stanley (2x SWE Intern) · Equifax (ML Engineer Intern) · Georgia Tech Student Foundation (Senior Quant Director)
- ⚡ Build execution algorithms, trading signals, and backtesting frameworks
- 📈 Research time-series models, volatility surfaces, and deep learning in markets
- 🏦 Manage and mentor a 37-analyst team running a $50K quant portfolio inside a $2.6M endowment
- Languages: Python · C++ · SQL · Java · TypeScript
- Libraries/Tools: NumPy · Pandas · scikit-learn · statsmodels · PyTorch · TensorFlow · Backtrader · Plotly/Dash
- Quant: Factor modeling · Time-series (ARIMA/GARCH, SARIMA) · Monte Carlo simulation · Portfolio optimization · IBKR API
- Infra: Kafka · Docker · Git · Linux · Azure DevOps
- 📊 Option-Density-Viz: Extracted risk-neutral densities from BTC/ETH options (Deribit API), fit arbitrage-free SVI smiles, applied Breeden–Litzenberger + COS methods, and built Plotly analytics dashboards
- 🤖 Financial Markets RL Simulator: Built multi-agent RL limit order book simulator (SPY, BTC, 10Y); PPO/SAC agents achieved 39% CAGR and Sharpe 1.3 over 10 years
- 📐 Neural PDE Models for Option Greeks: Built a physics-informed neural network embedding the Black–Scholes PDE to generate option Greeks across strikes and volatilities, delivering faster and more stable sensitivities than finite-difference and Monte Carlo methods
- 🏦 Company Bankruptcy Predictor: Developed ML pipeline to forecast corporate bankruptcy using financial ratios. Trained Logistic Regression, Random Forest, SVM, Gradient Boosting, and Neural Nets; best model (SVM with RBF kernel) achieved ~96–97% accuracy with strong recall on bankruptcy cases.
- 📫 Email: [email protected]
- 💼 LinkedIn: linkedin.com/in/drew-verzino