I am focused on quantitative finance, derivatives, portfolio construction, risk management, and machine learning for asset management. My work is mostly Python, C++, Excel modeling, and applied statistical methods.
This GitHub is organized as a portfolio of projects I built across pricing, risk, time series, private markets, and AI. The repositories below are the best place to start.
C++ quantitative finance library with Python bindings for derivatives pricing, Monte Carlo simulation, PDE solvers, yield curves, volatility curves, and delta hedging.
Capstone project on evergreen private funds, public SEC filing data, synthetic private market indices, performance analysis, and illiquidity aware portfolio allocation.
Applied AI and mathematical finance studies covering Black Litterman allocation, PSO option pricing, knowledge graphs, causal DAGs, financial NLP, reinforcement learning, LDA, and Naive Bayes.
Counterparty credit risk project covering FX exposure profiles, interest rate swap exposure, CVA, sensitivity analysis, historical simulation VaR, CDS hedges, vega hedges, and PCA swap curve scenarios.
Time series project comparing DCC GARCH and CCC GARCH covariance forecasts for SPY and TLT minimum variance portfolios, with rolling out of sample tests and Diebold Mariano comparisons.
World Cup forecasting research with Bayesian goal models, posterior bracket simulation, Kalshi market comparison, Kelly sizing, risk controls, and a local scan dashboard.
Soccer market strategy research system with live odds ingestion, implied probability reports, cross book edge scans, signal monitoring, and execution simulation.
Excel based portfolio construction model using factor regressions, historical simulation, mean variance optimization, inverse volatility risk parity, total risk contribution, VaR, and CVaR.
- Derivatives pricing: Black Scholes, binomial trees, Longstaff Schwartz, Monte Carlo, PDE methods, stochastic calculus
- Risk management: VaR, CVaR, CVA, exposure profiles, hedge VaR, scenario generation
- Portfolio construction: mean variance optimization, Black Litterman, risk parity, factor models
- Time series: GARCH, DCC, volatility forecasting, rolling out of sample evaluation
- Trading strategy research: prediction markets, implied odds, bookmaker vig, arbitrage scanning, Kelly sizing, paper trading
- Machine learning: NLP, LDA, Naive Bayes, causal graphs, knowledge graphs, reinforcement learning
- Tools: Python, C++, CMake, Jupyter, pandas, NumPy, SciPy, scikit learn, Excel