Quantitative finance projects focused on derivatives pricing, portfolio optimization, and numerical methods.
Featured:
-
options-pricer — European and American pricing via Black-Scholes, binomial tree, and Monte Carlo, with Greeks, implied vol, and convergence analysis.
-
portfolio-optimizer — Rolling backtest comparing sample vs Ledoit-Wolf covariance in max-Sharpe and min-variance optimization. Live demo included.
-
volatility-surface — Implied vol surface from real SPY chains, with no-arbitrage checks and interactive 3D dashboard.
-
limit-order-book — Price-time priority matching engine with Poisson order flow, market impact analysis, and live dashboard.
-
nse-options — Black-76 pricing validation and smile-screening framework for NSE Kenya options, with daily automated reports via GitHub Actions.
Languages: Python
Libraries: NumPy · pandas · SciPy · scikit-learn · matplotlib · Plotly · Streamlit · pytest
Quantitative finance: Derivatives pricing (Black-Scholes, binomial trees, Monte Carlo) · Greeks and finite-difference validation · Implied volatility inversion · Volatility surfaces and no-arbitrage constraints · Portfolio optimization (mean-variance, min-variance) · Covariance shrinkage (Ledoit-Wolf) · Backtesting with transaction costs · Statistical estimation and out-of-sample validation reproducible notebooks · modular Python packages Market microstructure · limit order books · order flow simulation · Poisson processes · market impact analysis
Engineering: Git · virtualenv · unit testing · Streamlit deployment ·