Open-source Python implementations and Claude prompts for quantitative finance.
Read the theory, explore the code, and copy what you need.
Complete implementation of the Black-Scholes framework covering European option pricing, arbitrage bounds, implied volatility via Brent's method, and put-call parity.
Step-by-step Delta, Gamma, Theta and Vega in Python. Includes formulas, plots, and an interactive chart that shows how gamma sharpens as time to expiry approaches zero.
Stochastic volatility step by step: each Heston parameter as a 1D slice (rho, xi, kappa, v_0), then stacked into the full 3D implied vol surface with a low-vs-high regime comparison.
Monte Carlo simulation under Geometric Brownian Motion with Itô correction. Terminal distributions, path simulation, and comparison against Black-Scholes analytical prices.
Cox-Ross-Rubinstein binomial tree for American and European option pricing, early exercise detection, and convergence to Black-Scholes.
Mean-variance optimization using the Markowitz framework, covering efficient frontier, Sharpe ratio maximization, constrained allocation, and portfolio backtesting.
Step-by-step Claude prompt that turns any Python financial model into a fully interactive, self-contained HTML dashboard.
Claude Design system + Claude Code skill that builds institutional equity research decks straight from an Excel model and exports a finished PDF.