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.
The second and third order Greeks a vol desk actually watches: vanna, volga, charm, speed, zomma, colour and ultima, each derived and plotted against the variable that moves it.
A long strangle against a short straddle, sized so vega is exactly zero. What is left is volga, and the page finds where that convexity peaks in every vol regime.
Gamma sliced one dimension at a time against spot, vol, time and strike, then stacked into the full 3D Black-Scholes gamma surface as expiry approaches.
Two numpy MLPs learn the implied volatility surface from real option chains, trained and scored on a strict out-of-time split.
Deep learning applied to the SPY implied volatility surface, with the data pipeline, the architecture choices, and an honest out-of-sample read.
A recurrent network learns a hedging policy directly from simulated paths instead of reading delta off a closed form, and is tested against the Black-Scholes hedge.
Lopez de Prado's HRP and HCAA built from scratch, clustering the correlation matrix before allocating, and benchmarked against Markowitz.
Five risk based allocation methods backtested side by side with GARCH volatility forecasts, including the turnover and cost drag each one carries.
Claude Opus 5 running a full investment committee, with independent analyst agents, a chair, and a decision record for every position taken.
Where to pull real option chains without paying for a data vendor, how to clean the quotes, and how to re-imply the vols so the surface is usable.
Building a fictional investment fund end to end with Claude, from the mandate and the brand through to the reporting pack.