Python code of commonly used stochastic models for Monte-Carlo simulations

A collection of numerical implementations for the simulation of well-known stochastic processes on MATLAB.

🧑🏻‍💻 Multivariate Time Series Modelling with Neural SDE driven by Jump Diffusion

black-scholes monte-carlo options-pricing financial-engineering machine-learning neural-networks c-plus-plus mathematics numerical-methods

Quantitative stress-testing engine simulating JPY liquidity squeezes using Merton Jump-Diffusion (MJD) and Black-Scholes option hedging.

An EGARCH-JDM-MVO pipeline built from scratch in C++ and Python.

Python library pricing options across 7 models: Black-Scholes, Binomial Tree, Monte Carlo, Finite Difference (Explicit/Implicit/Crank-Nicolson), Heston, Merton Jump Diffusion, and SABR. Calibrated to live SPY market data with full implied volatility surface construction and Greeks analysis.

Backtest framework for a QQQ LEAPS Call infinite-roll strategy. Buys gap-down entries, harvests at target delta, force-rolls at 300 DTE. Supports BSM & Merton Jump-Diffusion pricing, real QQQ CSV data, delta sensitivity sweep, and full bilingual trade reports.

Options pricing engine — Black-Scholes, Heston & Merton Jump-Diffusion models calibrated to real market data