Building a Monte Carlo Option Pricing Model in Python

A build-along course for quant analyst aspirants, prop trading applicants, and systematizing traders who want a real Monte Carlo pricing engine, not a toy script. You will write a vectorised GBM engine in NumPy, validate it against Black-Scholes and the live NSE Nifty chain, cut its error with antithetic, control variate, and Sobol techniques, compute Greeks by simulation, price path-dependent payoffs like the barriers inside Nifty-linked MLDs, move beyond GBM with jump-diffusion and Heston, handle early exercise with Longstaff-Schwartz, and ship the whole thing as a tested, fast Python package.