LechGrzelak/Computational-Finance-Course ? reverse-engineered prompt

I want a practical Python learning project for computational finance that mirrors this course and makes it easy to study topic by topic. Please organize the material by lecture, from the basics of stocks, options, and stochastic processes through option pricing, implied volatility, jump models, stochastic volatility, Fourier pricing, Monte Carlo methods, Heston, hedging and Greeks, forward start options, Bates, and exotic derivatives.

For each lecture, add clear runnable examples, short explanations in plain English, and code that actually demonstrates the idea, not just theory. If there are exercises or questions, include those in a simple way too. Make it feel like a self study course someone can open and run without a lot of setup pain.

Keep the structure clean and consistent across lectures, with readable notebooks or scripts, comments, and plots where they help. If something is missing, use the course topics, the linked videos, and the book description as guidance, and look up current docs online if you need to.