cantaro86/Financial-Models-Numerical-Methods ? reverse-engineered prompt

I want a Python project built around interactive Jupyter notebooks that teaches practical quantitative finance by actually letting me run the models, not just read theory. Make it a collection of mostly independent notebooks so someone can open any topic they care about and start experimenting.

Cover the main areas shown here, like Black Scholes pricing methods, Monte Carlo and binomial models, SDE simulations, Fourier based pricing, Heston and Lévy process models, PDE and PIDE methods, exotic and American options, volatility smile and calibration, Kalman filter examples, Ornstein Uhlenbeck process applications, and a simple mean variance optimization notebook. The code should feel well documented and ready to use, with math explained just enough for someone who already knows the basics of finance, statistics, and stochastic processes.

Please also make sure it runs smoothly in Jupyter Lab, with a simple local setup plus a Docker option so I can open it in the browser and explore. If anything is unclear, look up current docs online and make reasonable choices.