cantaro86/Financial-Models-Numerical-Methods resource

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7409 stars · 1277 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of interactive Jupyter notebooks covering quantitative finance topics such as option pricing, stochastic processes, PDE methods, Fourier methods, and Kalman filters, with ready-to-run Python implementations. It serves as a tutorial-style learning resource for students and practitioners with background in financial mathematics and Python.

Use cases

• learn option pricing with Monte Carlo and binomial trees
• study the Heston model and stochastic volatility
• understand Fourier methods for option pricing
• implement Kalman filters for financial time series
• explore Lévy processes and jump diffusion models
• solve Black-Scholes PDE numerically
• price American options in Python

When to choose

• you want runnable Python examples of financial models
• you are a student or self-taught learner with basic stochastic calculus knowledge
• you want to study less common topics like PDE methods, Lévy processes, or Kalman filters interactively

When to avoid

• you are an absolute beginner in finance or Python
• you need production-grade, maintained financial libraries
• you expect a complete, textbook-style reference

Facets

learning-resource · maturity active

data-science math simulation education mathematics tutorials python cross-platform quantitative-finance jupyter-notebooks option-pricing stochastic-processes monte-carlo kalman-filter heston-model pde-methods fourier-methods levy-processes

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