# cantaro86/Financial-Models-Numerical-Methods

Collection of notebooks about quantitative finance, with interactive python code.

Repository: https://github.com/cantaro86/Financial-Models-Numerical-Methods
Canonical: https://ross.abutalabs.com/products/financial-models-numerical-methods
Language: Jupyter Notebook
License: AGPL-3.0
License Family: copyleft
Topics: quantitative-finance, jupyter-notebooks, kalman-filter, option-pricing, financial-engineering, financial-mathematics, partial-differential-equations, fourier-inversion, stochastic-processes, stochastic-differential-equations, levy-processes, heston-model, brownian-motion, american-options, monte-carlo-methods, python, linear-regression, linear-systems-equations, jump-diffusion-mertons-model, econometrics
Last push: 2024-10-22T08:53:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2549, "days_push": 680, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7409, forks 1277 (observed 2026-08-28T04:09:59.715003+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: data-science, math, simulation
- domain: education, mathematics, tutorials
- platform: python, cross-platform
- tags: quantitative-finance, jupyter-notebooks, option-pricing, stochastic-processes, monte-carlo, kalman-filter, heston-model, pde-methods, fourier-methods, levy-processes

## Member repositories
- cantaro86/Financial-Models-Numerical-Methods (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:59.715003+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:37:52.035303+00:00, confidence not recorded.
  - readme: https://github.com/cantaro86/Financial-Models-Numerical-Methods (fetched 2026-08-28T04:09:59.715003+00:00, sha f0c3917861b8)
- Data as of 2026-08-30T08:39:29.467469+00:00.