# domokane/FinancePy

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

Repository: https://github.com/domokane/FinancePy
Canonical: https://ross.abutalabs.com/products/financepy
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Topics: risk, pricing, risk-management, asset-allocation, finance, valuation, python, derivatives-pricing, numba, bonds, students, fixed-income, derivatives, investment, currency, credit
Last push: 2026-08-22T16:19:41+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 2502, "days_push": 11, "days_rel": 12, "gap_med": 7.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3113, forks 431 (observed 2026-08-28T04:07:44.056587+00:00)

## What it is
FinancePy is a Python library for pricing and risk-managing financial derivatives across fixed-income, equity, FX, and credit asset classes. It provides market curve and volatility surface objects, pricing models (Black-Scholes, Bachelier, Vasicek, etc.), product modules, and date/schedule utilities, accelerated with Numba.

## Use cases
- price equity and fx options in python
- value bonds and fixed income instruments
- price credit default swaps
- compute derivative risk metrics
- build interest rate curves and volatility surfaces
- learn quantitative finance pricing models

## When to choose
- you need a pure-Python toolkit for pricing options, bonds, swaps, or CDS
- you want fast, Numba-accelerated pricing models with many worked notebook examples
- you are a student or quant prototyping valuation and risk workflows

## When to avoid
- you need production-grade, vendor-supported derivatives pricing with regulatory compliance
- you require live market data feeds or broker connectivity (the library does not provide these)
- you need portfolio backtesting or trading execution frameworks

## Facets
- artifact type: library
- maturity: active
- function: math, data-science, trading
- domain: fintech, data-science, mathematics
- platform: python, cross-platform
- tags: quantitative-finance, derivatives-pricing, fixed-income, risk-management, numba, options-pricing, credit-derivatives, financial-modeling

## Member repositories
- domokane/FinancePy (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:44.056587+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 2026-08-30T07:26:41.473417+00:00, confidence not recorded.
  - readme: https://github.com/domokane/FinancePy (fetched 2026-08-28T04:07:44.056587+00:00, sha 46d786ff373d)
  - registry_pypi: https://pypi.org/pypi/financepy/json (fetched 2026-08-29T09:41:47.568077+00:00, sha 0005e5f88b66)
- Data as of 2026-08-30T08:39:29.467469+00:00.