# enthought/pyql

Cython QuantLib wrappers

Repository: https://github.com/enthought/pyql
Canonical: https://ross.abutalabs.com/products/pyql
Language: Cython
License: NOASSERTION
License Family: other
Topics: quantlib, cython
Last push: 2026-07-17T21:42:26+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 5291, "days_push": 47, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1335, forks 234 (observed 2026-08-28T04:04:25.324842+00:00)

## What it is
PyQL provides Cython-based Python wrappers over the QuantLib C++ quantitative finance library, exposing objects like Date and Calendar with a simpler interface than SWIG-based QuantLib-Python. It requires QuantLib 1.5+ and supports Python 2 and 3.

## Use cases
- price bonds and options from python
- use quantlib date and calendar classes in python
- build python bindings for a c++ quant library
- compute financial schedules and day counts in python
- prototype quantitative finance models in python

## When to choose
- you want a lightweight, Cython-based alternative to the SWIG QuantLib-Python bindings
- you mainly need simple QuantLib objects like dates and calendars in Python
- you are comfortable compiling against QuantLib yourself

## When to avoid
- you need the full breadth of QuantLib instrument and pricing coverage out of the box
- you want prebuilt wheels with no C++ compilation
- you need polished documentation and tutorials

## Facets
- artifact type: library
- maturity: active
- function: sdk, math
- domain: fintech
- platform: python, cpp, cross-platform
- tags: quantlib, cython, quantitative-finance, bindings, fixed-income, date-calendar, python-bindings

## Member repositories
- enthought/pyql (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:25.324842+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 2026-08-30T04:44:22.060982+00:00, confidence not recorded.
  - readme: https://github.com/enthought/pyql (fetched 2026-08-28T04:04:25.324842+00:00, sha 88873a547233)
- Data as of 2026-08-30T08:39:29.467469+00:00.