# jsmidt/QuantPy

A framework for quantitative finance In python.

Repository: https://github.com/jsmidt/QuantPy
Canonical: https://ross.abutalabs.com/products/quantpy
Language: Python
License: BSD-4-Clause
License Family: permissive
Last push: 2023-05-25T23:22:41+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4946, "days_push": 1196, "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 1050, forks 138 (observed 2026-08-28T04:03:22.912090+00:00)

## What it is
QuantPy is a Python framework for quantitative finance, offering portfolio analysis tools such as importing daily returns from Yahoo and computing optimal weights for Sharpe ratio and efficient frontier. The project is explicitly very early-stage and not yet ready for production use.

## Use cases
- calculate optimal portfolio weights for maximum Sharpe ratio
- plot an efficient frontier for a set of assets
- import daily stock returns from Yahoo Finance
- profile market events against price data
- build a quantitative finance toolkit in Python

## When to choose
- you want to experiment with or contribute to an early-stage Python quant finance framework
- you need simple portfolio optimization like Sharpe ratio maximization
- you prefer a permissive BSD license for embedding in proprietary tools

## When to avoid
- you need a production-ready, well-tested quant finance library
- you require advanced features like backtesting, options pricing, or risk analytics
- you want actively maintained software with recent development activity

## Facets
- artifact type: library
- maturity: experimental
- function: data-science, data-visualization, math
- domain: fintech, data-science, analytics
- platform: python, cross-platform
- tags: quantitative-finance, portfolio-optimization, efficient-frontier, sharpe-ratio, finance

## Member repositories
- jsmidt/QuantPy (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:22.912090+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 2026-08-30T07:00:23.000787+00:00, confidence not recorded.
  - readme: https://github.com/jsmidt/QuantPy (fetched 2026-08-28T04:03:22.912090+00:00, sha 24109179da7f)
- Data as of 2026-08-30T08:39:29.467469+00:00.