# fmilthaler/FinQuant

A program for financial portfolio management, analysis and optimisation.

Repository: https://github.com/fmilthaler/FinQuant
Canonical: https://ross.abutalabs.com/products/finquant
Language: Python
License: MIT
License Family: permissive
Topics: portfolio-management, portfolio-optimisation, optimisation, finance, financial-analysis, analysis, financial, financial-portfolio-management, moving-average, bollinger-bands, portfolio-properties, monte-carlo, investment, investment-analysis, investment-portfolio, investment-strategies, returns, markowitz-portfolio, efficient-frontier, monte-carlo-simulation
Last push: 2023-11-04T08:38:31+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2782, "days_push": 1033, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1817, forks 234 (observed 2026-08-28T04:05:40.145099+00:00)

## What it is
FinQuant is a Python library for financial portfolio management, analysis, and optimisation. It lets users build portfolio objects from stock data, compute returns and technical indicators, and optimise allocations via the efficient frontier or Monte Carlo simulation.

## Use cases
- optimise stock portfolio allocations in python
- compute portfolio returns and risk metrics
- plot moving averages and bollinger bands for stocks
- run monte carlo simulation on an investment portfolio
- find the efficient frontier of a portfolio
- analyse investment portfolio performance

## When to choose
- you want a lightweight, few-lines-of-code tool for portfolio analysis and Markowitz optimisation in Python
- you need quick plots of returns, moving averages, and buy/sell signals
- you want to compare efficient frontier vs Monte Carlo portfolio optimisation

## When to avoid
- you need live trading, broker integration, or backtesting of strategies
- you require institutional-grade risk models or factor analysis
- you need actively maintained software with frequent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, math, analytics
- domain: fintech, data-science
- platform: python
- tags: portfolio-management, portfolio-optimisation, efficient-frontier, monte-carlo-simulation, quantitative-finance, moving-averages, bollinger-bands, investment-analysis, finance

## Member repositories
- fmilthaler/FinQuant (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:40.145099+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 2026-08-30T03:20:11.174516+00:00, confidence not recorded.
  - readme: https://github.com/fmilthaler/FinQuant (fetched 2026-08-28T04:05:40.145099+00:00, sha 03ae238a01ef)
  - registry_pypi: https://pypi.org/pypi/finquant/json (fetched 2026-08-29T10:59:24.260135+00:00, sha 615bec3c8cfe)
- Data as of 2026-08-30T08:39:29.467469+00:00.