cvxgrp/cvxportfolio
Portfolio optimization and back-testing. observed · 2026-08-28
Health v2 · maintenance only
58/100
- Activity 79
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3522
- days_rel: 423
- days_push: 128
- n_releases_24m: 1
Adoption not part of the score
1253 stars · 292 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Cvxportfolio is an object-oriented Python library for portfolio optimization and back-testing, implementing the convex-optimization trading models from the Stanford/BlackRock paper. It provides a market simulator, symbolic optimization-based trading policies, cost and risk models, and forecasters, built on CVXPY and Pandas.
Use cases
- backtest a portfolio optimization strategy in Python
- build a Markowitz mean-variance asset allocation policy
- simulate trading strategy performance including transaction and holding costs
- optimize asset allocation with long-only, leverage, or factor-neutral constraints
- research quantitative trading strategies on historical market data
- compare multiple trading policies with a market simulator
- define custom return forecasts and covariance risk models for trading policies
When to choose
- You want convex-optimization-based portfolio construction and back-testing in a Python library
- You prefer symbolic trading policies (objectives plus constraints) that work with any asset universe and Pandas time-series data
- You want a realistic market simulator with stock-specific transaction and holding cost defaults
- You plan to extend the framework with custom costs, constraints, or forecast models
When to avoid
- You need live order execution or broker connectivity - the library computes target weights and simulates rather than trading in production
- You need machine-learning or combinatorial (non-convex) portfolio optimization outside the CVXPY convex framework
- You need tick-level or high-frequency market microstructure simulation
- The GPL-3.0 license is incompatible with your project's licensing
Facets
library · maturity active
trading simulation math data-science fintech time-series data-science mathematics python cross-platform convex-optimization portfolio-optimization backtesting quantitative-finance markowitz cvxpy market-simulator trading-strategies risk-models transaction-costs
7 sources
- readme: https://github.com/cvxgrp/cvxportfolio · fetched 2026-08-28 · 57f1375701b2
- homepage: https://www.cvxportfolio.com · fetched 2026-08-29 · 0a20ccecb8f1
- registry_pypi: https://pypi.org/pypi/cvxportfolio/json · fetched 2026-08-29 · a5a86b5d0bd7
- site_page: https://www.cvxportfolio.com/en/1.5.0/api.html · fetched 2026-08-29 · 7fea9dba7b38
- site_page: https://www.cvxportfolio.com/en/1.5.0/manual.html · fetched 2026-08-29 · bd7b0276e1bb
- site_page: https://www.cvxportfolio.com/en/1.5.0/costs.html · fetched 2026-08-29 · 6d60c600915d
- site_page: https://www.cvxportfolio.com/en/1.5.0/forecasts.html · fetched 2026-08-29 · d84b32ec9267
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| cvxgrp/cvxportfolio | main | 58 |
For agents
markdown · JSON · MCP: product_card(name="cvxgrp/cvxportfolio")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem