PyPortfolioOpt

Name: PyPortfolioOpt
Author: Robert Andrew Martin and contributors

by Robert Andrew Martin and contributors · Library · also known as pypfopt

Python library for portfolio optimization: efficient frontier, Black-Litterman, hierarchical risk parity and discrete allocation

MIT licenseOpen sourceFreeLocalSelf-hostable

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Link verified Nov 2, 2026

Overview

PyPortfolioOpt is an open-source Python library for portfolio optimization created by Robert Andrew Martin and now maintained under the PyPortfolio organisation. It implements the classical mean-variance efficient frontier with pluggable expected-return and risk-model estimators (sample, exponentially weighted, shrinkage), Black-Litterman views blending, CVaR and semivariance objectives, hierarchical risk parity, and a discrete allocation step that converts weights into whole-share positions for a given budget. Optimization is expressed through cvxpy, so custom constraints and objectives can be added. It suits analysts and students who want transparent allocation code on top of pandas price data. It is not a backtester or data source, assumes inputs are already clean, and large universes or complex constraints can hit solver limits.

Capabilities

Mean-variance optimization with custom constraints via cvxpy
Return and risk estimators including shrinkage covariance
Black-Litterman model with investor views
Hierarchical risk parity and CVaR/semivariance objectives
Discrete allocation to whole shares

Typical workflows

Estimate returns and covariance from prices, optimize for maximum Sharpe, then allocate a budget
Blend market-implied returns with analyst views via Black-Litterman

Strengths

Readable API suited to teaching
MIT license
Extensible objectives and constraints

Limitations

No backtesting or data retrieval
Solver performance degrades with large universes or many constraints

Prerequisites

Python with pandas, NumPy, SciPy and cvxpy

Who it is for

Finance students
Analysts prototyping allocation models

Alternatives & comparison

Riskfolio-Lib covers a wider set of risk measures and optimization models and leans on the same cvxpy stack; PyPortfolioOpt is smaller and simpler to read. QuantLib addresses instrument pricing rather than allocation.

Ghostfolio
Open-source wealth-tracking web app for stocks, ETFs, crypto and other assets, self-hostable with Docker or used as a hosted service
Open sourceOpen source
QuantLib
Open-source C++ library for quantitative finance covering instruments, term structures, pricing engines and calendars, with Python and other bindings
Open sourceOpen source
Riskfolio-Lib
Python library for quantitative portfolio optimization with many risk measures, robust estimators and hierarchical clustering models
Open sourceOpen source
SciPy
Core open-source Python library for scientific computing on NumPy arrays: optimization, integration, interpolation, linear algebra, sparse matrices, statistics, signal processing
Open sourceFree

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Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

License
LICENSE (MIT)
https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/LICENSE
Supports: license_spdx
checked Nov 1, 2026
Official site
PyPortfolioOpt documentation
https://pyportfolioopt.readthedocs.io/en/latest
Supports: name, summary, capabilities
checked Nov 1, 2026
Repository
PyPortfolio/PyPortfolioOpt
https://github.com/PyPortfolio/PyPortfolioOpt
Supports: product_status, languages
checked Nov 1, 2026

Confidence: High
Reviewed: Nov 1, 2026
Links: Verified · Nov 2, 2026

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Ghostfolio

Ghostfolio · Portfolio & wealth management

Open-source wealth-tracking web app for stocks, ETFs, crypto and other assets, self-hostable with Docker or used as a hosted service

AGPL-3.0-onlyOpen sourceFreemiumSelf-hostedNov 2, 2026