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pyportfolioopt

library1.6.0pypypiunverified

PyPortfolioOpt is a financial portfolio optimization library for Python, providing methods for mean-variance optimization, Black-Litterman allocation, and risk parity. Version 1.6.0 supports CVXPY-based solvers and offers both classical and objective-based optimization approaches. Release cadence is irregular, with contributions from the community.

data

pip install pyportfolioopt

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · PYPORTFOLIOOPT

pyportfolioopt

datapythonv1.6.0

Install

16.7s avg

Import

—

Disk

453MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.6.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 16.7s · import 0.000s · 397MB

453MB installed

● package 453MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

EfficientFrontier

✓ from pypfopt import EfficientFrontier

✗ from pypfopt import EfficientFrontier

Basic mean-variance optimization using historical returns and sample covariance.

import pandas as pd from pypfopt import EfficientFrontier, risk_models, expected_returns # Sample data: prices of 3 assets data = pd.DataFrame({ 'AAPL': [1.0, 0.9, 0.8, 0.85], 'GOOG': [1.0, 1.1, 1.0, 1.05], 'MSFT': [1.0, 0.95, 1.1, 1.0] }) # Calculate expected returns and covariance matrix mu = expected_returns.mean_historical_return(data) Sigma = risk_models.sample_cov(data) # Optimize for maximum Sharpe ratio ef = EfficientFrontier(mu, Sigma) weights = ef.max_sharpe() print(ef.clean_weights())

Debug

Known issues

gotchaImport package as pypfopt, not pyportfolioopt. The pip install name and the import name differ.

fix

Use 'from pypfopt import ...' instead of 'from pyportfolioopt import ...'

affects: all

gotchaCVXPY may require a separate solver installation (e.g., 'pip install cvxpy') on some systems, especially for advanced solvers like ECOS or SCS.

fix

Install CVXPY with pip install cvxpy or use the bundled solver (may not work for large problems).

affects: all

breakingIn version 1.5+, the EfficientFrontier object no longer accepts a target return as a parameter to some methods; use objective functions instead.

fix

Use methods like `ef.efficient_return(target_return)` directly, not via a constructor parameter.

affects: >=1.5.0

deprecatedThe `pypfopt.plotting` module is deprecated as of version 1.6.0 and will be removed in a future release.

fix

Use matplotlib directly to plot efficient frontier or weights.

affects: >=1.6.0

Upgrade

Version history

1.6.0latest on PyPI · released Feb 26, 2026

Audit

Dependencies

cvxpyrequiredConvex optimization solver, required for optimization

numpyrequiredNumerical computations

pandasrequiredData handling and returns calculation

scipyoptionalScientific computing, used for some optimizers

Agent activity

4 hits · last 30 days

node

Resources

githubhttps://github.com/pyportfolio/pyportfolioopt ↗

packagehttps://pypi.org/project/pyportfolioopt/ ↗

homepagehttps://pyportfolioopt.readthedocs.io/en/latest/ ↗

docshttps://pyportfolioopt.readthedocs.io/en/latest/ ↗