PyPortfolioOpt | Financial portfolio optimisation in python | Portfolio library

by robertmartin8 Jupyter Notebook Version: 1.5.5 License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | PyPortfolioOpt Summary

PyPortfolioOpt is a Jupyter Notebook library typically used in Web Site, Portfolio applications. PyPortfolioOpt has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

. PyPortfolioOpt is a library that implements portfolio optimization methods, including classical mean-variance optimization techniques and Black-Litterman allocation, as well as more recent developments in the field like shrinkage and Hierarchical Risk Parity, along with some novel experimental features like exponentially-weighted covariance matrices. It is extensive yet easily extensible, and can be useful for both the casual investor and the serious practitioner. Whether you are a fundamentals-oriented investor who has identified a handful of undervalued picks, or an algorithmic trader who has a basket of strategies, PyPortfolioOpt can help you combine your alpha sources in a risk-efficient way. Head over to the documentation on ReadTheDocs to get an in-depth look at the project, or check out the cookbook to see some examples showing the full process from downloading data to building a portfolio.

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Quality

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Support

PyPortfolioOpt has a medium active ecosystem.

It has 3558 star(s) with 828 fork(s). There are 131 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 37 open issues and 379 have been closed. On average issues are closed in 105 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyPortfolioOpt is 1.5.5

Quality

PyPortfolioOpt has 0 bugs and 0 code smells.

Security

PyPortfolioOpt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PyPortfolioOpt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyPortfolioOpt is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

PyPortfolioOpt releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 6525 lines of code, 436 functions and 34 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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PyPortfolioOpt Key Features

No Key Features are available at this moment for PyPortfolioOpt.

PyPortfolioOpt Examples and Code Snippets

No matching distribution found for scs==2.1.4

Python

Lines of Code : 8

License : Strong Copyleft (CC BY-SA 4.0)

Copy

  - pip:
    - backtrader==1.9.76.123
    - livelossplot==0.5.4
    - pyportfolioopt==1.4.1
    - qdldl==0.1.5.post0
    - osqp==0.6.2.post0
    - pymdptoolbox==4.0b3

Community Discussions

Trending Discussions on PyPortfolioOpt

cvxpy: Converting a nonlinear constraint to an equivalent linear constraint

QUESTION

cvxpy: Converting a nonlinear constraint to an equivalent linear constraint

Asked 2020-Apr-24 at 15:35

Context: I'm the developer of PyPortfolioOpt, a python portfolio optimisation library, and I'm trying to allow users to add constraints to a maximum Sharpe ratio problem.

Currently, users can pass their constraints as a lambda function, e.g to make all weights greater than 1%:

...

ANSWER

Answered 2020-Apr-24 at 15:35

Perhaps there is some API for decomposing an already instantiated constraint so that I can put in a variable?

Constraints are immutable by design. Immutability simplifies much of CVXPY’s logic.

Why not construct a new constraint? You can certainly inspect the left and right hand sides of the constraint. Right now, that can be done by inspecting the args attribute (see https://github.com/cvxgrp/cvxpy/blob/master/cvxpy/constraints/nonpos.py#L97).

Source https://stackoverflow.com/questions/61338298

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install PyPortfolioOpt

If you would like to play with PyPortfolioOpt interactively in your browser, you may launch Binder here. It takes a while to set up, but it lets you try out the cookbook recipes without having to deal with all of the requirements.