Cvxportfolio Documentation

Cvxportfolio is a Python library for portfolio optimization. It enables users to quickly try optimization policies for financial portfolios by testing their past performance with a sophisticated market simulator.

Cvxportfolio is based on the book Multi-Period Trading via Convex Optimization (also available in PDF).

Installation

Cvxportolio is written in pure Python and can be easily installed in your favorite environment by simple:

pip install -U cvxportfolio

We show how this is done on our Installation and Hello World youtube video.

Example

Have a look at the Hello World Example to see Cvxportfolio in action.

Introduction

Cvxportfolio is an object-oriented library for portfolio optimization and backtesting which focuses on ease of use. It implements the models described in the accompanying book and can be extended with user-defined objects and methods to accommodate different data sources, custom cost models (both for simulation and optimization), constraints, and so on.

The main abstractions used are the MarketSimulator, which faithfully mimics the trading activity of a financial market, the collection of policies, which include both simple policies such as RankAndLongShort, and the optimization-based policies SinglePeriodOptimization and MultiPeriodOptimization. For these two, the user specifies the objective function (which is maximized) and a list of constraints which apply to the optimization. All these types of objects can be customized in many ways, including by deriving or redefining them.

Then, we provide the MarketData abstraction, which both serves historical data during a backtest and real-time data in online usage. We implement the interface to public data sources (Yahoo finance and FRED), as well as user-provided data (which can also be passed to all other objects).

In addition, we provide logic to easily parallelize backtesting of many different policies, or the same policy with different choices of hyperparameters, and cache on disk both historical data (for reproducibility) and various expensive calculations, such as estimates of covariance matrices.

We present the results of each backtest with a clear interface, BacktestResult, which defines various metrics of backtest performance and the logic to both print and plot them.

Testing locally

We ship our unit test suite with the software package, so after installing you can test in your local environment with:

python -m cvxportfolio.tests

We test against recent python versions (3.9, 3.10, 3.11) and recent versions of the main dependencies (from pandas 1.4, cvxpy 1.1, …, up to the current versions) on all major operating systems.

Licensing

Cvxportfolio is licensed under the Apache 2.0 permissive open source license.

Academic

If you use cvxportfolio for academic work you can cite the book it is based on:

@book{BBDKKNS:17,
    author       = {S. Boyd and E. Busseti and S. Diamond and R. Kahn and K. Koh and P. Nystrup and J. Speth},
    title        = {Multi-Period Trading via Convex Optimization},
    journal      = {Foundations and Trends in Optimization},
    year         = {2017},
    month        = {August},
    volume       = {3},
    number       = {1},
    pages        = {1--76},
    publisher    = {Now Publishers},
    url          = {http://stanford.edu/~boyd/papers/cvx_portfolio.html},
}