universal-portfolios 0.4.11

Collection of algorithms for online portfolio selection

Universal Portfolios

The purpose of this Python package is to put together different Online Portfolio Selection (OLPS) algorithms and provide unified tools for their analysis.

In short, the purpose of OLPS is to choose portfolio weights in every period to maximize its final wealth. Examples of such portfolios could be the Markowitz portfolio or the Universal portfolio. There is currently an active research in the area of online portfolios and even though the results are mostly theoretical, algorithms for practical use start to appear.

Several state-of-the-art algorithms are implemented, based on my understanding of the available literature. Contributions or corrections are more than welcomed.

Outline of this package

• examples contains two Python Notebooks:

  • Online Portfolios : explains the basic use of the library. Script sequence, various options, method arguments, and a strategy template to get you started.
  • Modern Portfolio Theory : goes deeper into the OLPS principle and the tools developped in this library to approach it.

• universal.data contains various datasets to help you in your journey

• universal.algos hosts the implementations of various OLPS algorithms from the litterature :

Benchmarks	Follow the winner	Follow the loser	Pattern matching	Other
BAH	Universal Portfolios	Anticorr	BNN	Markovitz
CRP	Exponential Gradient	PAMR	CORN	Kelly
BCRP		OLMAR		Best so far
DCRP		RMR		ONS
		CWMR		MPT
		WMAMR
		RPRT

• universal.algo provides a general class inherited by all the individual algos' subclasses. Algo computes the weights at every timestep.

• universal.result computes the portfolio wealth from the weights and various metrics on the strategy's performance.

Quick Start

from universal import tools
from universal.algos import CRP

if __name__ == '__main__':
  # Run CRP on a computed-generated portfolio of 3 stocks and plot the results
  tools.quickrun(CRP())

Additional Resources

If you do not know what online portfolio is, look at Ernest Chan blog, CASTrader or a recent survey by Bin Li and Steven C. H. Hoi.

Paul Perry followed up on this and made a comparison of all algorithms on more recent ETF datasets. The original authors of some of the algorithms recently published their own implementation on GitHub - Online Portfolio Selection Toolbox in MATLAB.

If you are more into R or just looking for a good resource about Universal Portfolios, check out blog and package logopt by Marc Delvaux.

Note : If you don't want to install the package locally, you can run both notebooks with Binder - modern-portfolio-theory.ipynb or On-line portfolios.ipynb

Installation

Only Python 3 is supported

pip install universal-portfolios

Development

poetry is used to manage the dependencies. Run poetry install to install a virtual environment and then poetry shell to launch it.

Exporting dependencies to the requirements.txt file is needed for mybinder.org. It is done via

poetry export --without-hashes -f requirements.txt > requirements.txt

Formatting

We use pre-commit hook to automatically format code and check for linting errors before each commit. If the checks fail you need to resolve the errors and amend the change set.

To setup the pre-commit hooks you need to install it first and then enter the project root directory and invoke the command (only once!):

pre-commit install

Running Tests

poetry run python -m pytest --capture=no --ff -x tests/

Contributors

Creator : Marigold

Thank you for your contributions!

Alexander Myltsev | angonyfox | booxter | dexhunter | DrPaprikaa | paulorodriguesxv | stergnator | Xander Dunn

Disclaimer

This software is for educational purposes only and is far from any production environment. Do not risk money which you are afraid to lose. Use the software at your own risk. The authors assume no responsibility for your trading results.