Publication Type

Journal Article

Publication Date

4-2016

Abstract

On-line portfolio selection is a practical financial engineering problem, which aims to sequentially allocate capital among a set of assets in order to maximize long-term return. In recent years, a variety of machine learning algorithms have been proposed to address this challenging problem, but no comprehensive open-source toolbox has been released for various reasons. This article presents the first open-source toolbox for "On-Line Portfolio Selection" (OLPS), which implements a collection of classical and state-of-the-art strategies powered by machine learning algorithms. We hope that OLPS can facilitate the development of new learning methods and enable the performance benchmarking and comparisons of different strategies. OLPS is an open-source project released under Apache License (version 2.0), which is available at github.com/OLPS/OLPS or OLPS.stevenhoi.org.

Keywords

On-line portfolio selection, online learning, trading system, simulation

Discipline

Computer Sciences | Databases and Information Systems | Finance and Financial Management

Research Areas

Data Management and Analytics

Publication

Journal of Machine Learning Research

Volume

17

Issue

35

First Page

1

Last Page

5

ISSN

1532-4435

Publisher

Journal of Machine Learning Research / Microtome Publishing

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://jmlr.org/papers/volume17/15-317/15-317.pdf

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