OLPS - Online Portfolio Selection via Machine Learning

OLPS is a research project for studying online portfolio selection using state-of-the-art machine learning algorithms. In this task, a portfolio manager is a decision maker, whose goal is to produce a portfolio strategy, aiming to maximize the cumulative wealth. He/she computes the portfolios sequentially. In each period, the manager has access to the sequence of previous price relative vectors. Then, he/she computes a new portfolio for next unknown price relative vector, where the decision criterion varies among different managers. The portfolio is scored based on portfolio period return. This procedure is repeated until the end, and the portfolio strategy is finally scored according to portfolio cumulative wealth.

Links

• Main Project Page
• Project on GitHub

The OLPS Project

The following Links are primarily designed to give a high level introduction to the research problem and how to use the toolbox. More detailed documentation, research papers, and manuals can be found on the Main Project Page, and GitHub. 


About OLPS

• Introduction
• Problem Setting
• Types of Strategies
• Useful References 
• Project members

OLPS Toolbox

• Purpose 
• Download
• Installation
• Tutorial on Usage
• Adding your own Strategies