An Introduction To Regret Minimization In Algorithmic Trading: A Survey of Universal Portfolio Techniques

TL;DR

This survey introduces universal portfolio techniques in algorithmic trading, focusing on regret minimization algorithms that guarantee performance without relying on market data assumptions.

Contribution

It provides a comprehensive overview of universal portfolio methods, including foundational concepts, algorithms, and recent advancements in the field.

Findings

Overview of Constant Rebalanced Portfolios

Analysis of Cover's Algorithm

Inclusion of transaction costs and side information

Abstract

In financial investing, universal portfolios are a means of constructing portfolios which guarantee a certain level of performance relative to a baseline, while making no statistical assumptions about the future market data. They fall under the broad category of regret minimization algorithms. This document covers an introduction and survey to universal portfolio techniques, covering some of the basic concepts and proofs in the area. Topics include: Constant Rebalanced Portfolios, Cover's Algorithm, Incorporating Transaction Costs, Efficient Computation of Portfolios, Including Side Information, and Follow The Leader Algorithm.