Online portfolio selection is a fundamental problem in computational finance,
which has been extensively studied across several research communities,
including finance, statistics, artificial intelligence, machine learning, and
data mining, etc. This article aims to provide a comprehensive survey and a
structural understanding of published online portfolio selection techniques.
From an online machine learning perspective, we first formulate online
portfolio selection as a sequential decision problem, and then survey a variety
of state-of-the-art approaches, which are grouped into several major
categories, including benchmarks, "Follow-the-Winner" approaches,
"Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and
"Meta-Learning Algorithms". In addition to the problem formulation and related
algorithms, we also discuss the relationship of these algorithms with the
Capital Growth theory in order to better understand the similarities and
differences of their underlying trading ideas. This article aims to provide a
timely and comprehensive survey for both machine learning and data mining
researchers in academia and quantitative portfolio managers in the financial
industry to help them understand the state-of-the-art and facilitate their
research and practical applications. We also discuss some open issues and
evaluate some emerging new trends for future research directions.

本文旨在提供关于在线投资组合选择技术的一揽子研究，其中包括基准线、赢家追踪方法、输家追踪方法、基于模式匹配的方法和元学习算法等在内的多个状态性方法，同时探讨这些算法与资本增长理论之间的关系。此文旨在帮助研究人员和投资经理更好地了解这些技术及其潜在应用，并评估未来研究方向的新趋势。