Deep Reinforcement Learning approaches to Online Portfolio Selection have
grown in popularity in recent years. The sensitive nature of training
Reinforcement Learning agents implies a need for extensive efforts in market
representation, behavior objectives, and training processes, which have often
been lacking in previous works. We propose a training and evaluation process to
assess the performance of classical DRL algorithms for portfolio management. We
found that most Deep Reinforcement Learning algorithms were not robust, with
strategies generalizing poorly and degrading quickly during backtesting.

近年来，深度强化学习在在线组合选择中的应用得到了广泛的关注。我们提出了一个新的培训和评估过程以评估古典深度强化学习算法在组合管理中的表现，发现大多数深度强化学习算法不够健壮，策略泛化能力差，在回归测试期间很快退化。

评估深度强化学习在在线投资组合管理中的稳健性

Benchmarking Robustness of Deep Reinforcement Learning approaches to  Online Portfolio Management

This work introduces the first small-loss and gradual-variation regret bounds
for online portfolio selection, marking the first instances of data-dependent
bounds for online convex optimization with non-Lipschitz, non-smooth losses.
The algorithms we propose exhibit sublinear regret rates in the worst cases and
achieve logarithmic regrets when the data is "easy," with per-iteration time
almost linear in the number of investment alternatives. The regret bounds are
derived using novel smoothness characterizations of the logarithmic loss, a
local norm-based analysis of following the regularized leader (FTRL) with
self-concordant regularizers, which are not necessarily barriers, and an
implicit variant of optimistic FTRL with the log-barrier.

本文介绍了在线组合优化中首个基于数据的小损失和渐变性遗憾界，并提出了相应算法实现，可在投资选择数目较多的情况下，拥有亚线性的遗憾率，并在某些情况下具有对数遗憾率。遗憾界是通过对对数损失的新光滑性特征、对遵循正则化领导者（FTRL）的本地基于范数的分析以及乐观 FTRL 的隐式变体得到的，后者采用对数障碍。

无 Lipschitzness 和 Smoothness 在线组合优化的数据相关上界

Data-Dependent Bounds for Online Portfolio Selection Without  Lipschitzness and Smoothness

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。