This paper investigates the problem of ensembling multiple strategies for sequential portfolios to outperform individual strategies in terms of long-term wealth. Due to the uncertainty of strategies' performances in the future market, which are often based on specific models and statistical assumptions, investors often mitigate risk and enhance robustness by combining multiple strategies, akin to common approaches in collective learning prediction. However, the absence of a distribution-free and consistent preference framework complicates decisions of combination due to the ambiguous objective. To address this gap, we introduce a novel framework for decision-making in combining strategies, irrespective of market conditions, by establishing the investor's preference between decisions and then forming a clear objective. Through this framework, we propose a combinatorial strategy construction, free from statistical assumptions, for any scale of component strategies, even infinite, such that it meets the determined criterion. Finally, we test the proposed strategy along with its accelerated variant and some other multi-strategies. The numerical experiments show results in favor of the proposed strategies, albeit with small tradeoffs in their Sharpe ratios, in which their cumulative wealths eventually exceed those of the best component strategies while the accelerated strategy significantly improves performance.

翻译：本文研究了为顺序投资组合集成多种策略以在长期财富方面超越单一策略的问题。由于策略在未来市场中的表现存在不确定性，且这些策略通常基于特定模型和统计假设，投资者常通过组合多种策略来降低风险并增强鲁棒性，类似于集成学习预测中的常见方法。然而，由于缺乏无分布且一致的偏好框架，组合决策因目标不明确而变得复杂。为填补这一空白，我们引入了一种新颖的策略组合决策框架，该框架不受市场条件影响，通过建立投资者在决策间的偏好来形成明确目标。基于此框架，我们提出了一种无需统计假设的组合策略构建方法，适用于任意规模（甚至无限）的组件策略，使其满足既定准则。最后，我们测试了所提策略及其加速变体以及其他一些多策略方法。数值实验结果表明，尽管夏普比率存在微小权衡，所提策略的累积财富最终超越了最佳组件策略，而加速策略则显著提升了性能。