论文标题
从半无限制的约束到结构化的强大政策：金融系统的最佳增益选择
From Semi-Infinite Constraints to Structured Robust Policies: Optimal Gain Selection for Financial Systems
论文作者
论文摘要
本文研究了在\ emph {双线性策略}框架内制定的金融交易系统的强大最佳增益选择问题，该框架在长期和短职位上分配了资本。关键目的是保证\ emph {可靠的正预期}（RPE）在一系列不确定的市场条件下均匀地利润，同时确保风险控制。此问题通过\ emph {semi-infinite}约束导致强大的优化公式，其中不确定性是由一组可能的返回参数集对象建模的。我们通过将半无限制约束转换为结构化策略来解决这一问题 - \ emph {balanged}策略和\ emph {互补}策略 - 可以显式地表征最佳解决方案。此外，我们提出了一种新型的图形方法，以有效地解决强大的增益选择问题，从而大大降低计算复杂性。与常规策略相比，对历史股票价格数据的经验验证在风险调整后的收益和下行风险方面表明了卓越的绩效。该框架通过合并鲁棒性注意事项来概括经典的均值优化，从而为不确定性下的稳健交易提供了系统，有效的解决方案。
This paper studies the robust optimal gain selection problem for financial trading systems, formulated within a \emph{double linear policy} framework, which allocates capital across long and short positions. The key objective is to guarantee \emph{robust positive expected} (RPE) profits uniformly across a range of uncertain market conditions while ensuring risk control. This problem leads to a robust optimization formulation with \emph{semi-infinite} constraints, where the uncertainty is modeled by a bounded set of possible return parameters. We address this by transforming semi-infinite constraints into structured policies -- the \emph{balanced} policy and the \emph{complementary} policy -- which enable explicit characterization of the optimal solution. Additionally, we propose a novel graphical approach to efficiently solve the robust gain selection problem, drastically reducing computational complexity. Empirical validation on historical stock price data demonstrates superior performance in terms of risk-adjusted returns and downside risk compared to conventional strategies. This framework generalizes classical mean-variance optimization by incorporating robustness considerations, offering a systematic and efficient solution for robust trading under uncertainty.
