The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase immunity to transaction costs over existing approaches. The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to changes in some key parameters.

翻译：本研究旨在基于图聚类算法的新型统计套利框架，开发一种有效的交易策略。通过融合量化方法与机器学习技术——包括凯利准则与集成机器学习分类器——以提升风险调整后收益，并增强对交易成本的抵御能力，从而改进现有方法。本研究致力于提供一种集成化的最优信号检测与风险管理方法。作为该方法的组成部分，我们提出并测试了针对日频交易策略中优化止盈与止损功能的创新方案。所有测试策略的表现均优于相应基准。最优技术组合与参数配置展现出显著优于相关基准的绩效指标。研究结果基于现实交易成本假设获得，但对部分关键参数的变化较为敏感。