英文标题：
《Robust Log-Optimal Strategy with Reinforcement Learning》
---
作者：
Yifeng Guo, Xingyu Fu, Yuyan Shi, Mingwen Liu
---
最新提交年份：
2018
---
英文摘要：
  We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion. It avoids GLOS\'s complex CDF estimation process,hence resists the \"Butterfly Effect\" caused by estimation error. Besides,RLOS retains GLOS\'s profitability and the optimization problem involved in RLOS is computationally far more practical compared to GLOS. Further, we combine RLOS with Reinforcement Learning (RL) and propose the so-called Robust Log-Optimal Strategy with Reinforcement Learning (RLOSRL), where the RL agent receives the analyzed results from RLOS and observes the trading environment to make comprehensive investment decisions. The RLOSRL\'s performance is compared to some traditional strategies on several back tests, where we randomly choose a selection of constituent stocks of the CSI300 index as assets under management and the test results validate its profitability and stability.
---
中文摘要：
我们提出了一种新的投资组合管理方法，称为鲁棒对数最优策略（RLOS），该方法通过二次泰勒展开逼近传统的目标函数来改进一般对数最优策略（GLOS）。它避免了GLOS复杂的CDF估计过程，从而抵抗了由估计误差引起的“蝴蝶效应”。此外，RLOS保留了GLOS的盈利能力，与GLOS相比，RLOS所涉及的优化问题在计算上更加实用。此外，我们将RLOS与强化学习（RL）相结合，提出了所谓的鲁棒强化学习对数优化策略（RLOSRL），其中RL代理接收RLOS的分析结果，并观察交易环境，以做出全面的投资决策。在多次回溯测试中，我们将RLOSRL的表现与一些传统策略进行了比较，在回溯测试中，我们随机选择CSI300指数的成分股作为管理资产，测试结果验证了其盈利能力和稳定性。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
--

---
PDF下载：
-->