英文标题：
《An intelligent financial portfolio trading strategy using deep
Q-learning》
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作者：
Hyungjun Park, and Min Kyu Sim, and Dong Gu Choi
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最新提交年份：
2019
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英文摘要：
Portfolio traders strive to identify dynamic portfolio allocation schemes so that their total budgets are efficiently allocated through the investment horizon. This study proposes a novel portfolio trading strategy in which an intelligent agent is trained to identify an optimal trading action by using deep Q-learning. We formulate a Markov decision process model for the portfolio trading process, and the model adopts a discrete combinatorial action space, determining the trading direction at prespecified trading size for each asset, to ensure practical applicability. Our novel portfolio trading strategy takes advantage of three features to outperform in real-world trading. First, a mapping function is devised to handle and transform an initially found but infeasible action into a feasible action closest to the originally proposed ideal action. Second, by overcoming the dimensionality problem, this study establishes models of agent and Q-network for deriving a multi-asset trading strategy in the predefined action space. Last, this study introduces a technique that has the advantage of deriving a well-fitted multi-asset trading strategy by designing an agent to simulate all feasible actions in each state. To validate our approach, we conduct backtests for two representative portfolios and demonstrate superior results over the benchmark strategies.
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中文摘要：
投资组合交易者努力确定动态投资组合分配方案，以便在整个投资期内有效分配其总预算。本研究提出了一种新的投资组合交易策略，该策略通过深度Q学习训练智能代理识别最优交易行为。我们为投资组合交易过程建立了一个马尔可夫决策过程模型，该模型采用离散的组合动作空间，确定每种资产在预先指定的交易规模下的交易方向，以确保实用性。我们新颖的投资组合交易策略利用了三个特点，在现实交易中表现优异。首先，设计一个映射函数来处理和转换一个最初发现但不可行的动作，使之成为最接近最初提出的理想动作的可行动作。其次，通过克服维度问题，本研究建立了agent和Q网络模型，在预定义的动作空间中推导出多资产交易策略。最后，本研究介绍了一种技术，该技术的优点是通过设计一个代理来模拟每个状态下的所有可行操作，从而得出一个非常适合的多资产交易策略。为了验证我们的方法，我们对两个有代表性的投资组合进行了回溯测试，并证明其结果优于基准策略。
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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
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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