Investor-Imitator: A Framework for Trading Knowledge Extraction
Investor-Imitator: A Framework for Trading Knowledge Extraction
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DOI：
10.1145/3219819.3220113
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Yi Ding;Weiqing Liu;Jiang Bian;Daoqiang Zhang;Tie-Yan Liu
中科院分区：
文献类型：
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作者：
Yi Ding;Weiqing Liu;Jiang Bian;Daoqiang Zhang;Tie-Yan Liu
股票交易在现实世界中是一种流行的投资方式。然而，由于缺乏足够的领域知识和经验，普通投资者很难对成千上万只股票进行人工分析。算法投资提供了另一种理性的方式，将人类的知识作为交易代理来表述。然而，在如此动荡的市场中设计有效的交易算法仍然需要良好的知识和经验。幸运的是，在这个大数据时代，各种历史交易记录很容易获得，提取隐藏在数据中的交易知识，帮助人们做出更好的决策，对我们来说是非常宝贵的。在本文中，我们提出了一个强化学习驱动的投资者-模仿者框架，通过一组逻辑描述符模仿投资者的行为来形式化交易知识。特别是，为了实例化特定的逻辑描述符，我们引入了Rank-Invest模型，该模型可以通过学习优化不同的评估指标来保持逻辑描述符的多样性。在实验中，我们首先模拟了三种类型的投资者，代表了我们在真实市场中可能遇到的不同程度的信息披露。通过对这些投资者的学习，我们可以从经验上了解目标投资者与投资者模仿者的内在交易逻辑，从中提取的可解释知识可以帮助我们更好地理解和构建交易组合。本文的实验结果充分证明了投资者-模仿者的设计目的，使投资者-模仿者成为金融投资研究中一个适用且有意义的智能交易框架。
Stock trading is a popular investment approach in real world. However, since lacking enough domain knowledge and experience, it is very difficult for common investors to analyze thousands of stocks manually. Algorithmic investment provides another rational way to formulate human knowledge as a trading agent. However, it still requires well-built knowledge and experience to design effective trading algorithms in such a volatile market. Fortunately, various kinds of historical trading records are easy to obtain in this big-data era, it is invaluable of us to extract the trading knowledge hidden in the data to help people make better decisions. In this paper, we propose a reinforcement learning driven Investor-Imitator framework to formalize the trading knowledge, by imitating an investor's behavior with a set of logic descriptors. In particular, to instantiate specific logic descriptors, we introduce the Rank-Invest model that can keep the diversity of logic descriptors by learning to optimize different evaluation metrics. In the experiment, we first simulate three types of investors, representing different degrees of information disclosure we may meet in real market. By learning towards these investors, we can tell the inherent trading logic of the target investor with the Investor-Imitator empirically, and the extracted interpretable knowledge can help us better understand and construct trading portfolios. Experimental results in this paper sufficiently demonstrate the designed purpose of Investor-Imitator, it makes the Investor-Imitator an applicable and meaningful intelligent trading framework in financial investment research.