图 书 馆
投资者模仿者：交易知识提取框架Investor‑Imitator: A Framework for Trading Knowledge Extraction |
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| 课程网址: | http://videolectures.net/kdd2018_ding_investor_imitator/ |
| 主讲教师: | Yi Ding |
| 开课单位: | 南京航空航天大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 投资者模仿者; 交易知识提取框架; 实例化特定的逻辑描述符; 逻辑描述符模仿投资者 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-27：cyh |
| 最后编审: | 2023-03-27：cyh |
| 阅读次数: | 28 |