Quantitative Trading on Stock Market Based on Deep Reinforcement Learning

被引：0
|
作者：
Wu, Jia [1 ]
Wang, Chen [1 ]
Xiong, Lidong [1 ]
Sun, Hongyong [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu, Peoples R China
来源：
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2019年
基金：
中国国家自然科学基金;
关键词：
Quantitative trading; Deep learning; Reinforcement learning; Deep reinforcement learning; MODEL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With the development of computer science technology and artificial intelligence, quantitative trading attracts more investors due to its efficiency and stable performance. In this paper, we explore the potential of deep reinforcement learning in quantitative trading. A LSTM-based agent is proposed to learn the temporal pattern in data and automatically trades according to the current market condition and the historical data. The input to the agent is the raw financial data and the output of the agent is decision of trading. The goal of the agent is to maximize the ultimate profit. Besides, to reduce the influence of noise in the market and to improve the performance of the agent, we use several technical indicators as an extra input. The proposed system has been back-tested on the stock market. The results demonstrate that our method performs well in most conditions.
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页数：8
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