交易策略
强化学习
算法交易
计算机科学
杠杆(统计)
人工智能
金融市场
机器学习
模仿
市场数据
高频交易
外汇市场
马尔可夫决策过程
马尔可夫过程
财务
经济
汇率
统计
社会心理学
数学
心理学
作者
Yang Liu,Qi Liu,Hongke Zhao,Pan Zhen,Chuanren Liu
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2020-04-03
卷期号:34 (02): 2128-2135
被引量:59
标识
DOI:10.1609/aaai.v34i02.5587
摘要
In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and finding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Specifically, considering the noisy financial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real financial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.
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