Using Reinforcement Learning in the Algorithmic Trading Problem

被引：15

作者：

Ponomarev, E. S. ^{[1
]}

Oseledets, I. V. ^{[1
,2
]}

Cichocki, A. S. ^{[1
]}

机构：

[1] Skolkovo Inst Sci & Technol, Moscow, Russia

[2] Russian Acad Sci, Marchuk Inst Numer Math, Moscow, Russia

来源：

JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS | 2019年 / 64卷 / 12期

关键词：

algorithmic trading; reinforcement learning; neural network; recurrent neural networks;

D O I：

10.1134/S1064226919120131

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The development of reinforced learning methods has extended application to many areas including algorithmic trading. In this paper trading on the stock exchange is interpreted into a game with a Markov property consisting of states, actions, and rewards. A system for trading the fixed volume of a financial instrument is proposed and experimentally tested; this is based on the asynchronous advantage actor-critic method with the use of several neural network architectures. The application of recurrent layers in this approach is investigated. The experiments were performed on real anonymized data. The best architecture demonstrated a trading strategy for the RTS Index futures (MOEX:RTSI) with a profitability of 66% per annum accounting for commission. The project source code is available via the following link: http://github.com/evgps/a3c_trading..

引用

页码：1450 / 1457

页数：8

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