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Using Reinforcement Learning in the Algorithmic Trading Problem

Authors :
Ponomarev, Evgeny
Oseledets, Ivan
Cichocki, Andrzej
Source :
ISSN 1064-2269, Journal of Communications Technology and Electronics, 2019, Vol. 64, No. 12, pp. 1450-1457
Publication Year :
2020

Abstract

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.

Details

Database :
arXiv
Journal :
ISSN 1064-2269, Journal of Communications Technology and Electronics, 2019, Vol. 64, No. 12, pp. 1450-1457
Publication Type :
Report
Accession number :
edsarx.2002.11523
Document Type :
Working Paper
Full Text :
https://doi.org/10.1134/S1064226919120131