[Submitted on 26 Feb 2020]

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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:
this http URL.