Using Reinforcement Learning in the Algorithmic Trading Problem
This addresses the problem of optimizing trading strategies for financial markets, but it is incremental as it applies existing RL methods to a specific domain.
The paper tackled algorithmic trading by interpreting stock exchange trading as a Markov game and proposed a system using asynchronous advantage actor-critic with neural networks, achieving a profitability of 66% per annum for RTS Index futures.
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.