Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs

TL;DR

This paper presents a deep reinforcement learning-based investment agent for forex trading that considers transaction costs and uses online learning to adapt to non-stationary markets, aiming for high profits with low costs.

Contribution

It introduces a novel online learning framework for deep reinforcement learning in forex trading that accounts for transaction costs and adapts to changing market conditions.

Findings

The model effectively incorporates transaction costs into trading decisions.

Online learning enables the system to adapt to market changes in real-time.

The approach achieves higher profitability compared to static models.

Abstract

In recent years, a wide range of investment models have been created using artificial intelligence. Automatic trading by artificial intelligence can expand the range of trading methods, such as by conferring the ability to operate 24 hours a day and the ability to trade with high frequency. Automatic trading can also be expected to trade with more information than is available to humans if it can sufficiently consider past data. In this paper, we propose an investment agent based on a deep reinforcement learning model, which is an artificial intelligence model. The model considers the transaction costs involved in actual trading and creates a framework for trading over a long period of time so that it can make a large profit on a single trade. In doing so, it can maximize the profit while keeping transaction costs low. In addition, in consideration of actual operations, we use online learning so that the system can continue to learn by constantly updating the latest online data instead of learning with static data. This makes it possible to trade in non-stationary financial markets by always incorporating current market trend information.