Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs
This work addresses the challenge of profitable and adaptive automated trading for investors in financial markets, though it appears incremental as it builds on existing deep reinforcement learning methods with specific adaptations like transaction cost consideration and online learning.
The paper tackles the problem of automated trading in the Forex market by proposing a deep reinforcement learning agent that considers transaction costs and uses online learning to adapt to non-stationary conditions, resulting in a framework designed to maximize profit while minimizing costs over long periods.
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.