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In recent years, a wide range of investment models have been created usingartificial intelligence. Automatic trading by artificial intelligence canexpand the range of trading methods, such as by conferring the ability tooperate 24 hours a day and the ability to trade with high frequency. Automatictrading can also be expected to trade with more information than is availableto humans if it can sufficiently consider past data. In this paper, we proposean investment agent based on a deep reinforcement learning model, which is anartificial intelligence model. The model considers the transaction costsinvolved in actual trading and creates a framework for trading over a longperiod of time so that it can make a large profit on a single trade. In doingso, it can maximize the profit while keeping transaction costs low. Inaddition, in consideration of actual operations, we use online learning so thatthe system can continue to learn by constantly updating the latest online datainstead of learning with static data. This makes it possible to trade innon-stationary financial markets by always incorporating current market trendinformation.

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