Using Deep Q Network to develop an autonomous agent for trading in the cryptocurrency market, Focusing on traders' behavior

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Eslami Seyed Mehrdad; Agha Sarram Mehdi; Zare Chahoki Mohamad Ali

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Journal Paper

Paper Information

Journal: Journal of Applied and Basic Machine Intelligence Research Year:2025 | Volume:2 | Issue:2 Page(s): 49-65

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Information Journal Paper

Title

Using Deep Q Network to develop an autonomous agent for trading in the cryptocurrency market, Focusing on traders' behavior

Author(s)

Eslami Seyed Mehrdad | Agha Sarram Mehdi | Zare Chahoki Mohamad Ali | Issue Writer Certificate

Keywords

autonomous Trading Agent

Deep Reinforcement Learning

Trades Behaviors

Deep Q Network

Abstract

Crypto currency market is a complex, uncertain and dynamic environment with significant volatility. Developing a trading strategy in this market is highly challenging and a key area of academic research. In this article, An autonomous Trading Agent has been designed to analyze the effects of traders' behavior (transactions they do) on changing market conditions. Although many factors influence the market, these effects impact ultimately through traders behaviors. In this article, The agent makes decisions only by reviewing and analyzing the transactions which has been done by traders. The agent is built using DDQN reinforcement learning algorithm. To train the agent, all HitBTC`s transactions during nearly 3 months for 3 cryptocurrency pairs have been gathered. The results show that the model converges and is stable. As a result, The transactions data are important source for decision making. Combining this method with price prediction methods can be a new approach in designing trader agents.

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