DQN-Based Crypto Trading: A Hybrid Approach to Market-Aware Buy-Hold Decisions and Profit-Driven Selling

Cryptocurrencies have quickly become a significant part of the financial landscape, largely due to their potential for substantial returns. This study examines a trading model based on Deep Q-Network (DQN) that combines reinforcement learning with a hybrid decision-making approach. Unlike traditional RL models that treat buy, hold, and sell decisions as equal, this framework differentiates the decision-making process. It enables DQN to learn optimal strategies for buying and holding while using a rule-based mechanism focused on profit targets for selling. This hybrid method ensures the model effectively captures market trends and secures profits at set thresholds, thereby minimizing unnecessary risk exposure. By adapting to changing market conditions and automating trade execution, this approach seeks to enhance profitability while reducing the risks associated with cryptocurrency trading. Experimental results highlight the effectiveness of this method in volatile market conditions