NFT alım-satımında veriye dayalı bir yaklaşım: Q-öğrenme tabanlı simülatör

Non-fungible tokens (NFTs) have garnered considerable attention in recent years due to their broad range of applications and potential as a lucrative investment opportunity. Given the nascent nature of the technology and the scarcity of comprehensive studies, there is a pressing need for a holistic trading framework that not only addresses the complexities inherent in the trading process but also proposes viable solutions to existing challenges. This study introduces a novel approach for navigating the NFT trading landscape, effectively confronting the various challenges and suggesting practical solutions. The trading environment is modeled using a Markov Decision Process (MDP), with Q-learning employed to simulate the environment and resolve the MDP problem. The study proposes machine learning models to tackle key challenges, including defining the market state, appraising NFT tokens, and addressing the illiquidity issue prevalent in the NFT market. The proposed approach yields an NFT trading strategy that has shown to outperform traditional strategies, generating substantial profits even amidst bearish market conditions. The Bored Ape Yacht Club (BAYC) collection serves as the primary data set, with the agent trained from June 1, 2021, through January 1, 2023. In testing period from January 1 to June 10, 2023, the proposed model outperformed traditional benchmarks, achieving a profit of 21.14% as opposed to a 20.39% loss for the best-performing benchmark. We assert that this forms a robust foundation for future research into NFT trading simulation and backtesting. We also identify potential areas for future enhancements, particularly possible improvements in the trading strategy and Q-learning approach. The insights gleaned significantly enhance the understanding of the importance of AI applications in the rapidly evolving field of NFT trading.