Trading Software Using Blockchain Technology

Abstract

Traders and speculators are paying more attention to Bitcoin as it becomes a more well recognized asset globally. Due to its extreme volatility, the cryptocurrency market has drawn interest from those looking to take advantage of large price swings for possible financial benefits. This study explores algorithmic trading tactics in the Bitcoin market, focusing on daily price fluctuations using directional categorization. Expanding on previous research, our method improves machine learning models' predictive power by integrating a wide range of characteristics, including as external variables and internal Bitcoin network metrics. In order to verify the performance among our models, we empirically assess them with gathered real-world trade data in the initial three months of 2023. When a predictor that is binary is used, our models show an average by the conclusion of the triennial trading cycle, 86% revenue, which is consistent with the results of conventional buy-and-hold tactics. Interestingly, our models incorporate a risk-perception index that is derived from prediction assurance of the models, which goes beyond traditional tactics. Performance gains from this combination outpace the returns from a straightforward buy-and-hold strategy by a factor of 12.5. These results highlight how machine learning models have a great deal of potential for profitably mining the volatile Bitcoin market. The efficacy noted in practical trading situations stimulates additional investigation and study in the area of trading algorithms tactics customized in relation to Bitcoin network.Top of Form