Improved Intelligent Model for Cryptocurrency Trading in Blockchain Platform
by Dr. B. B. Baridam, Ndueso, Etukudo Ekefre, Prof. C. Ugwu
Published: March 3, 2026 • DOI: 10.51584/IJRIAS.2026.110200039
Abstract
In studying Blockchain Technology, one of its predominant applications that have provided a massive growth in their recent global acceptance and market capitalization in the past few years is cryptocurrencies. Individual investors, notable institutions and corporate firms are readily investing in it. Predicting cryptocurrency prices owing to their volatile nature has been a challenging decision for researchers owing to social and psychological factors that affect price of cryptocurrency. Substantively, the crypto market is highly volatile when compared to the traditional commodity markets and may be affected by factors like sentimental, legal and other technical indicators. The uncertainty and unpredictable nature of cryptocurrency necessitated this study on Improved Intelligent Model for Cryptocurrency Trading in Blockchain Platform. The five cryptocurrencies utilized in this work were Bitcoin (BTC), Ethereum (ETH), XRP, Cadona (ADA), Solana (SOL). This study incorporated Bi-LSTM and Attention Mechanism techniques with trading strategies like buy, sell or hold depending on the choice of the investors. It is depicted that our model yields more accurate and reliable predictions when confirmed alongside with the Live price time-based model. This work provided with guarantee an interface that can be used by investors especially those in cryptocurrency trade for accurate predictions as it will go a long way in extenuating investment risk. This research adopts Object-Oriented Programming (OOP) methodology to design and implement an intelligent cryptocurrency trading system. This study was implemented using C# Programming Language with incorporation of python.NET. The system can be used in real-time scenarios as it is well trained and evaluated using standard data sets. The result depicted that this new system predicts cryptocurrency prices with high accuracy compared to the existing system. The outcome of this study assured us that our approach enhances the necessary assurance on the new system and offers customers a more reliable financial service in cryptocurrency trade.