AI-Driven Algorithmic Engine: Transforming Crypto Asset Management with Clustering Technique

Publication Date

2-17-2026

Document Type

Contribution to a Book

Publication Title

Studies in Big Data

Volume

180

DOI

10.1007/978-3-032-02428-2_17

First Page

391

Last Page

406

Abstract

Cryptocurrency trading becoming increasingly popular has led to a demand for automated and intelligent investment strategies. The present initiative aims to deliver an artificial intelligence-configured asset management protocol exclusively for crypto investments that combine machine learning and deep learning methods in granting trading recommendations. The system computes technical indicators such as Bollinger Bands, Fibonacci retracements, moving averages, and support/resistance levels using clustering techniques. In contrast, a deep learning system further refines these and issues final trading signals based on a holistic market analysis. The backend has been developed using FAST API to allow seamless movement of data to the frontend. Users can view real-time algorithmic decisions and investment activities presented to them via a transparent and easy-to-use dashboard. The platform supports deposits and withdrawals and comes with vaults for all the major cryptocurrencies, including BTC, ETH, and INJ. The Injective blockchain was chosen because of its fast transactions, low gas fee, and Helix DEX integration; thus, increased liquidity and accessibility. This approach of AI-based crypto asset management helps in better decision-making by AI reduction of human intervention and systematizing the approach from investor perspectives. The adaptive learning feature ensures that the system can dynamically respond to adaptive market conditions for enhanced performance. The presence of the back-testing system allows the validation of strategies before their full deployment, ensuring their robustness in various market scenarios. An asset management system is implemented on Injective blockchain and integrated with Helix DEX so as to make it trader-friendly and accessible. The model gets continually upgraded with fresh sets of data and insights, resulting in a highly trusted and forward-thinking trading tool that equips traders to drift through diverse market environments confidently through rigorous data-driven insights and advanced AI capabilities.

Keywords

Automated technical analysis, Bollinger bands, Crypto asset management, FAST API, Fibonacci retracements, Helix DEX, Injective blockchain, Machine learning in trading, Support and resistance clustering

Department

Mathematics and Statistics

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