Advances in Consumer Research
Issue 3 : 1102-1110
Original Article
Optimizing Cryptocurrency Trading Strategies through Artificial Intelligence and Blockchain Integration: A Multi-Model Framework for Predictive Analytics
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1
Associate Professor, Department of Management, BSSS Institute of Advanced Studies, Bhopal. Ema1Associate Professor, Department of Management, BSSS Institute of Advanced Studies, Bhopal.
2
Assistant Professor, Department of Management Studies, International Institute of Business Studies, Bangalore, Karnataka, India
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Assistant Professor, Department of Computer Application, Maharaja Surajmal Institute, New Delhi, India
4
Assistant Professor, Department of Business Management, Khalsa College For Women, Ludhiana, Punjab, India
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Professor, School of Business Joseph Institute of Business Administration, Chennai, Tamilnadu, India
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Professor, Amity School of Liberal Arts, Amity University, Manesar, Gurugram, Haryana, India
Abstract

Crypto markets feature volatility, fragmented data, and unstable trends. Conventional trading techniques tend to fall short in integrating real-time opportunity and hedging risks efficaciously. Artificial Intelligence (AI), with its ability to learn patterns, make decisions in real-time, and learn to adapt, can be a revolutionary chance for crypto trading optimization. Blockchain technology, however, with its transparent and decentralized design, can guarantee trading strategy execution integrity and accountability. This paper introduces an integrated system that combines AI-based trading models and smart contracts based on blockchain technologies to improve efficiency, transparency, and trust in crypto trading. From historical market data, social media sentiments, and current price inputs, this paper applies deep learning and reinforcement learning models in predicting patterns and making automated decisions. The blockchain feature enables traceability and secure implementation of strategies. Empirical evidence indicates that the hybrid system significantly surpasses conventional methods when it comes to return on investment and risk-adjusted return. The study yields significant implications for policymakers, traders, and FinTech designers.

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