QUANTUM AND AI-POWERED ALGORITHMIC TRADING WITH ETF

This paper proposes a hybrid trading system that leverages quantum computing and artificial intelligence  (AI) to trade exchange-traded funds (ETFs) efficiently. The system harnesses quantum computing’s  ability to process vast data points simultaneously, employing algorithms like quantum annealing for  accelerated decision-making and portfolio optimization. AI models, powered by deep learning,  reinforcement learning, and natural language processing (NLP), analyze financial articles to train on  current affairs, enabling the system to imitate human-like decision-making by extracting sentiment,  trends, and contextual insights from real-time news. 

This synergy delivers faster analysis and superior risk control over classical trading systems. The  framework integrates quantum processors with classical computers via a cloud infrastructure for seamless  data flow. Experimental analysis using ETF market datasets and simulated intraday trading scenarios  demonstrates that the hybrid approach achieves faster analytical convergence, improved risk management,  and more balanced portfolio allocations compared to traditional classical trading systems. Also, the  proposed model enhances trading performance without increasing market volatility, thereby supporting  market stability. This research demonstrates quantum computing’s practical application in ETF intraday  trading, boosting profits without disrupting market stability, to build resilient, high-performance financial  trading platforms.