Hybrid Quantum – A Framework for High Frequency Stock Trading Using Reinforcement Learning

Le résumé fourni par la source

This Paper shows information about the High Speed Stock Trading, In todays era the normal computers are struggling to identify and find the complex market patterns and strategies. So in order to solve this issue we built a new system that is a combination of quantum computing and classical computing in which Reinforcement Learning (a way for computers to learn with trial and error) is used with Quantum Machine Learning to make smarter trading decisions and strategies. Specially, we have used a quantum model (called a Variational Quantum classifier) as a part of Deep Q Network agent to help the system learning better trading strategies. Our goal is to use the superior representational power of quantum feature spaces for financial decision making. The hybrid agent was trained in a high-fidelity simulation environment that includes realistic market frictions and a risk-adjusted reward function. Backtesting results on unseen data indicate that the hybrid agent consistently performs better than its traditional counterpart. It achieves a total return of $21.2 \%$, which is higher than the $\mathbf{1 8 . 5 \%}$ return from the classical model. Additionally, risk-adjusted performance has clearly improved. The hybrid agent’s Sortino Ratio is $32.6 \%$ higher and its Sharpe Ratio is $26.4 \%$ higher. Its maximum drawdown is also lower. These findings demonstrate that quantum models are able to detect minute market relationships that conventional approaches might miss. As a result, trading tactics become more reliable and successful. This research marks a substantial breakthrough in the application of quantum technologies to challenging computational finance problems.

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