A Strategic Innovations for Stock Market Optimization by Using Data Science & Explainable AI in High Frequency Trading

In the era of technology, financial markets especially stock market has been completely transformed through the integration of data science and explainable artificial intelligence. This paper presents a comprehensive analysis of evolution in the high frequency trading with the support of XAI and data science which made it possible to do financial transactions in extremely high speeds, often within milliseconds. But with the execution of these techniques HFT raises few concerns regarding transparency, interpretability and complexity. This study explores how data science shapes the strategies of HFT such as market making, arbitrage strategies: simple arbitrage, triangle arbitrage, statistical arbitrage etc. This study also explores how XAI and data science techniques such as machine learning, big data analytics, momentum trading and quantum computing are being employed in optimizing trading strategies to improve the decision making and liquidity in stock market. It includes regulations of high frequency trading at global level and challenge faced by financial sector in adopting these technologies. This study investigates the qualitative Scopus indexed journal papers and IEEE explores paper for the referencing the data to find out the authenticated literature review. This study will help out to market participants such as traders, investors, policymakers who aims to navigate the future of finance in India.