Robotics and Algorithmic Trading: A New Era in Stock Market Trend Analysis

Abstract

This paper uses machine learning to examine how robots and algorithmic trading have transformed stock market trend analysis. The main goals are to assess how these sophisticated systems improve prediction accuracy, trading efficiency, market liquidity, and their problems and policy consequences. The research synthesizes academic, industrial, and technical literature using secondary sources. Significant results show that robots and algorithmic trading have enhanced trading speed, accuracy, and market efficiency while increasing market volatility data quality and model overfitting issues. Machine learning improves trend analysis by spotting complicated patterns and improving trading techniques. These advances need solid regulatory frameworks to control risks, including market instability and ethical issues. Policy implications include circuit breakers and transparency standards to promote fair and stable markets. This study emphasizes balancing technology innovation with regulation to provide a safe and fair trade environment.