This paper investigates the impact of behavioral biases, specifically loss aversion, regret aversion, reference dependence, and risk perception on algorithmic trading using the framework of Prospect Theory. Using structured questionnaire, the primary data was collected from the traders who use algorithm. Statistical tool, SmartPLS was employed to assess the endogenous factors and the behavioral biases that influence the intention to trade using algorithms. The findings indicate that risk perception and reference dependence significantly impact trading intent, whereas loss aversion and regret aversion do not show a significant influence on trading intent. This advocates that the systematic and emotion-free nature of algorithmic trading minimizes the effects of certain emotional biases. The study contributes profound understanding of behavioral biases of traders adopting algorithm offer distinctive path for future scope of research.