Feasibility of AI-Based Algorithmic Trading in Iran: A Comparative and Structural Assessment

The integration of artificial intelligence (AI) into algorithmic trading has reshaped the efficiency and architecture of global financial markets. While emerging economies such as India and Turkey have made notable progress through regulatory support and technological infrastructure, Iran has seen only limited and informal use of algorithmic trading tools, with no widespread or officially sanctioned implementation. Despite a growing body of academic research in machine learning applications for financial forecasting, these efforts have not yet translated into live or testable trading systems. This study explores the feasibility of adopting AI-driven algorithmic trading in Iran. It applies a structured framework to assess five key dimensions: technical readiness, regulatory environment, market conditions, economic sanctions, and institutional research capacity. A comparative analysis with peer economies (India, Turkey, and Pakistan) helps contextualize Iran's position. The findings indicate that Iran lacks the integrated legal, technical, and infrastructural ecosystem necessary for scalable adoption. However, its academic foundation offers a valuable starting point. The paper concludes with policy and research recommendations, including regulatory sandboxes, simulated trading environments, and university-industry collaborations to bridge the current implementation gap.

کلیدواژه ها:

Algorithmic Trading ، Artificial Intelligence ، Financial Technology