Artificial Intelligence in Quantitative Trading: Application Pipeline, Prospects, and Risk Governance

Abstract

Artificial intelligence (AI) has become a significant force in the field of quantitative trading because it extends traditional rule-based systems to areas such as adaptive prediction, dynamic configuration and automated execution. At the same time, the widespread adoption of machine learning, reinforcement learning, and workflows based on large language models in financial practice has raised concerns about data overfitting, data vulnerability, herding effects, systemic risk, and governance failure. This paper provides a comprehensive review and conceptual synthesis of the application of AI in quantitative trading by integrating recent literature on machine learning, prediction and portfolio optimization in financial markets, strategy mining driven by large language models, quantitative crisis management, and AI-based risk management. This paper follows the structure of actual trading processes and explores how AI supports data collection, feature engineering, model development, portfolio construction, and execution. It further identifies four representative risk categories: model risk, data dependency and quality risk, market liquidity and volatility risk, and operational and cybersecurity risk. Based on these findings, this paper proposes a multi-layered governance framework that combines robust model engineering, investment-level controls, implementation safeguards, human oversight, and regulatory coordination. This paper argues that compared to replacing human judgment with fully autonomous algorithms, the future of quantitative trading relies more heavily on building auditable systems that combine data discipline, model validation, risk control, and human oversight.