Quant Sniping

Quantitative trading is not one strategy, and quant funds are not one coordinated enemy. Modern markets are shaped by high-frequency market makers, statistical arbitrage systems, trend-following funds, index strategies, volatility controls, execution algorithms, options hedging, and increasingly powerful artificial intelligence. Quant Sniping: Quantitative Trading in the Age of AI presents a practical framework for understanding this machine-driven market structure. It explains how quantitative firms generate returns, why large institutions must split orders, how factor mining and AI fit into research, why complex models often overfit, and how liquidity, leverage, capacity, risk limits, and crowding shape institutional behavior.

The central idea of quant sniping is not to attack quantitative funds or predict their private rules. It is to recognize mechanical behavior after it becomes visible, understand why it may persist, and determine when it is beginning, continuing, or losing effectiveness. Written for quantitative researchers, systematic traders, financially literate investors, and professionals interested in AI-driven markets, this book connects market microstructure, institutional constraints, risk management, factor research, and AI-assisted trading into one coherent quantitative research framework.