Abstract

Quantitative trading models have become an essential analytical foundation for modern financial markets particularly in high-frequency trading algorithmic strategies and risk-sensitive decision systems. Yet beneath the observable statistical behaviors of asset prices lie hidden components that significantly influence market outcomes. These hidden components include latent variables unobserved market factors micro-structural noise behavioral irregularities and dynamic feedback loops that escape standard modeling frameworks. Understanding these hidden dimensions is crucial because financial markets rarely behave as perfectly transparent systems rather they operate through complex interconnected structures where the interaction between visible price signals and invisible drivers shapes patterns of volatility momentum liquidity arbitrage breakdown and market reversals. This research paper explores the philosophical analytical and methodological foundations of quantitative trading models wi