AI-Driven Algorithmic Trading with Adaptive Market Regime Detection

Abstract

The increasing electronification of financial markets has transformed trading from a predominantly human activity into a complex socio-technical process in which algorithmic systems interact with heterogeneous market structures at extremely short timescales. This paper examines AI-driven algorithmic trading from a systems perspective, with particular emphasis on adaptive market regime detection. Rather than focusing narrowly on predictive accuracy or profitability, the paper treats regime detection as an infrastructure problem that links data ingestion, feature engineering, online learning, execution logic, risk constraints, and regulatory compliance. It argues that adaptive regime detection creates structural trade-offs between sensitivity and stability, between model complexity and explainability, and between autonomy and oversight. The paper develops a conceptual architecture for regime-aware trading systems in which detection modules, execution engines, risk governors, and human-in-the-loop controls are organized as loosely coupled layers. This architecture supports graceful degradation, auditability, and policy enforcement while retaining the ability to respond to changing volatility, liquidity, and information regimes. The discussion draws on market microstructure, machine learning, and regulatory literature to analyze failure modes including feedback, data drift, crowding, and procyclical behavior. Governance implications are examined with respect to fairness, market integrity, model risk management, and algorithmic accountability. The paper concludes by identifying future research directions, including cross-market regime synchronization, causal discovery, energy-efficient inference, and the design of resilient institutions for autonomous trading systems.