From Rule-Based Trading to Adaptive Algorithms: A Review of AI in Financial Markets

Reyansh Mangal

From Rule-Based Trading to Adaptive Algorithms: A Review of AI in Financial Markets

Author(s)	Reyansh Mangal
Country	India
Abstract	Traditional financial frameworks, such as Modern Portfolio Theory (MPT), often fail to account for real-world frictions including transaction costs, information asymmetries, and gradual portfolio adjustment. This paper examines transition from these classical, rule-based systems to Artificial Intelligence (AI)-based trading models designed to navigate the complexities of modern, data-intensive markets. Current literature indicates that AI applications in trading can be categorized into three primary paradigms: Machine Learning (ML) for non-linear signal generation, Deep Learning (DL) for hierarchical feature extraction from high-dimensional and alternative data (such as sentiment analysis), and Reinforcement Learning (RL) for sequential decision-making that explicitly incorporates market constraints. A notable recent development is the rise of hybrid architectures that modularize the trading process – separating signal prediction from execution optimization to balance predictive power with operational robustness. Central to this transition is a shift in training methodologies, moving from static historical modeling towards dynamic walk-forward validation and simulated market environments. Despite technical advancements, empirical evidence suggests that AI does not offer a “silver bullet” for universal outperformance; rather, its efficacy is found in improved adaptability and execution efficiency under realistic frictions. However, the increased use of AI-based trading must account for critical ethical and systemic risks, including model opacity, algorithmic herding, and the concentration of technological advantages in the hands of a small pool of investors. While AI significantly enhances market responsiveness, its successful integration requires a synthesis of computational innovation with rigorous economic reasoning and transparent regulatory governance to ensure long-term market stability.
Keywords	Artificial Intelligence in Finance, Algorithmic Trading, Machine Learning, Deep Learning, Reinforcement Learning, Hybrid Trading Architectures, Market Frictions, Walk-Forward Validation, Systemic Risk, Algorithmic Transparency
Field	Mathematics > Economy / Commerce
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-13

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From Rule-Based Trading to Adaptive Algorithms: A Review of AI in Financial Markets

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