Advancing Adaptive Quantitative Trading Systems through Continual Learning Architectures Designed for Non-Stationary Financial Distribution Shifting Environments
Keywords:
Continual Learning, Quantitative Trading, Non-Stationary Environments, Systemic Risk, Adaptive AI, Financial Infrastructure, Distribution Shifting.Abstract
The integration of artificial intelligence within quantitative trading has historically struggled with the phenomenon of non-stationarity, where the statistical properties of financial markets evolve unpredictably over time. Traditional machine learning paradigms, which rely on the assumption of independent and identically distributed data, often fail as market regimes shift, leading to catastrophic forgetting and model obsolescence [16]. This paper proposes a comprehensive architectural framework for adaptive quantitative trading systems based on the principles of continual learning. By transitioning from static retraining cycles to dynamic, lifelong learning infrastructures, these systems can mitigate the risks associated with distribution shifts [8]. We explore the structural trade-offs between stability and plasticity, the necessity of memory-augmented architectures for preserving historical market intelligence, and the socio-technical implications of deploying such autonomous agents within global financial infrastructures. The research emphasizes the critical role of robust governance and ethical policy frameworks in managing the systemic risks introduced by high-frequency adaptive behaviors [1]. Through a systems-level analysis, we argue that the future of resilient financial engineering lies not in larger models, but in more agile, self-evolving architectures capable of navigating the perpetual flux of global capital markets [18].
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