Model Retraining Protocols define the systematic procedures and schedules for updating the parameters or architecture of quantitative models, particularly those used in algorithmic trading and risk management. The primary purpose is to maintain model accuracy and relevance in dynamic market environments, especially within volatile crypto markets. This prevents model decay.
Mechanism
These protocols specify triggers for retraining, which can be time-based (e.g., daily, weekly), event-driven (e.g., significant market regime shifts, performance degradation), or data-driven (e.g., detection of concept drift in input data). The process involves re-collecting recent market data, re-estimating model parameters, and often re-validating the updated model against new or out-of-sample data before deployment.
Methodology
The methodology centers on continuous learning and adaptation, often employing techniques like rolling window analysis or online learning. It addresses issues such as data obsolescence and model decay, ensuring that predictive algorithms and risk systems remain calibrated to current market microstructure and participant behavior. This mitigates the risk of performance deterioration due to stale models.
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