Validation is a design decision, not a final checkbox.
Reproducible experiments, leakage checks, and clear baselines for AI-assisted trading research.
A quantitative research pipeline should define its evaluation before it celebrates a result. Data preparation, feature construction, training, and testing all need boundaries that reflect the intended decision time.
Chronological splits help prevent a model from being trained on future observations. They are one part of a broader process: inspect feature availability, corporate-action handling, missing data, and the separation between experiment selection and final evaluation.
AI-assisted research can increase the number of hypotheses explored. That makes experiment records, selection criteria, and reproducibility more useful. Microsoft Qlib provides a structured example of the stages between data, models, and portfolio workflows.
A practical deliverable includes versioned code and data assumptions, comparisons with simple baselines, sensitivity checks, and realistic costs. Forward paper trading adds operational evidence before a project is considered for live deployment. Every step should answer a specific question about the system.
This perspective is research commentary. It does not constitute investment advice or evidence of live strategy performance. Experimental methods require project-specific testing. Equations and code illustrate research formulations, not measured live returns.