SRBMPR combinatorial purged cross-validation

A rigorous, non-calendar structural evaluation protocol designed to test market behavior hypotheses without artificial boundaries or information leakage. This research module details the application of Combinatorial Purged Cross-Validation (CPCV) to raw market psychology cycles.

Non-calendar structural cycle integrity

Traditional market analysis forces data into arbitrary calendar boxes such as monthly closes or fixed-week windows. Within the SRBMPR framework, a behavioral cycle is identified strictly through raw data continuity and endogenous market behavior. Calendar month does not equal an SRBMPR cycle. CPCV acts as an independent evaluation layer that respects these natural market horizons without imposing synthetic temporal partitions.

Eliminating information leakage via purging

Financial market structures and behavioral containers span multi-period horizons, extending from formation to apex and resolution. Standard cross-validation risks severe lookahead bias when overlapping containers cross training and testing splits. By implementing strict combinatorial purging, SRBMPR eliminates structural overlap, ensuring test partitions remain completely untainted by preceding or succeeding training states.

Combinatorial testing and robust validation

Rather than evaluating a single sequential path that may suffer from historical bias, CPCV generates multiple combinatorial paths across non-calendar partitions. This structural protocol ensures that behavioral psychology observations are robust across varying market regimes, offering institutional researchers, quants, and academic peers a fully transparent and auditable testing standard.