Core framework for creating quantitative models
Building a robust quant strategy means navigating a concise set of conceptual pillarsโfrom abstraction choices to causal interpretationโwhile keeping the process lean enough to ship code. The seven themes below condense an entire chapterโs worth of methodology into an actionable checklist.
Whatโs inside:
Abstraction hierarchy: Manage complexity via nested conceptual layersโfrom raw data elements up to portfolioโlevel behavioursโso each design decision lives at its natural level of generality.
System architecture & multiโhierarchical design: Map canonical hierarchy families (data, signals, orders) into interacting dynamic subsystems, preserving modularity and fault isolation.
Modelโmarket isomorphism & validity: Test both internal consistency and external generalisability to confirm the model faithfully mirrors the market mechanism it intends to exploit.
Modelling methodology: Specify how to model by selecting mathematical families, estimation techniques, and diagnostics that best align with the phenomenon and data structure.
Processingโlevel workflow: Align Elements โ Structure โ Sequence โ Process to transform inputs into executable strategies through a documented operational lifecycle.
Quantitative modelling pipeline: Follow the endโtoโend cycleโproblem formulation; data acquisition; feature engineering; model selection and tuning; validation; deployment; monitoringโwith tight feedback loops for continuous edge renewal.
Causality & simplicityโprecision balance: Make the causal stance explicit (predictive vs explanatory) and navigate the tradeโoff between parsimonious clarity and predictive granularity to protect confidence under uncertainty.
