Objective-oriented quantitative investment: A specification-driven framework for automated synthesis of trading strategy pipelines

Abstract

Automated quantitative research has made striking progress, yet each system answers the same question: which strategy scores highest on a scalar metric? We argue this question is incomplete. Professional investors do not order "the highest return"; they order an identity--pure stock-selection alpha uncontaminated by style exposure, resilient in unilateral market declines, within turnover and capacity budgets. We call the incumbent paradigm result-oriented and propose Objective-Oriented Quantitative Investment (OOQI): a specification-driven framework in which (i) the full strategy pipeline is modeled as a typed design space of interchangeable modules with explicit interface contracts (8.85 x 10^8 assemblies in our reference instantiation); (ii) investor intent is formalized as a strategy profile specification--a composable language of measurable, falsifiable clauses from eight requirement families, with hard/soft semantics and an interaction algebra; and (iii) a compiler translates specifications into constrained assemblies and verifies satisfaction clause-by-clause. Because search over large assembly spaces inflates apparent satisfaction, we develop a verification protocol treating the satisfaction rate itself as a statistical object, subject to deflation for search width, temporal holdout, and random-assembly null models. A synthetic demonstration with 32 pipeline assemblies shows that result-oriented selection attains the top in-sample information ratio while satisfying only 25% of the specification, whereas specification-driven selection satisfies 100% of it at a 5.5% score cost. The accompanying theory shows satisfaction-driven synthesis is NP-hard in general yet constant-factor approximable in a conflict-free regime; specifications form a lattice dual to assemblies; each clause carries a Lagrangian shadow price; and rolling re-certification is anytime-valid via e-processes.