Objective-Oriented Quantitative Investment: A Specification-Driven Framework for Automated Synthesis of Trading Strategy Pipelines

Battle Count: 65

This paper is directly and fundamentally about quantitative trading strategy construction. It proposes a complete paradigm shift from result-oriented (scalar metric maximization) to objective-oriented (specification satisfaction) strategy synthesis. The framework addresses core practitioner concerns: style purity, regime robustness, execution feasibility, capacity constraints, and backtest overfitting. It provides formal propositions proving that the two paradigms select different strategies, quantifies the cost of specification, and supplies a complete verification protocol. The design space covers the full pipeline from universe selection through risk overlay. The work is grounded in established quantitative finance literature (Sharpe, Fama-French, Clarke et al., Bailey & Lopez de Prado) and directly addresses how professional capital is allocated in practice.

The framework is extremely complex, comprising: (1) a typed design space with 9 stages, module contracts, and side-effect signatures; (2) a specification language with 8 families, 30+ clauses, hard/soft semantics, priorities, and interaction algebra; (3) a three-stage compiler (translation, assembly, certification); (4) a verification protocol with search ledger, temporal isolation, random-assembly null, deflated satisfaction, satisfaction-PBO, and stress certification; (5) a Bayesian compiler with tempered likelihood; (6) inverse-problem formalization with Tikhonov regularization; (7) NP-hardness theory with constant-factor approximation regimes; (8) Galois-connection duality; (9) Lagrangian shadow prices; (10) anytime-valid e-process certification; (11) engineering schemas for specification, certificate, and module registry; (12) governance and operations lifecycle. The reference implementation is ~800 lines of Python for the compiler, but full production deployment would require substantial engineering infrastructure. The framework is deliberately agnostic about the compiler implementation (LLM agent, Bayesian optimizer, or human committee).

The demonstration is fully synthetic with a completely disclosed data-generating process (N=300 stocks, T=208 weeks, seed=7, all parameters published). The code is approximately 120 lines of NumPy/pandas for the demonstration and ~800 lines of Python for the reference compiler. All 32 assemblies, ten-seed robustness study, bootstrap audits, and threshold atlas are enumerated. However, the production case study and full protocol instantiation (deflated SR, PBO) are marked as roadmap items. The SpecNet architecture is a blueprint without performance claims. Code availability is stated as intended for release upon publication.