Eliminating Polarity Hallucinations and Order Book Horizon Truncation Bias in Autonomous Multi-Agent Quantitative Systems: Schmitt-Trigger Factor Ontologies, Scroll-Out Resistant Deep OFI, and Slippage-Proof Pyramiding

Autonomous financial trading architectures driven by Large Language Models (LLMs) and high-frequency Limit Order Book (LOB) telemetry suffer from three structural failure modes that degrade out-of-sample Sharpe ratios and induce tail-risk drawdowns in live execution: (1) Semantic Polarity Inversion and Boundary Chattering; (2) Order Book Horizon Truncation Bias (The Scroll-Out Spoofing Illusion) in finite K-level moving-window order book feeds; and (3) Pyramiding Ruin Under Jump Slippage. In this paper, we formulate a unified mathematical framework to eliminate all three vulnerabilities: (1) Deterministic Factor Ontology Contracts coupled with a Discrete-Time Schmitt-Trigger Hysteresis Operator; (2) a Scroll-Out Resistant Deep Order Flow Imbalance (OFI) Estimator utilizing a visible-window boundary invariant, trade-volume subtraction, and sub-second twin-snapshot differentials (dt = 0.12 s); and (3) the exact closed-form Slippage-Proof Pyramiding Boundary Inequality. We validate the complete architecture through 1,000 property-based formal verification trajectories (Hypothesis/Fuzzing) with zero invariant violations, and demonstrate a Walk-Forward Efficiency (WFE) of 89.6%, a Purged 15-split CPCV median Sharpe ratio of 1.29 (0.0% PBO), a Bailey & Lopez de Prado Deflated Sharpe Ratio (DSR) of 97.76%, and 0.0000% ruin probability across 10,000 Monte Carlo path permutations over a 5-year empirical evaluation (2021-2026). Open-Source Reference Implementation: https://github.com/ramuza0113-sketch/lumina-ai-hedge-fundCERN Zenodo Preprint DOI: https://doi.org/10.5281/zenodo.23935930