Audit-Bundled Evidence from BTC Price and Sentiment Data

This work presents a controlled empirical demonstration that internal regulatory state alone can deterministically alter externally observable decision outcomes under identical inputs. Using real Bitcoin price, volume, and sentiment data, the study isolates participation gating as a first-class regulatory function, independent of market prediction, optimization, or trading performance. A neurosymbolic Regulatory Intelligence system (SpiralBrain v3.0) is used strictly as a deterministic experimental instrument. For a fixed, byte-identical market input sequence, different injected regulatory profiles produce cleanly separated participation postures (buy vs. hold), despite identical inference logic and signal processing. This establishes that externally visible decision behavior can reflect internal regulatory bias even when no internal metrics are inspected. The contribution is methodological rather than predictive. The study does not evaluate profitability, alpha, or generalization. Instead, it provides an existence proof that internal regulation leaves an auditable behavioral signature under uncertainty, demonstrating how refusal to act (“hold”) can function as an explicit, governed decision outcome rather than as indecision or system failure. This paper forms part of the Regulatory Intelligence Paradigm research corpus and serves as a domain-level validation showing how viability-first cognition manifests as controlled participation behavior in a high-uncertainty financial environment.