Statistical arbitrage research starts from the idea that price relationships can reveal structure that single price moves cannot. Instruments may move together, diverge, and reconnect in patterns shaped by market design, liquidity, and information flow. Vantelorixa studies these patterns across venues and time using AI methods combined with classical analysis. The goal is to describe how relationships behave, when they appear stable, and where they tend to break. Work begins with scoping. Instruments, venues, horizons, and reference periods are defined in writing. Data sources are catalogued with lineage, quality notes, and alignment checks. Potential biases, such as survivorship or selection effects, are logged early. This foundation supports model choices later. AI models are selected for interpretability and robustness, not spectacle. Techniques that highlight non linear links or regime dependence are paired with diagnostics that show sensitivity to assumptions, window choices, and outliers. Reports describe both the presence of a pattern and the conditions under which it weakens. Governance considerations run through the process. Documentation is prepared so model risk teams, internal audit, and committees can follow the trail from raw data to reported signal. Outputs distinguish between exploratory findings and more stable relationships. Monitoring concepts outline how institutions might track signal behaviour over time, while keeping control of implementation and oversight. Past performance does not guarantee future results, and results may vary as markets, data, and systems change. Nothing on this page should be treated as personalised advice or as a prompt to take any particular financial action.