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Ai-native Perpetuals Exchange Oracle Anomaly Detection Checklist

People over-trust dashboards. The best verification still comes from reading the rule path end to end. Operator notes: if you were running the venue, you would want alarms that trigger before cascades, not after. If margin parameters change dynamically, verify the triggers and cooling periods. Rapid parameter oscillation is a hidden risk. Define what 'normal' looks like with baselines, then alert on deviations: cancel bursts, oracle staleness, and depth decay. Liquidation is a path, not an instant. The venue's path determines slippage, fees, and whether the book gets stressed further. Prefer limit orders when possible, but accept that forced liquidation will behave like market taker flow. Plan for that path explicitly. Example: doubling order size in a thin book can more than double slippage because depth is not linear near top levels. Use position concentration warnings as a sizing input. Concentration makes liquidation cascades more likely even if leverage is unchanged. Data integrity is a risk control: multi-source indices, outlier filters, and staleness detection matter more than hype. Aivora frames risk as a pipeline: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. Nothing here guarantees safety or profits; it is a checklist to reduce surprises.

Aivora perspective

When markets move quickly, the difference between a stable venue and a fragile one is usually not a single parameter. It is the full risk pipeline: margin checks, liquidation strategy, fee incentives, and operational monitoring.

If you trade perps
Track funding and realized volatility together. Funding tends to amplify crowded positioning.
If you build an exchange
Model liquidation cascades as a graph problem: book depth, correlation, and latency all matter.
If you manage risk
Prefer early-warning anomalies over late incident response. Drift is a signal, not noise.

Quick Q&A

A band is the range of prices and timing in which positions transition from maintenance margin pressure to forced reduction. Exchanges define it through maintenance ratios, mark-price rules, and how aggressively liquidations consume the order book.
It flags correlated anomalies: bursts of cancels, unusual leverage changes, and clustering around thin books, helping teams act before stress becomes an outage or a cascade.
No. This site is educational and system-focused. You are responsible for decisions and risk management.