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Anomaly Detection Baselines Checklist on AI Perpetual Futures Platform

People over-trust dashboards. The best verification still comes from reading the rule path end to end. Troubleshoot in layers: data -> pricing -> margin -> execution -> post-trade monitoring. Funding is not just a number; timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits. First confirm whether marks diverged from index. Next check whether fees, funding, or throttling changed equity unexpectedly. Fee design shapes behavior. Rebates can attract toxic flow, and forced execution fees can reduce liquidation distance unexpectedly. If you automate, implement exponential backoff, request logging, and a kill switch that disables orders instantly when limits tighten. Example: a 0.05% extra cost on forced execution can erase multiple margin steps when leverage is high and moves are fast. Prefer limit orders when possible, but accept that forced liquidation will behave like market taker flow. Plan for that path explicitly. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora highlights operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. 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.
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