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Oracle Fallback Design Framework for Ai-native Perpetuals Exchange

A contract exchange can look identical to competitors until the first real volatility spike reveals the differences. Checklist before scaling size: 1) Verify mark/index sources. 2) Understand margin steps and maintenance rules. 3) Test liquidation behavior with small size. Latency risk is real. When latency rises, a maker strategy can become taker flow and your costs jump right when you need stability. 4) Confirm fee tiers and forced execution costs. 5) Review risk limits, circuit breakers, and incident transparency. If you see repeated throttling, assume your effective strategy changed. Re-run your risk math with higher costs and worse fills. Example: a temporary rate-limit tightening can cause missed exits and worse effective prices even without a price crash. Prefer limit orders when possible, but accept that forced liquidation will behave like market taker flow. Plan for that path explicitly. Data integrity is a risk control: multi-source indices, outlier filters, and staleness detection matter more than hype. Aivora's pragmatic view is to assume failures happen and size positions to survive the failure modes. 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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