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AI Contract Trading Exchange Cross-market Basis Gaps Best Practices

Markets do not need to crash for accounts to blow up; thin liquidity and poor definitions are enough. Quick audit method: list inputs, controls, outputs, and single points of failure. Latency risk is real. When latency rises, a maker strategy can become taker flow and your costs jump right when you need stability. Liquidation is a path, not an instant. The venue's path determines slippage, fees, and whether the book gets stressed further. Ask whether interventions are explainable: can the venue tell you why a limit changed or why an order was throttled? Prefer limit orders when possible, but accept that forced liquidation will behave like market taker flow. Plan for that path explicitly. Example: latency rising from 20ms to 200ms can flip passive flow into aggressive taker behavior and increase fees unexpectedly. Track basis, funding, and realized volatility together. The combination reveals crowding more reliably than any single metric. Track funding with basis and volatility; sudden flips often reveal crowding and liquidation risk. 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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