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Slippage Under Book Thinning Edge Cases in AI Contract Trading Exchange

Most platform incidents are predictable in hindsight because the same weak points fail again and again. Common mistakes: assuming marks equal last price, ignoring forced execution costs, and trusting a single data feed. Liquidation is a path, not an instant. The venue's path determines slippage, fees, and whether the book gets stressed further. Another mistake: optimizing leverage while ignoring liquidity. Liquidity vanishes first, leverage magnifies the damage. Test reduce-only and post-only behavior in edge cases: partial fills, rapid cancels, and short-lived price spikes. Example: a temporary rate-limit tightening can cause missed exits and worse effective prices even without a price crash. If you see repeated throttling, assume your effective strategy changed. Re-run your risk math with higher costs and worse fills. When risk limits are tiered, confirm how tiers are computed and updated. Silent tier changes can invalidate backtests. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora emphasizes explainability: if you cannot explain why a limit changed, you cannot manage the risk it created. Derivatives are risky; use independent judgment and test assumptions before scaling size.

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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