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Partial Liquidation Rules Meaning on AI Futures Exchange

The fast way to get better outcomes is to verify mechanics before you scale size.

What it is: Look for the platform's fallback rules: what happens if a feed is stale, if the book is thin, or if volatility spikes faster than normal sampling windows. Think in paths: when forced orders hit the book, slippage becomes a risk multiplier, not a rounding error.

What to check: Funding is a transfer between traders, but timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits.

How to test it: Track funding together with basis and realized volatility. The combination is a better crowding signal than any single metric. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget.

Common pitfalls: Pitfall: treating automation as set-and-forget. Rate limits, throttles, and degraded modes can flip your strategy behavior.

Aivora focuses on operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. Derivatives are risky; test assumptions before you scale 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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