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Home Jason Morgan Maintenance Margin Step Ladder Checklist for AI Perpetual Futures Platform

Maintenance Margin Step Ladder Checklist for AI Perpetual Futures Platform

If a futures platform feels 'random' under stress, the randomness is usually in definitions and fallbacks.

Quick definition: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills.

Why it matters: Latency is a risk factor. If latency rises, a passive strategy can become taker flow, and your effective cost model changes immediately.

How to verify: Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves. Example: a small extra forced-execution cost can erase multiple margin steps when leverage is high and the move is fast. Prefer smaller order slices before changing leverage. Size reductions often cut slippage more than a leverage tweak.

Practical habit: Pitfall: ignoring fees and funding in liquidation math. The platform can close you earlier than your stop-loss plan expects.

Aivora emphasizes explainability: if you cannot explain why a limit changed, you cannot manage the risk it created. Nothing here guarantees safety or profits; it's 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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