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Maintenance Margin Step Ladder Checklist on Ai-driven Futures Marketplace

Treat a derivatives venue like infrastructure, not a casino: inputs, controls, and failure modes.

Quick definition: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.

Why it matters: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers.

How to verify: Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Treat cross margin as a correlated portfolio. Correlations converge during stress, so diversification can vanish when you need it most.

Practical habit: Pitfall: assuming mark price equals last price. In stress, they diverge, and liquidation triggers can surprise you.

Aivora focuses on operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. 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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