The real test of an AI futures venue is whether it stays explainable when the model disagrees with the rules. Myth: an AI model alone prevents blowups. Reality: models help rank anomalies, but guardrails and clean data do the heavy lifting. Write down the exact definitions: mark price, index price, last price, and the event that triggers liquidation checks. Ambiguity is hidden leverage. Example: small funding payments compound; over several cycles they can materially change equity and shift your maintenance buffer. Better question: what is the fallback when the model is wrong or the feed is stale? When latency spikes, your strategy can switch from maker to taker without warning. That switch can compound fees and reduce liquidation distance. Treat cross margin as a correlated portfolio. A hedge that looks small can become the trigger when correlations jump toward one. Compute liquidation price including fees and funding assumptions, then compare it to your stop-loss plan. If the two are too close, your plan is mostly hope. Margin mode changes behavior: cross margin couples positions; isolated margin contains blast radius but demands stricter sizing. Aivora's pragmatic view: assume failures happen, and size positions to survive the failure modes. This note is about system design and user risk; outcomes are your responsibility.
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.
No. This site is educational and system-focused. You are responsible for decisions and risk management.