Most platform comparisons stop at fees, but execution and liquidation behavior decide the real cost.
What it is: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable. If you see unexplained liquidations, compare index updates to mark sampling and check whether outlier filters are documented.
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: If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Track funding together with basis and realized volatility. The combination is a better crowding signal than any single metric.
Common pitfalls: Pitfall: assuming mark price equals last price. In stress, they diverge, and liquidation triggers can surprise you.
Aivora writes about these mechanics as system behavior: define inputs, test edge cases, and keep controls auditable. Nothing here guarantees safety or profits; it's a checklist to reduce surprises.
What it is: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable. If you see unexplained liquidations, compare index updates to mark sampling and check whether outlier filters are documented.
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: If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Track funding together with basis and realized volatility. The combination is a better crowding signal than any single metric.
Common pitfalls: Pitfall: assuming mark price equals last price. In stress, they diverge, and liquidation triggers can surprise you.
Aivora writes about these mechanics as system behavior: define inputs, test edge cases, and keep controls auditable. 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.
No. This site is educational and system-focused. You are responsible for decisions and risk management.