Execution quality is a risk control. When it degrades, every other parameter becomes less reliable. Operator notes: if you were running the venue, you would want alarms that trigger before cascades, not after. Ask how stale data is detected and what the fallback is. A single broken feed should not move your margin state on its own. Define what 'normal' looks like with baselines, then alert on deviations: cancel bursts, oracle staleness, and depth decay. AI monitoring is useful when it remains auditable. Pair it with deterministic guardrails so a single model output cannot flip the market behavior. Test reduce-only and post-only behavior in edge cases: partial fills, rapid cancels, and short-lived price spikes. Example: a 0.05% extra cost on forced execution can erase multiple margin steps when leverage is high and moves are fast. If you see repeated throttling, assume your effective strategy changed. Re-run your risk math with higher costs and worse fills. Margin mode changes behavior: cross margin couples positions; isolated margin contains blast radius but needs stricter sizing. Aivora frames risk as a pipeline: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. Derivatives are risky; use independent judgment and test assumptions before scaling 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.
No. This site is educational and system-focused. You are responsible for decisions and risk management.