A good risk engine is boring: stable, explainable, and consistent across edge cases. Myth: an AI model alone prevents blowups. Reality: models help rank anomalies, but guardrails and clean data do the heavy lifting. AI monitoring is useful when it remains auditable. Pair it with deterministic guardrails so a single model output cannot flip the market behavior. Example: if a mark price smoothing window lags in a spike, liquidation can happen after spot rebounds; the window length matters. Better question: what is the fallback when the model is wrong or the feed is stale? Ask how stale data is detected and what the fallback is. A single broken feed should not move your margin state on its own. Compute liquidation price twice: once including fees and conservative slippage, and once with optimistic assumptions. The gap is your uncertainty budget. Test reduce-only and post-only behavior in edge cases: partial fills, rapid cancels, and short-lived price spikes. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora highlights operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. This is educational content about mechanics, not financial advice.
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.