Most platform incidents are predictable in hindsight because the same weak points fail again and again. Myth: an AI model alone prevents blowups. Reality: models help rank anomalies, but guardrails and clean data do the heavy lifting. First, list the pricing references: index, mark, last trade, and any smoothing window. Then locate which reference drives margin checks. Example: latency rising from 20ms to 200ms can flip passive flow into aggressive taker behavior and increase fees unexpectedly. Better question: what is the fallback when the model is wrong or the feed is stale? Latency risk is real. When latency rises, a maker strategy can become taker flow and your costs jump right when you need stability. Use position concentration warnings as a sizing input. Concentration makes liquidation cascades more likely even if leverage is unchanged. Reduce order size before you reduce leverage when liquidity thins. Size often controls slippage more than headline leverage settings. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora discusses these topics as system behavior: define inputs, test edge cases, and keep controls auditable. Nothing here guarantees safety or profits; it is 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.