Markets do not need to crash for accounts to blow up; thin liquidity and poor definitions are enough. Myth: an AI model alone prevents blowups. Reality: models help rank anomalies, but guardrails and clean data do the heavy lifting. Ask how stale data is detected and what the fallback is. A single broken feed should not move your margin state on its own. Example: a 0.05% extra cost on forced execution can erase multiple margin steps when leverage is high and moves are fast. 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. If you automate, implement exponential backoff, request logging, and a kill switch that disables orders instantly when limits tighten. Track basis, funding, and realized volatility together. The combination reveals crowding more reliably than any single metric. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora emphasizes explainability: if you cannot explain why a limit changed, you cannot manage the risk it created. 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.