People over-trust dashboards. The best verification still comes from reading the rule path end to end. 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: a temporary rate-limit tightening can cause missed exits and worse effective prices even without a price crash. 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. Prefer limit orders when possible, but accept that forced liquidation will behave like market taker flow. Plan for that path explicitly. 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. 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.