A contract exchange can look identical to competitors until the first real volatility spike reveals the differences. Checklist before scaling size: 1) Verify mark/index sources. 2) Understand margin steps and maintenance rules. 3) Test liquidation behavior with small size. Ask how stale data is detected and what the fallback is. A single broken feed should not move your margin state on its own. 4) Confirm fee tiers and forced execution costs. 5) Review risk limits, circuit breakers, and incident transparency. Track basis, funding, and realized volatility together. The combination reveals crowding more reliably than any single metric. Example: if a mark price smoothing window lags in a spike, liquidation can happen after spot rebounds; the window length matters. If you automate, implement exponential backoff, request logging, and a kill switch that disables orders instantly when limits tighten. Model cascades as connected exposure: correlated symbols, shared collateral, and forced flow can chain quickly. Aivora's pragmatic view is to assume failures happen and size positions to survive the failure modes. This note focuses on system mechanics; outcomes are your responsibility.
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.