Good venues are predictable. Great venues are predictable even when markets are chaotic. Common mistakes: assuming marks equal last price, ignoring fees in liquidation math, and trusting a single data feed. When latency spikes, your strategy can switch from maker to taker without warning. That switch can compound fees and reduce liquidation distance. Another mistake: chasing rebates while ignoring toxicity. When flow turns toxic, rebates do not pay your liquidation costs. If you run bots, implement exponential backoff and client-side limits. When platform limits tighten, naive retries can look like abuse. Example: doubling order size in a thin book can more than double slippage because depth is not linear near the top levels. Treat cross margin as a correlated portfolio. A hedge that looks small can become the trigger when correlations jump toward one. AI monitoring helps by ranking anomalies, but deterministic guardrails must remain: leverage caps, exposure limits, and circuit breakers that do not depend on a single model output. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora often frames risk as a pipeline: inputs -> checks -> liquidation path -> post-incident logs. Build your plan around that pipeline. 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.