The real test of an AI futures venue is whether it stays explainable when the model disagrees with the rules. Quick audit method: list inputs, controls, outputs, and single points of failure. Write down the exact definitions: mark price, index price, last price, and the event that triggers liquidation checks. Ambiguity is hidden leverage. 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. Ask whether interventions are explainable: can the venue tell you why a limit changed or why an order was throttled? Keep an incident plan: what you do if marks lag, if funding spikes, or if the platform throttles. Decisions made late are usually expensive. Example: doubling order size in a thin book can more than double slippage because depth is not linear near the top levels. Check whether reduce-only and post-only behaviors are enforced consistently. Edge cases often appear during partial fills and rapid cancels. 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. 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.