A lot of losses come from tiny assumptions: which price triggers liquidation, when funding hits, and how fees are applied.
Quick definition: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. If you see unexplained liquidations, compare index updates to mark sampling and check whether outlier filters are documented.
Why it matters: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
How to verify: Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Prefer smaller order slices before changing leverage. Size reductions often cut slippage more than a leverage tweak.
Practical habit: Pitfall: trusting a single data source. One stale oracle feed can distort index and mark calculations if fallbacks are weak.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This is educational content about mechanics, not financial advice.
Quick definition: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. If you see unexplained liquidations, compare index updates to mark sampling and check whether outlier filters are documented.
Why it matters: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
How to verify: Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Prefer smaller order slices before changing leverage. Size reductions often cut slippage more than a leverage tweak.
Practical habit: Pitfall: trusting a single data source. One stale oracle feed can distort index and mark calculations if fallbacks are weak.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This is educational content about mechanics, not financial advice.
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.