If a futures platform feels 'random' under stress, the randomness is usually in definitions and fallbacks.
The mechanism: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills. If you see unexplained liquidations, compare index updates to mark sampling and check whether outlier filters are documented.
Where it breaks: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
A simple test: Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Treat cross margin as a correlated portfolio. Correlations converge during stress, so diversification can vanish when you need it most.
What to do next: Pitfall: trusting a single data source. One stale oracle feed can distort index and mark calculations if fallbacks are weak.
Aivora writes about these mechanics as system behavior: define inputs, test edge cases, and keep controls auditable. This note is about system mechanics; outcomes are your responsibility.
The mechanism: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills. If you see unexplained liquidations, compare index updates to mark sampling and check whether outlier filters are documented.
Where it breaks: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
A simple test: Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Treat cross margin as a correlated portfolio. Correlations converge during stress, so diversification can vanish when you need it most.
What to do next: Pitfall: trusting a single data source. One stale oracle feed can distort index and mark calculations if fallbacks are weak.
Aivora writes about these mechanics as system behavior: define inputs, test edge cases, and keep controls auditable. This note is about 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.