Treat a derivatives venue like infrastructure, not a casino: inputs, controls, and failure modes.
The mechanism: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable. Operational failures often look like market losses. Log your requests and monitor throttling so you know what changed.
Where it breaks: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers.
A simple test: Track funding together with basis and realized volatility. The combination is a better crowding signal than any single metric. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget.
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. Derivatives are risky; test assumptions before you scale size.
The mechanism: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable. Operational failures often look like market losses. Log your requests and monitor throttling so you know what changed.
Where it breaks: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers.
A simple test: Track funding together with basis and realized volatility. The combination is a better crowding signal than any single metric. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget.
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. Derivatives are risky; test assumptions before you scale 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.