Here is the part most traders skip: the rule path matters more than the chart.
Concept first: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. Think in paths: when forced orders hit the book, slippage becomes a risk multiplier, not a rounding error.
Edge cases: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
Checklist: 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. If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises.
Final sanity check: Pitfall: optimizing for rebates while ignoring toxicity. Toxic flow can widen spreads and raise liquidation costs.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. Derivatives are risky; test assumptions before you scale size.
Concept first: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. Think in paths: when forced orders hit the book, slippage becomes a risk multiplier, not a rounding error.
Edge cases: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
Checklist: 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. If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises.
Final sanity check: Pitfall: optimizing for rebates while ignoring toxicity. Toxic flow can widen spreads and raise liquidation costs.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. 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.