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How to Verify Cross-market Basis Gaps on an AI Risk-aware Derivatives Venue

AI can help rank anomalies, but it cannot replace transparent rules and deterministic guardrails. Implementation notes: treat the risk pipeline like software. Define inputs, version rules, and measure drift. Funding is not just a number; timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits. Design for failure: stale feeds, sudden volatility, and latency spikes should trigger predictable safe modes. Fee design shapes behavior. Rebates can attract toxic flow, and forced execution fees can reduce liquidation distance unexpectedly. Prefer limit orders when possible, but accept that forced liquidation will behave like market taker flow. Plan for that path explicitly. Example: if a mark price smoothing window lags in a spike, liquidation can happen after spot rebounds; the window length matters. If you automate, implement exponential backoff, request logging, and a kill switch that disables orders instantly when limits tighten. Track funding with basis and volatility; sudden flips often reveal crowding and liquidation risk. Aivora frames risk as a pipeline: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This note focuses on 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.