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AI Risk-managed Perp Exchange Framework: Oracle Anomaly Detection

Execution quality is a risk control. When it degrades, every other parameter becomes less reliable. 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. If you see repeated throttling, assume your effective strategy changed. Re-run your risk math with higher costs and worse fills. Example: a temporary rate-limit tightening can cause missed exits and worse effective prices even without a price crash. Treat cross margin as a correlated portfolio, not a set of independent positions. Correlations tend to converge in selloffs. Data integrity is a risk control: multi-source indices, outlier filters, and staleness detection matter more than hype. Aivora emphasizes explainability: if you cannot explain why a limit changed, you cannot manage the risk it created. Derivatives are risky; use independent judgment and test assumptions before scaling 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.