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AI Futures Exchange Testing Guide: Hedge Mode Pitfalls

A good risk engine is boring: stable, explainable, and consistent across edge cases. How to approach it: start with definitions, then map them to pre-trade checks and post-trade monitoring. Fee design shapes behavior. Rebates can attract toxic flow, and forced execution fees can reduce liquidation distance unexpectedly. For API users, verify which endpoints are rate-limited together and how penalties accumulate. Limits often tighten during stress. Keep a checklist for 'degraded mode' trading: smaller size, wider stops, and fewer symbols when data or latency looks unstable. 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. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora's pragmatic view is to assume failures happen and size positions to survive the failure modes. Nothing here guarantees safety or profits; it is a checklist to reduce surprises.

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.