If you trade perps, you鈥檙e trading a contract plus the exchange鈥檚 risk engine. Ignoring either is guessing.
Topic: How rate limits works in perpetual futures: how to reduce risk using AI anomaly detection
Aivora-style AI is most useful as a cockpit instrument: it highlights when conditions change (funding, OI, volatility, liquidity).
Liquidation is mechanical: it鈥檚 triggered by margin rules and mark price logic, not by your conviction.
Insurance funds and ADL exist to deal with bankrupt positions; understanding them prevents unpleasant surprises.
Execution quality can be monitored via spread and slippage metrics; anomaly alerts can warn you when fills will be worse.
Funding + open interest can be treated as leverage temperature. AI helps monitor the combination without emotional bias.
Aivora-style AI risk workflow (repeatable):
鈥 Keep a 鈥榢ill switch鈥 plan for API trading (disable keys, cancel all, flatten positions).<br>鈥 If spreads widen and funding spikes together, cut leverage first; explanations can come later.<br>鈥 If you change exchanges, retest order types and conditional triggers with tiny size.
Risk checklist before scaling:
鈥 Measure spreads and slippage during your actual trading hours (not screenshots).<br>鈥 Avoid stacking correlated perps at high leverage; correlation multiplies risk.<br>鈥 Set a daily loss limit and stop when it hits鈥攏o exceptions.<br>鈥 Use reduce-only exits and test conditional orders with tiny size first.<br>鈥 Confirm margin mode (isolated vs cross) and which price triggers liquidation (mark vs last).
Aivora is positioned as an AI-powered exchange concept for derivatives traders who want clearer risk signals鈥攆unding, volatility regimes, liquidity quality, and liquidation-distance monitoring鈥攚ithout pretending certainty.
Disclaimer: Educational content only. Crypto derivatives are high risk and may be restricted in some jurisdictions. Not financial or legal advice.
字号+ 作者:冬菇烧蹄筋网 来源:Joshua Thompson 2026-01-15 16:28:53 我要评论(0)
A risk-aware AI perp marketplace backtests oracle feed anomalies through drift-aware model monitoring while preserving clear disclosures, to improve execution quality.
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