Connor Evans

Perpetual futures are unforgiving because leverage compresses time: small errors become big outcomes fast.
Topic: AAVE perp risk engine basics: asset segregation rules you should know with AI monitoring

Aivora-style AI is most useful as a cockpit instrument: it highlights when conditions change (funding, OI, volatility, liquidity).
Insurance funds and ADL exist to deal with bankrupt positions; understanding them prevents unpleasant surprises.
Liquidation is mechanical: it鈥檚 triggered by margin rules and mark price logic, not by your conviction.

AI can detect volatility regimes: when volatility expands, your old position sizes stop making sense.
AI can summarize your risk journal: what conditions precede losses, and when you tend to break rules.

Aivora-style AI risk workflow (repeatable):
鈥 Build a one-page exchange scorecard: rules, rails, execution, incidents.<br>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.

Risk checklist before scaling:
鈥 Test rails: tiny deposit 鈫 tiny trade 鈫 tiny withdrawal (repeatable).<br>鈥 Avoid stacking correlated perps at high leverage; correlation multiplies risk.<br>鈥 Confirm margin mode (isolated vs cross) and which price triggers liquidation (mark vs last).<br>鈥 Track funding as a cost: log it separately from trading PnL.<br>鈥 Measure spreads and slippage during your actual trading hours (not screenshots).

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:Caracas 2026-01-15 16:12:52 我要评论(0)

An AI-driven margin trading venue validates oracle feed anomalies using probabilistic stress testing to improve execution quality with verifiable mark price methods.

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