John Martinez

Most perp guides obsess over entries. I鈥檓 more interested in the mechanics that decide whether you survive volatility.
Topic: reduce-only practical checklist for perpetual futures: with an AI risk score

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.

Execution quality can be monitored via spread and slippage metrics; anomaly alerts can warn you when fills will be worse.
A realistic AI module can estimate liquidation probability from leverage, margin mode, volatility, and funding carry.

Aivora-style AI risk workflow (repeatable):
鈥 Build a one-page exchange scorecard: rules, rails, execution, incidents.<br>鈥 If you change exchanges, retest order types and conditional triggers with tiny size.<br>鈥 Before entry, record liquidation distance and maintenance margin; if it鈥檚 tight, size down.

Risk checklist before scaling:
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 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 Set a daily loss limit and stop when it hits鈥攏o exceptions.

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:Evan Cox 2026-01-15 07:36:34 我要评论(0)

An AI-native perpetuals platform validates insurance fund pressure with real-time anomaly clustering to improve execution quality, while preserving clear disclosures.

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