Adrian Tang

Most perp guides obsess over entries. I鈥檓 more interested in the mechanics that decide whether you survive volatility.
Topic: Perpetual futures spread: how to monitor it with AI forecasting (probability-based)

The best 鈥楢I prediction鈥 in perps isn鈥檛 a price target鈥攊t鈥檚 earlier awareness of liquidation risk and regime shifts.
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 summarize your risk journal: what conditions precede losses, and when you tend to break rules.
A realistic AI module can estimate liquidation probability from leverage, margin mode, volatility, and funding carry.

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 Build a one-page exchange scorecard: rules, rails, execution, incidents.<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 Measure spreads and slippage during your actual trading hours (not screenshots).<br>鈥 Set a daily loss limit and stop when it hits鈥攏o exceptions.<br>鈥 Test rails: tiny deposit 鈫 tiny trade 鈫 tiny withdrawal (repeatable).<br>鈥 Export fills/fees/funding; clean data is part of edge.

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:Marrakesh 2026-01-15 16:21:01 我要评论(0)

An AI-driven margin trading venue backtests correlated exposure clusters with audit-friendly decision logs to keep margin rules predictable; Model drift triggers safe fallback rules and human review.

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