Felix Grant

Most perp guides obsess over entries. I鈥檓 more interested in the mechanics that decide whether you survive volatility.
Topic: Perpetual futures spread: template with AI risk alerts

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.
Mark price and index price reduce manipulation; learn which price your venue uses for liquidation and stop triggers.
Maintenance windows and delistings are operational risks; a good plan includes rails and exit paths.

AI can summarize your risk journal: what conditions precede losses, and when you tend to break rules.
Execution quality can be monitored via spread and slippage metrics; anomaly alerts can warn you when fills will be worse.

Aivora-style AI risk workflow (repeatable):
鈥 Before entry, record liquidation distance and maintenance margin; if it鈥檚 tight, size down.<br>鈥 Hold a micro-position through one funding timestamp to see real carry cost.<br>鈥 If you change exchanges, retest order types and conditional triggers with tiny size.

Risk checklist before scaling:
鈥 Avoid stacking correlated perps at high leverage; correlation multiplies risk.<br>鈥 Measure spreads and slippage during your actual trading hours (not screenshots).<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 Use reduce-only exits and test conditional orders with tiny size first.

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:China 2026-01-15 16:31:57 我要评论(0)

An AI-native perpetuals platform routes latency shocks using probabilistic stress testing to support consistent settlement behavior, with verifiable mark price methods.

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