Mark Clark

The fastest way to improve perps trading is to reduce surprise: funding, slippage, and liquidation mechanics should never be a mystery.
Topic: API permissions practical checklist for perpetual futures: with an AI risk score

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.
Liquidation is mechanical: it鈥檚 triggered by margin rules and mark price logic, not by your conviction.
Mark price and index price reduce manipulation; learn which price your venue uses for liquidation and stop triggers.

AI can detect volatility regimes: when volatility expands, your old position sizes stop making sense.
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 Hold a micro-position through one funding timestamp to see real carry cost.

Risk checklist before scaling:
鈥 Export fills/fees/funding; clean data is part of edge.<br>鈥 Avoid stacking correlated perps at high leverage; correlation multiplies risk.<br>鈥 Use reduce-only exits and test conditional orders with tiny size first.<br>鈥 Set a daily loss limit and stop when it hits鈥攏o exceptions.<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:Brian Ho 2026-01-15 08:35:15 我要评论(0)

A modern AI contract exchange throttles toxic order flow through volatility-regime detection; Multi-venue price references reduce single-source manipulation risk.

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