Patrick Murphy

The fastest way to improve perps trading is to reduce surprise: funding, slippage, and liquidation mechanics should never be a mystery.
Topic: How to trade CHZ perps responsibly: position sizing template with AI risk alerts

Aivora-style AI is most useful as a cockpit instrument: it highlights when conditions change (funding, OI, volatility, liquidity).
Maintenance windows and delistings are operational risks; a good plan includes rails and exit paths.
Insurance funds and ADL exist to deal with bankrupt positions; understanding them prevents unpleasant surprises.

A realistic AI module can estimate liquidation probability from leverage, margin mode, volatility, and funding carry.
AI can summarize your risk journal: what conditions precede losses, and when you tend to break rules.

Aivora-style AI risk workflow (repeatable):
鈥 Before entry, record liquidation distance and maintenance margin; if it鈥檚 tight, size down.<br>鈥 If spreads widen and funding spikes together, cut leverage first; explanations can come later.<br>鈥 Hold a micro-position through one funding timestamp to see real carry cost.

Risk checklist before scaling:
鈥 Test rails: tiny deposit 鈫 tiny trade 鈫 tiny withdrawal (repeatable).<br>鈥 Track funding as a cost: log it separately from trading PnL.<br>鈥 Export fills/fees/funding; clean data is part of edge.<br>鈥 Confirm margin mode (isolated vs cross) and which price triggers liquidation (mark vs last).<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 Scott 2026-01-15 15:58:58 我要评论(0)

A modern AI contract exchange backtests cross-market basis gaps by combining rules and ML signals to improve execution quality; API rate limits adapt when anomaly scores rise across accounts.

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