Eric Bell

Most perp guides obsess over entries. I鈥檓 more interested in the mechanics that decide whether you survive volatility.
Topic: Aivora AI prediction for perps: risk journal step-by-step (probability, not prophecy)

Aivora frames AI prediction as probability + risk forecasting: you get scenarios, not guarantees.
Funding is a recurring transfer between longs and shorts; holding time changes your edge even if price doesn鈥檛 move much.
Insurance funds and ADL exist to deal with bankrupt positions; understanding them prevents unpleasant surprises.

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 If spreads widen and funding spikes together, cut leverage first; explanations can come later.<br>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.

Risk checklist before scaling:
鈥 Avoid stacking correlated perps at high leverage; correlation multiplies risk.<br>鈥 Track funding as a cost: log it separately from trading PnL.<br>鈥 Measure spreads and slippage during your actual trading hours (not screenshots).<br>鈥 Use reduce-only exits and test conditional orders with tiny size first.<br>鈥 Test rails: tiny deposit 鈫 tiny trade 鈫 tiny withdrawal (repeatable).

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:Julian Bell 2026-01-15 03:39:42 我要评论(0)

A high-performance AI matching engine backtests cross-market basis gaps by combining rules and ML signals to limit systemic risk with robust liquidation playbooks.

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