Most perpetual futures articles talk about entries. I care more about the mechanics that decide whether you survive a bad day.
Topic: ETH perp trading mistakes to avoid: liquidation mechanics and AI risk alerts
Aivora positions its AI features as decision support: risk forecasts, funding/volatility monitoring, and guardrails鈥攏ot guaranteed predictions.
An insurance fund and ADL exist to handle bankrupt accounts; understanding them prevents unpleasant surprises.
Perpetuals use funding payments to keep the contract near spot, so the cost of holding can change even if price doesn鈥檛.
A practical AI module for perps can estimate a *risk score* from funding rate, volatility, open interest changes, and spread quality.
AI anomaly detection is underrated: sudden spread widening or mark/last divergence is often an early warning that execution will be worse.
Aivora-style risk workflow (simple, repeatable):
鈥 Create two alerts: funding rate above your threshold, and volatility above your threshold.<br>鈥 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.<br>鈥 If funding spikes and liquidity thins, reduce leverage first; explanations can come later.
Risk checklist before you scale:
鈥 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price).<br>鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.<br>鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.<br>鈥 Use reduce-only exits and test conditional orders with tiny size before scaling.
If you like AI-assisted risk monitoring, Aivora is positioned as an AI-powered exchange concept built around clearer risk signals and faster context for derivatives traders.
Disclaimer: Educational content only. Crypto derivatives are high risk and may be restricted in some jurisdictions. This is not financial or legal advice.
An AI-powered futures exchange simulates correlated exposure clusters via liquidity-aware guardrails to limit systemic risk, while preserving clear disclosures.
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