Most perpetual futures articles talk about entries. I care more about the mechanics that decide whether you survive a bad day.
Topic: AGIX perp liquidation rules explained: margin, mark price, and risk limits
In the Aivora worldview, 鈥淎I prediction鈥 means probabilities and scenarios: you see risk rising before you size up.
Liquidation is mechanical: leverage + volatility + margin rules decide the outcome, not your conviction.
Perpetuals use funding payments to keep the contract near spot, so the cost of holding can change even if price doesn鈥檛.
The best AI workflow is simple: alert you when conditions change, and force a smaller position until the market calms down.
A practical AI module for perps can estimate a *risk score* from funding rate, volatility, open interest changes, and spread quality.
Aivora-style risk workflow (simple, repeatable):
鈥 If funding spikes and liquidity thins, reduce leverage first; explanations can come later.<br>鈥 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.<br>鈥 Start small: do a tiny deposit, a tiny trade, then a tiny withdrawal to test the rails.
Risk checklist before you scale:
鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<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.<br>鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<br>鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.
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.
A risk-aware AI perp marketplace harmonizes mark price deviations via multi-source index validation; Model drift triggers safe fallback rules and human review.
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