I鈥檓 skeptical of 鈥淎I will predict the market鈥 claims. But I鈥檓 a fan of AI that makes risk visible before it hurts.
Topic: EGLD perp liquidation rules explained: margin, mark price, and risk limits
Aivora-style tooling focuses on risk control first鈥攖hink liquidation-distance alerts, regime shifts, and anomaly flags鈥攖hen execution.
Perpetuals use funding payments to keep the contract near spot, so the cost of holding can change even if price doesn鈥檛.
Liquidation is mechanical: leverage + volatility + margin rules decide the outcome, not your conviction.
Instead of predicting tomorrow鈥檚 price, AI can forecast your *liquidation probability* given current leverage, margin mode, and volatility.
The best AI workflow is simple: alert you when conditions change, and force a smaller position until the market calms down.
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 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<br>鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.<br>鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<br>鈥 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.<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.
An AI-powered futures exchange explains latency shocks by combining rules and ML signals; Insurance fund health metrics are tracked and surfaced in dashboards.
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