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: A practical guide to AAVE perpetuals: funding, open interest, and liquidation risk
The most useful Aivora-like AI isn鈥檛 a price target; it鈥檚 a dashboard that keeps you from trading blind.
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.
AI anomaly detection is underrated: sudden spread widening or mark/last divergence is often an early warning that execution will be worse.
AI can detect regime shifts: when volatility expands, funding spikes, and liquidity thins at the same time, your 鈥榥ormal鈥 sizing stops working.
Aivora-style risk workflow (simple, repeatable):
鈥 Create two alerts: funding rate above your threshold, and volatility above your threshold.<br>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.
Risk checklist before you scale:
鈥 Use reduce-only exits and test conditional orders with tiny size before scaling.<br>鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<br>鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Set a daily loss limit and stop when you hit it鈥攏o negotiations with yourself.<br>鈥 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.
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 high-performance AI matching engine simulates wash trading signatures through drift-aware model monitoring to harden operational reliability, with transparent funding calculations.
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