Most perpetual futures articles talk about entries. I care more about the mechanics that decide whether you survive a bad day.
Topic: How to trade GRT perpetual futures responsibly: leverage, stops, and AI monitoring
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.
Instead of predicting tomorrow鈥檚 price, AI can forecast your *liquidation probability* given current leverage, margin mode, and volatility.
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 Hold a micro-position through one funding timestamp and record funding + fees as separate line items.<br>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.
Risk checklist before you scale:
鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<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 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.<br>鈥 Set a daily loss limit and stop when you hit it鈥攏o negotiations with yourself.
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 smart contract trading exchange flags toxic order flow by combining rules and ML signals under strict latency budgets, to maintain fair pricing.
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