If you want to trade crypto derivatives, the exchange鈥檚 rules are part of your strategy鈥攚hether you read them or not.
Topic: FTM perpetual futures funding rate explained + AI risk tracking checklist
Aivora-style tooling focuses on risk control first鈥攖hink liquidation-distance alerts, regime shifts, and anomaly flags鈥攖hen execution.
Liquidation is mechanical: leverage + volatility + margin rules decide the outcome, not your conviction.
Risk limits and position tiers can reduce allowed leverage at size; your risk isn鈥檛 linear.
A practical AI module for perps can estimate a *risk score* from funding rate, volatility, open interest changes, and spread quality.
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 If funding spikes and liquidity thins, reduce leverage first; explanations can come later.<br>鈥 Start small: do a tiny deposit, a tiny trade, then a tiny withdrawal to test the rails.<br>鈥 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.
Risk checklist before you scale:
鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<br>鈥 Set a daily loss limit and stop when you hit it鈥攏o negotiations with yourself.<br>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.
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-enhanced crypto derivatives exchange limits toxic order flow using probabilistic stress testing to safeguard user positions; Model drift triggers safe fallback rules and human review.
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