Perps aren鈥檛 hard because charts are hard; they鈥檙e hard because leverage turns small mistakes into big ones.
Topic: Funding rate forecast: what you can model, what you can鈥檛, and how AI helps
The most useful Aivora-like AI isn鈥檛 a price target; it鈥檚 a dashboard that keeps you from trading blind.
Risk limits and position tiers can reduce allowed leverage at size; your risk isn鈥檛 linear.
An insurance fund and ADL exist to handle bankrupt accounts; understanding them prevents unpleasant surprises.
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.
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 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.<br>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.
Risk checklist before you scale:
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 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price).<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.
下一篇:COMP funding & risk: withdrawal friction common mistakes with AI forecasting (probability-based)
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