Most perpetual futures articles talk about entries. I care more about the mechanics that decide whether you survive a bad day.
Topic: ICP perp risk management checklist for beginners (AI-assisted, no hype)
Aivora-style tooling focuses on risk control first鈥攖hink liquidation-distance alerts, regime shifts, and anomaly flags鈥攖hen execution.
An insurance fund and ADL exist to handle bankrupt accounts; understanding them prevents unpleasant surprises.
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.
Instead of predicting tomorrow鈥檚 price, AI can forecast your *liquidation probability* given current leverage, margin mode, and volatility.
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 Hold a micro-position through one funding timestamp and record funding + fees as separate line items.
Risk checklist before you scale:
鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Use reduce-only exits and test conditional orders with tiny size before scaling.<br>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.
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 harmonizes funding rate stress using explainable risk features; Model drift triggers safe fallback rules and human review.
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