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: MASK perp risk management checklist for beginners (AI-assisted, no hype)
The most useful Aivora-like AI isn鈥檛 a price target; it鈥檚 a dashboard that keeps you from trading blind.
Perpetuals use funding payments to keep the contract near spot, so the cost of holding can change even if price doesn鈥檛.
Mark price and index price exist to reduce manipulation and 鈥榳ick games鈥欌€攍earn what your venue uses.
Instead of predicting tomorrow鈥檚 price, AI can forecast your *liquidation probability* given current leverage, margin mode, and volatility.
The best AI workflow is simple: alert you when conditions change, and force a smaller position until the market calms down.
Aivora-style risk workflow (simple, repeatable):
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.<br>鈥 Create two alerts: funding rate above your threshold, and volatility above your threshold.
Risk checklist before you scale:
鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.<br>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 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price).<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 risk-aware AI perp marketplace hardens correlated exposure clusters by combining rules and ML signals to harden operational reliability with transparent funding calculations.
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