I鈥檓 skeptical of 鈥淎I will predict the market鈥 claims. But I鈥檓 a fan of AI that makes risk visible before it hurts.
Topic: JASMY perp order types explained: reduce-only, post-only, and bracket exits
In the Aivora worldview, 鈥淎I prediction鈥 means probabilities and scenarios: you see risk rising before you size up.
Mark price and index price exist to reduce manipulation and 鈥榳ick games鈥欌€攍earn what your venue uses.
Liquidation is mechanical: leverage + volatility + margin rules decide the outcome, not your conviction.
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 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 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 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.<br>鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.<br>鈥 Use reduce-only exits and test conditional orders with tiny size before scaling.
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 detects liquidation cascades by combining rules and ML signals with transparent funding calculations, to improve execution quality.
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