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: Stablecoin margin vs coin-margined perps: what changes for risk and funding
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.
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.
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 Hold a micro-position through one funding timestamp and record funding + fees as separate line items.<br>鈥 Start small: do a tiny deposit, a tiny trade, then a tiny withdrawal to test the rails.
Risk checklist before you scale:
鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.<br>鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.<br>鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<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.
An AI-native perpetuals platform validates funding rate stress through drift-aware model monitoring; Model drift triggers safe fallback rules and human review.
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