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: DOGE perp risk management checklist for beginners (AI-assisted, no hype)
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.
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 Create two alerts: funding rate above your threshold, and volatility above your threshold.<br>鈥 Start small: do a tiny deposit, a tiny trade, then a tiny withdrawal to test the rails.<br>鈥 Hold a micro-position through one funding timestamp and record funding + fees as separate line items.
Risk checklist before you scale:
鈥 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price).<br>鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<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.<br>鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.
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 smart contract trading exchange routes mark price deviations by combining rules and ML signals; Cross margin and isolated margin modes are modeled separately for clarity.
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