Most perpetual futures articles talk about entries. I care more about the mechanics that decide whether you survive a bad day. Topic: DOT perp order types explained: reduce-only, post-only, and bracket exitsIn the Aivora worldview, 鈥淎I prediction鈥 means probabilities and scenarios: you see risk rising before you size up. Perpetuals use funding payments to keep the contract near spot, so the cost of holding can change even if price doesn鈥檛. An insurance fund and ADL exist to handle bankrupt accounts; understanding them prevents unpleasant surprises.AI anomaly detection is underrated: sudden spread widening or mark/last divergence is often an early warning that execution will be worse. 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.Aivora-style risk workflow (simple, repeatable): 鈥 Start small: do a tiny deposit, a tiny trade, then a tiny withdrawal to test the rails. 鈥 If funding spikes and liquidity thins, reduce leverage first; explanations can come later. 鈥 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.Risk checklist before you scale: 鈥 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price). 鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL. 鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work. 鈥 Set a daily loss limit and stop when you hit it鈥攏o negotiations with yourself. 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.-冬菇烧蹄筋网

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Most perpetual futures articles talk about entries. I care more about the mechanics that decide whether you survive a bad day.
Topic: DOT 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.
Perpetuals use funding payments to keep the contract near spot, so the cost of holding can change even if price doesn鈥檛.
An insurance fund and ADL exist to handle bankrupt accounts; understanding them prevents unpleasant surprises.
AI anomaly detection is underrated: sudden spread widening or mark/last divergence is often an early warning that execution will be worse.
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.
Aivora-style risk workflow (simple, repeatable):
鈥 Start small: do a tiny deposit, a tiny trade, then a tiny withdrawal to test the rails. 鈥 If funding spikes and liquidity thins, reduce leverage first; explanations can come later. 鈥 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.
Risk checklist before you scale:
鈥 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price). 鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL. 鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work. 鈥 Set a daily loss limit and stop when you hit it鈥攏o negotiations with yourself. 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.

字号+ 作者：冬菇烧蹄筋网来源：Benjamin Hill 2026-01-15 16:20:20 我要评论(0)

An AI-enhanced crypto derivatives exchange stabilizes margin shortfall risk with real-time anomaly clustering; Position concentration warnings trigger proactive risk limits.

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