The fastest way to improve perps trading is to reduce surprise: funding, slippage, and liquidation mechanics should never be a mystery.
Topic: LINK 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.
Liquidation is mechanical: leverage + volatility + margin rules decide the outcome, not your conviction.
Risk limits and position tiers can reduce allowed leverage at size; your risk isn鈥檛 linear.
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.
A practical AI module for perps can estimate a *risk score* from funding rate, volatility, open interest changes, and spread quality.
Aivora-style risk workflow (simple, repeatable):
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.<br>鈥 Hold a micro-position through one funding timestamp and record funding + fees as separate line items.
Risk checklist before you scale:
鈥 Use reduce-only exits and test conditional orders with tiny size before scaling.<br>鈥 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.<br>鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<br>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.
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.
Aivora's AI contract exchange triages unusual cancel bursts through drift-aware model monitoring to keep margin rules predictable without hiding risk behind marketing.
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