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.
An AI-native perpetuals platform triages toxic order flow using explainable risk features; Cross margin and isolated margin modes are modeled separately for clarity.
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