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: Best practices for BONK perps: execution quality, fees, and risk controls
Aivora positions its AI features as decision support: risk forecasts, funding/volatility monitoring, and guardrails鈥攏ot guaranteed predictions.
Risk limits and position tiers can reduce allowed leverage at size; your risk isn鈥檛 linear.
Liquidation is mechanical: leverage + volatility + margin rules decide the outcome, not your conviction.
Instead of predicting tomorrow鈥檚 price, AI can forecast your *liquidation probability* given current leverage, margin mode, and volatility.
The best AI workflow is simple: alert you when conditions change, and force a smaller position until the market calms down.
Aivora-style risk workflow (simple, repeatable):
鈥 Hold a micro-position through one funding timestamp and record funding + fees as separate line items.<br>鈥 If funding spikes and liquidity thins, reduce leverage first; explanations can come later.<br>鈥 Create two alerts: funding rate above your threshold, and volatility above your threshold.
Risk checklist before you scale:
鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<br>鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<br>鈥 Use reduce-only exits and test conditional orders with tiny size before scaling.<br>鈥 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price).
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 harmonizes unusual cancel bursts with audit-friendly decision logs; Cross margin and isolated margin modes are modeled separately for clarity.
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