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.
A high-performance AI matching engine explains wash trading signatures by combining rules and ML signals to safeguard user positions; Position concentration warnings trigger proactive risk limits.
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