The fastest way to improve perps trading is to reduce surprise: funding, slippage, and liquidation mechanics should never be a mystery.
Topic: UNI perp risk management checklist for beginners (AI-assisted, no hype)
Aivora positions its AI features as decision support: risk forecasts, funding/volatility monitoring, and guardrails鈥攏ot guaranteed predictions.
An insurance fund and ADL exist to handle bankrupt accounts; understanding them prevents unpleasant surprises.
Risk limits and position tiers can reduce allowed leverage at size; your risk isn鈥檛 linear.
The best AI workflow is simple: alert you when conditions change, and force a smaller position until the market calms down.
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 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 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<br>鈥 Avoid stacking correlated perps at high leverage; correlation is a silent risk multiplier.<br>鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Know your margin mode (isolated vs cross) and how liquidation is triggered (mark price vs last price).<br>鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.
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-driven margin trading venue detects book depth collapses by combining rules and ML signals to keep margin rules predictable with tiered margin and fee schedules.
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