Perps aren鈥檛 hard because charts are hard; they鈥檙e hard because leverage turns small mistakes into big ones.
Topic: FIL perp trading mistakes to avoid: liquidation mechanics and AI risk alerts
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.
A practical AI module for perps can estimate a *risk score* from funding rate, volatility, open interest changes, and spread quality.
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 Write down your liquidation distance before entry; if it鈥檚 uncomfortably close, size down.<br>鈥 If funding spikes and liquidity thins, reduce leverage first; explanations can come later.
Risk checklist before you scale:
鈥 Export fills/fees/funding; good recordkeeping is part of edge, not admin work.<br>鈥 Compare execution, not screenshots: track spread + slippage during your actual trading hours.<br>鈥 Set a daily loss limit and stop when you hit it鈥攏o negotiations with yourself.<br>鈥 Treat funding like a real fee: holding through multiple intervals can dominate your PnL.<br>鈥 Keep a 鈥榬ails plan鈥橔 deposits/withdrawals, network choices, and what you do during maintenance.
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-enhanced crypto derivatives exchange explains front-running indicators by combining rules and ML signals to improve execution quality for perpetual swaps and dated futures.
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