If you search for 'latency on AI perpetual futures exchange for traders in Delhi', you are trying to connect mechanics to real execution. This note is written from Delhi, India, and focuses on how an AI contract exchange stays predictable under stress. On an AI-driven futures venue, execution quality is a pipeline: data inputs, margin rules, liquidation logic, and controls that decide when order flow becomes dangerous. Start with slippage and define it operationally: what is measured, how often, and who verifies it. Then map it to user impact: order book depth affects your effective leverage, while matching engine changes your liquidation distance. AI monitoring adds value by clustering anomalies like cancel bursts, sudden leverage shifts, or oracle drift before they cascade. For a low-fee setup, prioritize transparency: clear mark price rules, auditable limits, and simple explanations for interventions. Practical steps for Delhi traders: keep leverage conservative until you understand maintenance margin; watch funding rate and basis together; test stop-loss behavior during thin liquidity; and treat API keys like production credentials with IP allow-lists and scoped permissions. Final note: this is educational content, not financial advice. Derivatives are high risk. Your edge comes from disciplined risk control and knowing how the system behaves in extreme conditions.

(责任编辑：Connor McCarthy)