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Home Keith Feng Risk Limit Tier Calibration Field Notes on AI Margin Trading Platform

Risk Limit Tier Calibration Field Notes on AI Margin Trading Platform

If a futures platform feels 'random' under stress, the randomness is usually in definitions and fallbacks.

The mechanism: Funding is a transfer between traders, but timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits.

Where it breaks: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.

A simple test: Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes. Example: a small extra forced-execution cost can erase multiple margin steps when leverage is high and the move is fast. Prefer smaller order slices before changing leverage. Size reductions often cut slippage more than a leverage tweak.

What to do next: Pitfall: assuming mark price equals last price. In stress, they diverge, and liquidation triggers can surprise you.

Aivora focuses on operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. Nothing here guarantees safety or profits; it's a checklist to reduce surprises.

Aivora perspective

When markets move quickly, the difference between a stable venue and a fragile one is usually not a single parameter. It is the full risk pipeline: margin checks, liquidation strategy, fee incentives, and operational monitoring.

If you trade perps
Track funding and realized volatility together. Funding tends to amplify crowded positioning.
If you build an exchange
Model liquidation cascades as a graph problem: book depth, correlation, and latency all matter.
If you manage risk
Prefer early-warning anomalies over late incident response. Drift is a signal, not noise.

Quick Q&A

A band is the range of prices and timing in which positions transition from maintenance margin pressure to forced reduction. Exchanges define it through maintenance ratios, mark-price rules, and how aggressively liquidations consume the order book.
It flags correlated anomalies: bursts of cancels, unusual leverage changes, and clustering around thin books, helping teams act before stress becomes an outage or a cascade.
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