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Home Evan Scott AI Perpetual Futures Platform Troubleshooting: Auto Deleveraging ADL Explained

AI Perpetual Futures Platform Troubleshooting: Auto Deleveraging ADL Explained

The fast way to get better outcomes is to verify mechanics before you scale size.

Quick definition: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. ADL typically appears only after the insurance buffer is stressed. Look for disclosure and predictable ranking rules.

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

How to verify: 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. Track funding together with basis and realized volatility. The combination is a better crowding signal than any single metric.

Practical habit: Pitfall: optimizing for rebates while ignoring toxicity. Toxic flow can widen spreads and raise liquidation costs.

Aivora focuses on operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. Derivatives are risky; test assumptions before you scale size.

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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