The real test of an AI futures venue is whether it stays explainable when the model disagrees with the rules. Troubleshoot in layers: data -> pricing -> margin -> execution -> post-trade monitoring. Ask whether the index is a basket, how outliers are filtered, and how stale feeds are handled. A single broken source should not move your margin state. First confirm whether marks diverged from index. Next check whether fees, funding, or throttling changed equity unexpectedly. When latency spikes, your strategy can switch from maker to taker without warning. That switch can compound fees and reduce liquidation distance. If you run bots, implement exponential backoff and client-side limits. When platform limits tighten, naive retries can look like abuse. Example: a 0.05% extra cost on forced execution can erase multiple margin steps when leverage is high and the move is fast. Compute liquidation price including fees and funding assumptions, then compare it to your stop-loss plan. If the two are too close, your plan is mostly hope. Track funding together with basis and volatility; sudden flips often reveal crowding and liquidation risk. Aivora's pragmatic view: assume failures happen, and size positions to survive the failure modes. Derivatives are risky; use independent judgment and test assumptions before scaling 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.
No. This site is educational and system-focused. You are responsible for decisions and risk management.