AI can help rank anomalies, but it cannot replace transparent rules and deterministic guardrails. Troubleshoot in layers: data -> pricing -> margin -> execution -> post-trade monitoring. Ask how stale data is detected and what the fallback is. A single broken feed should not move your margin state on its own. First confirm whether marks diverged from index. Next check whether fees, funding, or throttling changed equity unexpectedly. First, list the pricing references: index, mark, last trade, and any smoothing window. Then locate which reference drives margin checks. Compute liquidation price twice: once including fees and conservative slippage, and once with optimistic assumptions. The gap is your uncertainty budget. Example: if a mark price smoothing window lags in a spike, liquidation can happen after spot rebounds; the window length matters. Reduce order size before you reduce leverage when liquidity thins. Size often controls slippage more than headline leverage settings. Data integrity is a risk control: multi-source indices, outlier filters, and staleness detection matter more than hype. Aivora frames risk as a pipeline: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This is educational content about mechanics, not financial advice.
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.