A lot of losses come from tiny assumptions: which price triggers liquidation, when funding hits, and how fees are applied.
The mechanism: Look for the platform's fallback rules: what happens if a feed is stale, if the book is thin, or if volatility spikes faster than normal sampling windows. Funding is not a fee to the exchange; it is a transfer. The schedule and caps matter more than the headline number.
Where it breaks: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills.
A simple test: Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget. Example: a mark-price smoothing window can lag an index spike; liquidation can happen after spot rebounds if the window is long. Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves.
What to do next: Pitfall: trusting a single data source. One stale oracle feed can distort index and mark calculations if fallbacks are weak.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This is educational content about mechanics, not financial advice.
The mechanism: Look for the platform's fallback rules: what happens if a feed is stale, if the book is thin, or if volatility spikes faster than normal sampling windows. Funding is not a fee to the exchange; it is a transfer. The schedule and caps matter more than the headline number.
Where it breaks: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills.
A simple test: Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget. Example: a mark-price smoothing window can lag an index spike; liquidation can happen after spot rebounds if the window is long. Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves.
What to do next: Pitfall: trusting a single data source. One stale oracle feed can distort index and mark calculations if fallbacks are weak.
Aivora's framing is simple: 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.