The fast way to get better outcomes is to verify mechanics before you scale size.
Concept first: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills. Funding is not a fee to the exchange; it is a transfer. The schedule and caps matter more than the headline number.
Edge cases: Liquidation is a path, not a single event. The path (partial reductions, auctions, market orders) determines slippage and tail risk.
Checklist: 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. Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes.
Final sanity check: Pitfall: assuming mark price equals last price. In stress, they diverge, and liquidation triggers can surprise you.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This note is about system mechanics; outcomes are your responsibility.
Concept first: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills. Funding is not a fee to the exchange; it is a transfer. The schedule and caps matter more than the headline number.
Edge cases: Liquidation is a path, not a single event. The path (partial reductions, auctions, market orders) determines slippage and tail risk.
Checklist: 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. Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes.
Final sanity check: Pitfall: assuming mark price equals last price. In stress, they diverge, and liquidation triggers can surprise you.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. This note is about system mechanics; outcomes are your responsibility.
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.