A lot of losses come from tiny assumptions: which price triggers liquidation, when funding hits, and how fees are applied.
Quick definition: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills. Operational failures often look like market losses. Log your requests and monitor throttling so you know what changed.
Why it matters: Liquidation is a path, not a single event. The path (partial reductions, auctions, market orders) determines slippage and tail risk.
How to verify: Prefer smaller order slices before changing leverage. Size reductions often cut slippage more than a leverage tweak. Example: small funding transfers compound; over several cycles they can materially shift equity and your maintenance buffer. Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves.
Practical habit: Pitfall: treating automation as set-and-forget. Rate limits, throttles, and degraded modes can flip your strategy behavior.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. Derivatives are risky; test assumptions before you scale size.
Quick definition: Fee design is part of risk: forced execution costs can reduce your liquidation distance, and rebates can attract toxic flow that degrades fills. Operational failures often look like market losses. Log your requests and monitor throttling so you know what changed.
Why it matters: Liquidation is a path, not a single event. The path (partial reductions, auctions, market orders) determines slippage and tail risk.
How to verify: Prefer smaller order slices before changing leverage. Size reductions often cut slippage more than a leverage tweak. Example: small funding transfers compound; over several cycles they can materially shift equity and your maintenance buffer. Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves.
Practical habit: Pitfall: treating automation as set-and-forget. Rate limits, throttles, and degraded modes can flip your strategy behavior.
Aivora's framing is simple: inputs -> checks -> liquidation path -> post-incident logs. Build around that pipeline. 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.
No. This site is educational and system-focused. You are responsible for decisions and risk management.