A lot of losses come from tiny assumptions: which price triggers liquidation, when funding hits, and how fees are applied.
The mechanism: Latency is a risk factor. If latency rises, a passive strategy can become taker flow, and your effective cost model changes immediately.
Where it breaks: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
A simple test: Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes. Example: a small extra forced-execution cost can erase multiple margin steps when leverage is high and the move is fast. If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises.
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 note is about system mechanics; outcomes are your responsibility.
The mechanism: Latency is a risk factor. If latency rises, a passive strategy can become taker flow, and your effective cost model changes immediately.
Where it breaks: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable.
A simple test: Run a small-size rehearsal when liquidity is thin. Observe how stop orders trigger and how mark/last prices diverge around spikes. Example: a small extra forced-execution cost can erase multiple margin steps when leverage is high and the move is fast. If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises.
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 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.