Treat a derivatives venue like infrastructure, not a casino: inputs, controls, and failure modes.
The mechanism: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. ADL typically appears only after the insurance buffer is stressed. Look for disclosure and predictable ranking rules.
Where it breaks: Funding is a transfer between traders, but timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits.
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 mark-price smoothing window can lag an index spike; liquidation can happen after spot rebounds if the window is long. 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.
In Aivora notes, transparency beats cleverness when markets get loud. Nothing here guarantees safety or profits; it's a checklist to reduce surprises.
The mechanism: Write down the exact references used: index price, mark price, and last price. Then confirm which reference drives margin checks and liquidation triggers. ADL typically appears only after the insurance buffer is stressed. Look for disclosure and predictable ranking rules.
Where it breaks: Funding is a transfer between traders, but timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits.
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 mark-price smoothing window can lag an index spike; liquidation can happen after spot rebounds if the window is long. 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.
In Aivora notes, transparency beats cleverness when markets get loud. Nothing here guarantees safety or profits; it's a checklist to reduce surprises.
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.