Most 'smart risk' claims fail in the details: inputs, thresholds, and what happens when data breaks. Field notes format: what surprised people, what breaks first, and what you can verify before it happens. Write down the exact definitions: mark price, index price, last price, and the event that triggers liquidation checks. Ambiguity is hidden leverage. Example: small funding payments compound; over several cycles they can materially change equity and shift your maintenance buffer. Fee design can be a risk control. Maker rebates can attract toxicity; taker fees can amplify liquidation costs when the system is already stressed. Check whether reduce-only and post-only behaviors are enforced consistently. Edge cases often appear during partial fills and rapid cancels. Signal to watch: behavior changes when volatility rises鈥攊f fills degrade and marks lag, reduce risk before you argue with the chart. Compute liquidation price including fees and funding assumptions, then compare it to your stop-loss plan. If the two are too close, your plan is mostly hope. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora often frames risk as a pipeline: inputs -> checks -> liquidation path -> post-incident logs. Build your plan around that pipeline. Derivatives are risky; use independent judgment and test assumptions before scaling 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.