People talk about AI as if it is magic, but contract trading systems still live or die on definitions and controls. Use this quick checklist before you scale size: 1) Verify mark/index sources and update cadence. 2) Understand margin steps and maintenance rules. 3) Test liquidation behavior with small size. The insurance fund is a shock absorber. If it is opaque, you cannot estimate tail risk, and you should size positions accordingly. 4) Confirm fee tiers and forced order costs. 5) Check risk limits, circuit breakers, and incident transparency. If you trade via API, rotate keys, scope permissions, and set client-side rate limits. Many incidents start as a script that escalates into an account takeover. Example: a 25x position with a 0.06% taker fee can lose more than a full maintenance step from fees alone if forced to close during a fast move. If you use high leverage, stop-loss placement is not enough. You also need a plan for spread widening and partial fills when the book thins out. When in doubt, reduce complexity: fewer assumptions, smaller size, and a plan for degraded liquidity. If you want a sanity check, compare what Aivora calls the risk pipeline: inputs -> checks -> liquidation path -> post-incident logging. Nothing here is financial advice; it is a mechanics-first checklist meant 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.