If a futures platform feels 'random' under stress, the randomness is usually in definitions and fallbacks.
What it is: Funding is a transfer between traders, but timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits.
What to check: Look for the platform's fallback rules: what happens if a feed is stale, if the book is thin, or if volatility spikes faster than normal sampling windows.
How to test it: If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget.
Common pitfalls: Pitfall: ignoring fees and funding in liquidation math. The platform can close you earlier than your stop-loss plan expects.
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.
What it is: Funding is a transfer between traders, but timing, rounding, and caps can change equity at the worst moment. Verify schedule and limits.
What to check: Look for the platform's fallback rules: what happens if a feed is stale, if the book is thin, or if volatility spikes faster than normal sampling windows.
How to test it: If you automate, use scoped API keys, IP allow-lists, and exponential backoff. Limits often tighten exactly when volatility rises. Example: doubling size in a thin book can more than double slippage because depth is not linear near top levels. Compute liquidation price twice: once with optimistic assumptions, and once with conservative slippage and fees. The gap is your uncertainty budget.
Common pitfalls: Pitfall: ignoring fees and funding in liquidation math. The platform can close you earlier than your stop-loss plan expects.
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.