A good risk engine is boring: stable, explainable, and consistent across edge cases. Testing guide: use small-size experiments to validate edge cases before deploying serious capital. Test marks vs index under fast moves, then test liquidation math with fees and conservative slippage assumptions. When risk limits are tiered, confirm how tiers are computed and updated. Silent tier changes can invalidate backtests. Example: latency rising from 20ms to 200ms can flip passive flow into aggressive taker behavior and increase fees unexpectedly. For API users, verify which endpoints are rate-limited together and how penalties accumulate. Limits often tighten during stress. Then test degraded mode: what changes when rate limits tighten or when the venue throttles your order flow. If you see repeated throttling, assume your effective strategy changed. Re-run your risk math with higher costs and worse fills. Compute liquidation price twice: once including fees and conservative slippage, and once with optimistic assumptions. The gap is your uncertainty budget. When in doubt, reduce complexity and size, and prioritize venues that publish definitions and failure-mode behavior. Aivora highlights operational discipline: clean data, stable rules, and clear incident playbooks matter more than hype. 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.