Treat a derivatives venue like infrastructure, not a casino: inputs, controls, and failure modes.
Concept first: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable. Operational failures often look like market losses. Log your requests and monitor throttling so you know what changed.
Edge cases: 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.
Checklist: Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Treat cross margin as a correlated portfolio. Correlations converge during stress, so diversification can vanish when you need it most.
Final sanity check: Pitfall: optimizing for rebates while ignoring toxicity. Toxic flow can widen spreads and raise liquidation costs.
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.
Concept first: An AI risk layer should be explainable: it can rank anomalies, but deterministic guardrails must remain stable and auditable. Operational failures often look like market losses. Log your requests and monitor throttling so you know what changed.
Edge cases: 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.
Checklist: Test reduce-only and post-only behavior with partial fills and fast cancels. Edge cases often appear during rapid moves. Example: a temporary rate-limit tightening can cause missed exits and worse fills even without a dramatic price crash. Treat cross margin as a correlated portfolio. Correlations converge during stress, so diversification can vanish when you need it most.
Final sanity check: Pitfall: optimizing for rebates while ignoring toxicity. Toxic flow can widen spreads and raise liquidation costs.
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.