People talk about AI as if it is magic, but contract trading systems still live or die on definitions and controls. Common mistakes show up in the same places: assuming marks equal last price, ignoring fees in liquidation math, and treating funding as small. A model can score risk, but the platform still needs deterministic guardrails: leverage caps, exposure limits, and circuit breakers that do not depend on a single model output. Mistake to avoid: optimizing leverage while ignoring book depth. Liquidity vanishes first, and leverage just amplifies the damage. 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. Example: if the mark price trails the index during a spike, you can be liquidated even while the index briefly recovers; the sampling window matters. Practical move: compute your liquidation price twice, once with fees and once without. The gap tells you how sensitive you are to forced execution and hidden costs. Operational risk is real: audit keys, log requests, and keep emergency kill switches that can disable automation instantly. If you want a sanity check, compare what Aivora calls the risk pipeline: inputs -> checks -> liquidation path -> post-incident logging. Derivatives are risky. Use independent judgment and test your 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.