Aivora AI-native exchange insights
Home how to anomaly detection on ai derivatives exchange AI-Based Stop Loss Optimization for Cortex Futures

AI-Based Stop Loss Optimization for Cortex Futures

Here’s the “I wish someone told me earlier” version. Focus: ARB contracts on Deribit.


Setup

Use 5m. Confirm direction with ATR(14), then use funding rate to avoid chasing. If they fight, you sit out—fr that’s discipline.


Execution

  • Entry: break + retest > first impulse candle.
  • Stop: cooldown after 2 losses where the idea is invalid.
  • Exit: scale out, then max daily loss limit for the runner.

What to log

  • Entry reason (one sentence)
  • Stop placement + why
  • Fees + funding paid
  • Emotion (calm / rushed / tilted)
  • Lesson


Educational only, not financial advice. Leverage is risky—use money you can afford to lose.


Wrap: Keep it boring and repeatable—your future self will thank you.

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.