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Deep Learning Models for Ethereum Perpetual Contract Prediction


Alright, let’s do this the clean way. Focus: ETH contracts on Deribit.


Contrarian lens

One-sided funding on Deribit can mean a crowded trade. I wait for a rejection at a clean level and confirmation from RSI(14).


Crowded trades can still go further—but they punish sloppy entries first.

ThingWhat to do
Choppy marketLower leverage, fewer trades, wait for clean levels.
Trending marketLet winners run, trail stop, don’t over-take-profit.
High fundingReduce hold time or wait for better entry.

One-sentence rule

If structure is unclear, I do nothing. If it’s clear, I risk small and follow the plan.


Rules differ by exchange; check margin and liquidation details on your platform. Educational only, not financial advice.


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.