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The Role of Machine Learning in Curve Liquidation Prevention

Here’s the “I wish someone told me earlier” version. Focus: ETH contracts on Gate.io.


Quick Q&A

What’s the first filter?
Structure + MACD.
How to avoid chasing?
Retest entries; confirm with open interest.
What kills good trades?
Fees/funding + oversizing. lowkey it’s boring but true.
Exit idea?
Scale out in parts; protect with trailing stop.

Note: Common mistake: ignoring fees/funding because it ‘seems small’. Fix it by slowing down and sizing smaller.


Leverage is risky—use money you can afford to lose. 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.