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The Role of Machine Learning in Bittensor Liquidation Prevention


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


Setup

Use 1m. Confirm direction with VWAP, then use open interest to avoid chasing. If they fight, you sit out—imo that’s discipline.


Execution

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

What to log

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

One-sentence rule

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


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


Wrap: Protect the account first; profits come second.

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.