liquidation heatmap interpreter

The problem

Traders often treat liquidation clusters as guaranteed price targets, leading to premature entries or holding losers into stop hunts. Relying on raw heatmap data without a probabilistic framework results in getting trapped by volatility wicks and cascading liquidations.

What it does

• Identifies high-leverage clusters that act as price magnets or rejection zones.
• Classifies liquidity zones into proximal, magnet, trapped, and distal categories for risk assessment.
• Provides a framework for adjusting position sizing and stop placement relative to liquidity cascades.
• Analyzes the confluence between liquidation levels and market structure to filter noise from significant zones.

Why this beats prompting it yourself

Generic LLM prompts often interpret heatmaps as simple support and resistance. This skill implements a disciplined risk-first methodology that treats liquidity as a volatility map rather than a predictive crystal ball, preventing the "exact target" fallacy.

Use cases

• Setting partial profit-taking zones near high-density liquidation clusters.
• Identifying potential stop-hunt zones to avoid placing stops in high-liquidity wicks.
• Gauging whether a breakout has sufficient liquidity "fuel" to continue or if it is a trap.
• Analyzing market bias by comparing the weight of liquidity above versus below current price.

Known limitations

Requires external access to liquidation heatmap data (e.g., Coinglass, Kingfisher). Does not provide real-time price feeds or automated execution.