Opinion: The real-world value of liquidation heat maps is severely overestimated and should not be given too much weight in the trading system

See alsoSeptember 28, BlockBeats News, Coinkarma founder Benson Sun points out that the usefulness of a liquidation heat map is overestimated. It is based on trading history with leverage assumptions such as 5x to 50x and dense zones do not equal true pending positions. This article explains misread sources and reference value boundaries.

The label: liquidation heatmap, Benson Sun, Coinkarma, derivatives trading and contract clearing

Featured mapAbstract technology and finance styles representing the heat of data and probability distribution, excluding real people, exchange trademarks and specific price figures.

On September 28, BlockBeats News reported that Coinkarma founder Benson Sun stated that the utility of liquidation hotspots in trading systems is overestimated. For readers who are interested in contract trading and derivatives, it's worth noting this: The liquidation chart isn't a prediction table for where bitcoin prices will inevitably go, but rather a set of probabilities generated based on historical transactions with leverage assumptions.

One, density zones are not real position maps.

A common way to generate a liquidation heat map is based on the history of market transactions, and then fold in hypothetical leverage multiples such as 5x, 10x, 20x, 30x, 50x, which reflect at what prices positions may be cleared. Benson Sun emphasizes that the clearing density area seen by users does not mean there are actually an equal number of positions waiting to be blown up.

Variable moves the clearing position

The core of the original idea was that there is a gap between hypothetical and real trades. Someone may have already left, someone will increase or decrease their position, and some people will add collateral. As long as the size of the position, the premium or average cost changes, the liquidation heat map moves with it. In other words, dense blocks are stationary on the screen, behind which corresponds to a set of dynamic positions; if traders only look at the position of the brown block while ignoring the change in position, it is easy to misread the model output as market facts.

Three, the price of aluminum will surely sweep this place away.

Many traders find that there is a large clearing density area near a certain price, and the direct interpretation of prices will be swept away. This judgment moves the liquidation heatmap from an auxiliary tool to a definitive conclusion. In derivatives markets, it may indeed lead to chain-tightening, but the dense zone only indicates where potential liquidity congregates and does not guarantee that prices must reach there. The market path is influenced by capital flows towards emotions, order book depth and trader behavior in common, and these variables are difficult for a single tool to cover.

Fourth, what it means to the reader.

This reminds readers to re-evaluate the importance of a liquidation heat chart. It can help observe potential risk bands in contract markets, but cannot be used as a standalone basis for trading. A more robust way to understand it is to view the liquidation graph as a reference to where there may be a clearing pressure spike rather than a promise that prices will inevitably go up. When the tool is given too much interpretative power, it could instead create false certainty.

Follow-up: The value of tools returns to the auxiliary layer

Discussions surrounding the clearing heat chart may lead traders to place more emphasis on hypothetical conditions and failure scenarios of an instrument. For novices, the key is not memorizing a dense area in a graph but understanding why it moves; for advanced traders, the focus is putting the cleared chart back into its low-weight position and avoiding being dragged along by narratives about magnetic tiles. In the long run, competition among derivatives trading instruments will be driven not only by whether charts are intuitive but also by whether users can identify implicit assumptions within them.