Slope Health and Distance Health Explained in Trend Detector
Slope Health and Distance Health are plain-English context labels that help users understand whether trend structure looks supportive, stretched, weak, or extreme.
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Use the live Trend Detector to compare this concept with the rest of the TradingSimuLab model stack.
Open the Trend DetectorWhy health labels exist
Raw trend metrics can be hard to interpret quickly. Slope Health and Distance Health translate structure into easier language. They help the user understand whether the trend base is supportive and whether price is sitting at a reasonable or stretched distance from that base.
These labels are not separate predictions. They are interpretation aids. They make the Trend Detector more readable without exposing proprietary model weights or encouraging users to overfit to one number.
Slope health
Slope Health focuses on whether the trend base is rising, falling, flat, or extreme. A healthier slope generally supports the direction of the current trend read. A weak or flat slope can make the read less decisive. An extreme slope can sometimes point to a fast move that deserves more caution.
The key is context. A bullish slope can support a bullish trend, but if exhaustion risk is high or timing is fragile, the read should still be balanced.
Distance health
Distance Health focuses on how stretched price is relative to its trend base. Healthy distance means the move is not obviously too far from its base. Extreme distance can suggest overextension. Weak distance can suggest the price is not supporting the trend read cleanly.
Distance Health is especially useful for avoiding late entries in research workflows. It reminds users that the quality of a trend is not only about direction. It is also about where price sits relative to the structure that supports it.
Using health labels with the model stack
Use health labels after the main trend strength and exhaustion read. Then compare with Trend Persistence for durability, Timing Model for setup quality, and Risk Simulation for downside path. If health labels are supportive across the stack, the read is cleaner. If they conflict, the user should avoid forcing a simple conclusion.
This is the kind of layered interpretation that makes the dashboard more valuable than a single-indicator score.