> For the complete documentation index, see [llms.txt](https://torch-1.gitbook.io/litepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://torch-1.gitbook.io/litepaper/system-mechanics/sharpness-quality.md).

# Sharpness quality

Range sharpness measures how narrow or precise a trader's selected price interval is, relative to the current price of the token. It captures the trader’s willingness to make a high-confidence, precision prediction versus a broad, lower-risk hedge.

This produces a dimensionless value that is consistent across assets with different price scales. To map this to a normalized score between 0 and 1 (where 1 is extremely sharp and 0 is extremely broad), we use an exponential scoring function:

$$
\text{Sharpness Score} = 1 - e^{-\frac{0.25}{\text{Range Sharpness}}}
$$

With initial parameters set to k=0.25:

<figure><img src="/files/wiPcarl1mqd39tK3MjOe" alt="" width="563"><figcaption></figcaption></figure>

Sample values:

<table><thead><tr><th width="140.8182373046875"></th><th width="133.9090576171875"></th><th width="304.181640625"></th></tr></thead><tbody><tr><td>0.05</td><td>0.993</td><td>Very sharp, high precision bet</td></tr><tr><td>0.10</td><td>0.95</td><td>Moderately sharp</td></tr><tr><td>0.25</td><td>0.63</td><td>Balanced between risk and coverage</td></tr><tr><td>0.50</td><td>0.39</td><td>Broad, conservative prediction</td></tr><tr><td>1.00</td><td>0.22</td><td>Very wide range</td></tr></tbody></table>