Our charts are equipped with four different scales you can use to display the data. Understanding their differences and applying the correct scale is crucial to correctly converting numbers to visual graphs.

You can select the desired scale here:

# How to know which scale is correct?

Well, it depends on what you are looking at because diverse data types require distinctive scales. It all comes down to how the algorithm interprets underlying numbers to paint a chart for you so that you can analyse these numbers in a visual way.

Here are the four available scales we can show you the data with:

## Linear scale (lin)

The most popular scale (that doesn't mean the most correct) to display data. To paint a chart, the algorithm allocates the same amount of chart space (pixels) to the same piece of raw data (i.e., \$1 change). That basically means you will always see a one-dollar change in the data in the same way on the chart. Sounds reasonable until it doesn't.

#### Look at Apple's price since its IPO in December 1980:

It seems like the stock price was barely moving for the first 30 years and skyrocketed only after the financial crisis in 2008, right? Well, it's because of the faulty linear scale, which treats every one-dollar movement in price in the same way. To put it differently, a one-dollar movement in stock price occupies the same space (the same number of pixels) on the chart all the time. What's the problem with that?

The problem is the stock price in 1980 was \$0.16, meaning that a one-dollar rise meant more than a 600% gain in price back then, and now – when the stock price is approximately \$142 (as of 01/02/2023), the same one-dollar rise means only 0.7% of the price movement. Yet, the chart represents both movements in the same (linear) way. Hence the depressed look of the first 30 years and thus the confusion when viewing the results.

The rule of thumb is this: the linear scale should be used only with metrics and data displayed in the percentage values, such as margins, the average growth of something, or upsides.

## Logarithmic scale (log)

Look at the same data (Apple's stock price) but visualised on the logarithmic scale.

Pretty much a different picture, huh? A logarithmic (aka geometrical scale) is trying to plot data points as percentage changes, not as a hard fixed equal increment. In that case, each interval is increased by a factor of the base of the logarithm. That helps you better understand what is happening on the chart.

Non-percentage metrics like stock price, revenues, EPS, cash, debt, price target, etc., should always be displayed on a logarithmic scale unless you have a good reason not to do this, in which case you probably don't need this explanation anyway.

## Percentage scale (%)

Useful, especially when you want to compare several companies or metrics with each other. For example, to know if the revenues of Apple grow as fast as the revenues of Zoom. Comparing pure numbers doesn't make sense due to a different – nomen omen – the scale of the sales.

Three years ago, Apple’s revenues were \$260bn, and now are at the \$394bn level. As for Zoom, revenues were at \$0.5bn and now are at \$4.35bn. It’s difficult to say whose sales grew faster, but it becomes apparent when you plot the data on the percentage scale.

Use the percentage scale always when comparing two non-percentage values or companies. It's also pretty useful when comparing the stock price growth rate vs. the EPS growth rate or Revenue Estimates Revisions in percentage terms vs. stock price growth to see if the positive revision is already fully discounted in the stock price or not.

## Percent off high scale (po)

It shows the magnitude of a decline in a metric reading from its recent high; as the name suggests, the value is displayed as a percentage. For example, 25% for a stock price means the stock price already fell 25% from its last high.

It’s primarily used to determine how low can a stock price go during the correction period according to its history, but other uses are also common. One of them is to compare how much the stock price fell from its recent high with how much the price target was reduced (or an estimated forecast was reduced). This helps you to consider whether the pessimistic analyst’s revision justified the magnitude of the price dive or not.

Here is one example of how it looks on the chart.

# Displaying different scales at the same time

To make your life easier, we've introduced the auto-scale feature, which basically tries to suggest the correct scale for the particular metric. When you add a metric already shown in percentage values (like Upside), we will switch your view to the linear scale.

But...

If you’d like to add the Price and Price Target parameters at the same time, we will split the chart and switch the scale to logarithmic since it’s more suitable for dollar-based metrics such as these.

On some rare occasions, you might want to change the scales to see the different views. You can do so by clicking the different scale view, but make sure you know what you’re doing.

The default view, when adding different metrics that should be displayed in different scales, looks like this: