Ten years of trading days, arranged along Hilbert curves.
Hilbert curves can be used to compress a 1D dataset into a 2D chart. This is particularly useful for very large datasets. In this demonstration, ten years of daily stock prices are plotted along hilbert curves. Data close together in time will appear close together in 2D-space.
The charts above show each company's return over the month, 21 trading days, to every day of the past ten years, all on one scale: orange where the price rose over the month before, and blue where it fell, the stronger the colour the bigger the move. When the whole market fell, as in March 2020, the same places are blue on every chart.
The curves are in reverse chronological order, so the most recent days are at the top left. Each year's days are outlined, and the years named on the first chart.
The advantage of using a Hilbert curve is that the data density is very high. The charts above show the data for every single day over 10 years, so that's roughly 2,500 data points.
On the other hand, the Hilbert curve makes it difficult to see the actual chronological location of a particular datum. It's possible to see roughly where in the ten years a point is by looking at the four quadrants, but only if you are already acquainted with Hilbert curves and know which directions they take. The outlines here help, but within each year it's still hard to tell which way the days run.
Horizon charts achieve a higher data density than normal line charts while retaining the chronological scale.
These show each company's return over the year, 252 trading days, to every day of the same ten years, in the same colours: monthly returns change too often to read along a line this long.
Hilbert curves provide impressive data density and are good at preserving the locality of the original one-dimensional dataset.
However, it becomes almost impossible to discern the precise location of a particular point relative to the original one-dimensional scale, so "high-density" alternatives such as Horizon Charts may be better if that's important.
By Jason Davies.