Thread by @SqueezeMetrics

When you

1. segment a stock's time-series into a "price" (up, down) and "volatility" (rising, falling) component, and then
2. plot each of those on an axis, and then
3. color the coordinates according to forward returns at each of the [x, y] coordinates...

...you learn a lot about the way a stock trades that you wouldn't be able to learn by simply looking at a chart.

The above stock, for example, responds negatively to increases in volatility (↑), and positively to decreases on volatility (↓).

Seems useful to know.

But you'd *never* learn something like this just by looking at the time-series itself.

This may seem obvious, but to the extent that it is possible, it is good to view data from many angles, cross-sectionally, and in multiple dimensions.

Heatmaps give us three dimensions.

3-D color-coded scatter plots give us four!

Heck, add some interactivity and animation (sliders, time-progression) to a 3-D color-coded scatterplot and you're viewing *five* dimensions of data, or more.

This is advantageous!

Why did we tweet this?

Because we're on the verge of losing a bet with someone.

We thought that we'd start seeing more heatmaps and 3-D visualization in financial research and analysis within a few months. But this has not happened.

Please pull it together, people.