Better, faster EMAs

Without a doubt, the most requested Deephaven feature has been more powerful moving averages.
In version 0.15.0, Deephaven improved aggregations through update_by.

Now, you can compute exponential moving averages (EMAs) that are more efficient and properly update when values in the middle of your table change.

Let's look at an example:

from deephaven import time_table
from deephaven.updateby import ema_time

t1 = time_table("PT00:00:00.1").update(
    [
        "Label = ii%2 == 0 ? `cos` : `sin`",
        "X = ii%2 == 0 ? cos(0.05*ii)+0.7*random() : sin(0.01*ii)+0.7*random()",
    ]
)

t2 = t1.update_by([ema_time("Timestamp", "PT00:00:01", "XEma=X")], by="Label")

from deephaven.plot import Figure

p = (
    Figure()
    .plot_xy("Value", t=t2, x="Timestamp", y="X", by=["Label"])
    .plot_xy("EMA", t=t2, x="Timestamp", y="XEma", by=["Label"])
    .show()
)

Future releases will support Simple Moving Averages (SMAs), moving standard deviations, and more. See the updateby Python module for the latest list of supported update_by aggregations. If there is a new aggregation you want to see, tell us on Slack.

Brought to you by Deephaven's update_by