ema_tick

ema_tick creates a tick-based (row-based) EMA (exponential moving average) for an update_by table operation. The formula for the tick-based EMA of a column $X$ is:

$a = e^{\frac{-1}{\tau}}$

$\bar{x}_0 = x_0$

$\bar{x}_i = a*\bar{x}_{i-1} + (1-a)*x_i$

Where:

$\tau$ is decay_ticks, an input parameter to the method.
$\bar{x}_i$ is the exponential moving average of $X$ at step $i$ .
$x_i$ is the current value.
$i$ denotes the time step, ranging from $i=1$ to $i = n-1$ , where $n$ is the number of elements in $X$ .

Syntax

ema_tick(
    decay_ticks: int,
    cols: list[str],
    op_control: OperationControl = None,
) -> UpdateByOperation

Parameters

Parameter	Type	Description
decay_ticks	int	The decay rate in ticks (rows).
cols	list[str]	The columns to be operated on. These can include expressions to rename the output (e.g., `NewCol = Col`). If `None`, EMA is calculated for all columns.
op_control optional	OperationControl	Defines how special cases should behave. The default value is `None`, which uses default `OperationControl` settings.

Returns

An UpdateByOperation to be used in an update_by table operation.

Examples

One column, no groups

The following example calculates the tick-based (row-based) EMA of the X column, renaming the resultant column to EmaX. The decay rate, decay_ticks, is set to 2. No grouping columns are specified, so the EMA is calculated over all rows.

from deephaven.updateby import ema_tick
from deephaven import empty_table

source = empty_table(10).update(["Letter = (i % 2 == 0) ? `A` : `B`", "X = i"])

result = source.update_by(ops=ema_tick(decay_ticks=2, cols=["EmaX = X"]))

One EMA column, one grouping column

The following example builds on the previous by specifying Letter as the key column. Thus, the EMA is calculated on a per-letter basis.

from deephaven.updateby import ema_tick
from deephaven import empty_table

source = empty_table(10).update(["Letter = (i % 2 == 0) ? `A` : `B`", "X = i"])

result = source.update_by(ops=ema_tick(decay_ticks=2, cols=["EmaX = X"]), by=["Letter"])

Multiple EMA columns, multiple grouping columns

The following example builds on the previous by calculating the EMA of multiple columns in the same UpdateByOperation. Also, the groups are defined by unique combinations of letter and boolean in the Letter and Truth columns, respectively.

from deephaven.updateby import ema_tick
from deephaven import empty_table

source = empty_table(20).update(
    [
        "Letter = (i % 2 == 0) ? `A` : `B`",
        "Truth = randomBool()",
        "X = i",
        "Y = randomInt(5, 10)",
    ]
)

result = source.update_by(
    ops=ema_tick(decay_ticks=2, cols=["EmaX = X", "EmaY = Y"]), by=["Letter", "Truth"]
)

Multiple `UpdateByOperations`, multiple grouping columns

The following example builds on the previous by calculating the EMA of multiple columns, each with its own UpdateByOperation. This allows each EMA to have its own decay rate. The different decay rates are reflected in the renamed resultant column names.

from deephaven.updateby import ema_tick
from deephaven import empty_table

source = empty_table(20).update(
    [
        "Letter = (i % 2 == 0) ? `A` : `B`",
        "Truth = randomBool()",
        "X = i",
        "Y = randomInt(5, 10)",
    ]
)

ema_x = ema_tick(decay_ticks=2, cols=["EmaX2rows = X"])
ema_y = ema_tick(decay_ticks=4, cols=["EmaY5rows = Y"])

result = source.update_by(ops=[ema_x, ema_y], by=["Letter", "Truth"])