Ema
Ema creates an EMA (exponential moving average) for an updateBy table operation. The formula for an EMA is:
Where:
- is the window size, an input parameter to the method.
- is the EMA.
- is the current value.
Syntax
Ema(tickDecay, pairs...)
Ema(control, tickDecay, pairs...)
Ema(control, timestampColumn, timeDecay, pairs...)
Ema(control, timestampColumn, durationDecay, pairs...)
Ema(timestampColumn, timeDecay, pairs...)
Ema(timestampColumn, durationDecay, pairs...)
Parameters
| Parameter | Type | Description |
|---|---|---|
| tickDecay | long | The decay rate in ticks (rows). |
| pairs | String... | The input/output column name pairs. |
| control | OperationControl | Defines how special cases should behave. If not given, default |
| timestampColumn | String | The column in the source table to use for timestamps. |
| timeDecay | long | The decay rate in nanoseconds. |
| durationDecay | Duration | The decay rate in a Duration object. |
Returns
An UpdateByOperation to be used in an updateBy table operation.
Examples
One column, no groups
The following example calculates the tick-based and time-based EMA of the X column, renaming the resultant column to EmaX. The tick decay rate is set to 5 rows, and the time decay rate is set to 5 seconds. No grouping columns are specified, so the EMA is calculated for all rows.
baseTime = parseInstant("2023-01-01T00:00:00 ET")
source = emptyTable(10).update("Timestamp = baseTime + i * SECOND", "Letter = (i % 2 == 0) ? `A` : `B`", "X = i")
result = source.updateBy([Ema(5, "EmaTickX = X"), Ema("Timestamp", 5 * SECOND, "EmaTimeX = X")])
- source
- result
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.
baseTime = parseInstant("2023-01-01T00:00:00 ET")
source = emptyTable(10).update("Timestamp = baseTime + i * SECOND", "Letter = (i % 2 == 0) ? `A` : `B`", "X = i")
result = source.updateBy([Ema(5, "EmaTickX = X"), Ema("Timestamp", 5 * SECOND, "EmaTimeX = X")], "Letter")
- source
- result
Multiple EMA columns, multiple grouping columns
The following example builds on the previous by calculating the EMA of multiple columns with each UpdateByOperation. Also, the groups are defined by unique combinations of letter and boolean in the Letter and Truth columns, respectively.
baseTime = parseInstant("2023-01-01T00:00:00 ET")
source = emptyTable(20).update("Timestamp = baseTime + i * SECOND", "Letter = (i % 2 == 0) ? `A` : `B`", "Truth = randomBool()", "X = i", "Y = randomInt(5, 10)")
result = source.updateBy([Ema(2, "EmaTickX = X", "EmaTickY = Y"), Ema("Timestamp", 3 * SECOND, "EmaTimeX = X", "EmaTimeY = Y")], "Letter", "Truth")
- source
- result
Multiple UpdateByOperations, multiple grouping columns
The following example builds on the previous by calculating the tick- and time-based EMA of the X and Y columns using different EMA UpdateByoperations. This allows each EMA to have its own decay rate. The decay rates are reflected in the renamed resultant columns.
baseTime = parseInstant("2023-01-01T00:00:00 ET")
source = emptyTable(20).update("Timestamp = baseTime + i * SECOND", "Letter = (i % 2 == 0) ? `A` : `B`", "Truth = randomBool()", "X = i", "Y = randomInt(5, 10)")
emaTickX = Ema(1, "EmaTickX_1row = X")
emaTickY = Ema(5, "EmaTickY_5rows = Y")
emaTimeX = Ema("Timestamp", 2 * SECOND, "EmaTimeX_2sec = X")
emaTimeY = Ema("Timestamp", 4 * SECOND, "EmaTimeY_4sec = Y")
result = source.updateBy([emaTickX, emaTickY, emaTimeX, emaTimeY], "Letter", "Truth")
- source
- result