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Version: Java (Groovy)

ByEmaSimple

ByEmaSimple calculates Exponential Moving Averages (EMA) in the Deephaven Query Language.

Syntax​

The constructor for ByEmaSimple is shown below.

new ByEmaSimple(
        nullBehavior,
        nanBehavior,
        type,
        mode,
        timescale,
        timeUnit
)

Parameters​

ParameterTypeDescription
nullBehaviorBadDataBehavior
nanBehaviorBadDataBehavior
modeenum
  • TICK uses each new entry of data to determine the EMA window.
  • TIME uses time to determine the EMA window.
timescaledouble

The number of units for the EMA window.

timeUnitenum
  • DAYS - Time unit representing twenty four hours.
  • HOURS - Time unit representing sixty minutes.
  • MICROSECONDS - Time unit representing one thousandth of a millisecond.
  • MILLISECONDS - Time unit representing one thousandth of a second.
  • MINUTES - Time unit representing sixty seconds.
  • NANOSECONDS - Time unit representing one thousandth of a microsecond.
  • SECONDS - Time unit representing one second.
typeenum
  • LEVEL - sets the first EMA value to the first value recorded.
  • DIFFERENCE - sets the first EMA value to zero assuming the change is initially zero.

Returns​

Parameterized constructor to use for calculating an EMA.

Examples​

In the following example, we use the time-table and update methods to create a table with random data to simulate real-time data. We then use ByEmaSimple to configure what we want for our EMA.

ema1sec is set to use BD_SKIP to skip null values and skip NaN values. The type is set to LEVEL and we are using TIME rather than TICKS.

ema_data then uses ema1sec to calculate the total EMA on a timescale of 10 seconds.

import io.deephaven.numerics.movingaverages.ByEma
import io.deephaven.numerics.movingaverages.AbstractMa
import io.deephaven.numerics.movingaverages.ByEmaSimple
import java.util.concurrent.TimeUnit


ema1sec = new ByEmaSimple(
        ByEma.BadDataBehavior.BD_SKIP,
        ByEma.BadDataBehavior.BD_SKIP,
        AbstractMa.Type.LEVEL,
        AbstractMa.Mode.TIME,
        10,
        TimeUnit.SECONDS
)

chars = "ABCDE".toCharArray()

result = timeTable('00:00:01').update("Sym = chars[ new Random().nextInt(5)]", "Numbers = new Random().nextInt(100)")

ema_data = result.view("EMA_data = ema1sec.update(Timestamp, Numbers)").tail(1)
ema_data_grouped = result.view("Sym", "EMA_data = ema1sec.update(Timestamp, Numbers, Sym)").lastBy("Sym").tail(5)