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Version: Python

rolling_avg_time

rolling_avg_time creates a simple moving average in an update_by table operation using time as the windowing unit. Time can be specified as integer numbers of nanoseconds or strings. The moving average can be calculated using forward and/or backward windows.

SMA=∑1nxinSMA = \frac{\sum_{1}^{n}x_{i}}{n}

Where:

  • nn is the number window size in ticks
  • xix_{i} is the current value

For a time-based SMA, n is the number of observations in the window, determined by fwd_time and rev_time.

Syntax​

rolling_avg_time(
    ts_col: str, cols: list[str],
    rev_time: Union[int, str],
    fwd_time: Union[int, str],
) -> UpdateByOperation

Parameters​

ParameterTypeDescription
ts_colstr

The name of the column containing timestamps.

colslist[str]

The column(s) to be operated on. These can include expressions to rename the output (e.g., NewCol = Col). If None, the rolling average is calculated for all applicable columns.

rev_timeUnion[int,str]

The look-behind window size. This can be expressed as an integer in nanoseconds or a string duration, e.g., "PT00:00:00.001" or "PTnHnMnS", where H is hour, M is minute, and S is second.

fwd_timeUnion[int,str]

The look-forward window size. This can be expressed as an integer in nanoseconds or a string duration, e.g., "PT00:00:00.001" or "PTnHnMnS", where H is hour, M is minute, and S is second.

Returns​

An UpdateByOperation to be used in an update_by table operation.

Examples​

The following example performs an update_by on the source table using three rolling_avg_time operations. Each uses different rev_time and fwd_time values to show how they affect the output.

from deephaven.updateby import rolling_avg_time
from deephaven.time import to_j_instant
from deephaven import empty_table

base_time = to_j_instant("2023-01-01T00:00:00 ET")

source = empty_table(10).update(
    [
        "Timestamp = base_time + i * SECOND",
        "Letter = (i % 2 == 0) ? `A` : `B`",
        "X = randomInt(0, 25)",
    ]
)

op_before = rolling_avg_time(
    ts_col="Timestamp",
    cols=["WindowBefore = X"],
    rev_time="PT00:00:03",
    fwd_time=int(-1e9),
)
op_after = rolling_avg_time(
    ts_col="Timestamp",
    cols=["WindowAfter = X"],
    rev_time="-PT00:00:01",
    fwd_time=int(3e9),
)
op_middle = rolling_avg_time(
    ts_col="Timestamp",
    cols=["WindowMiddle = X"],
    rev_time="PT00:00:01",
    fwd_time="PT00:00:01",
)

result = source.update_by(ops=[op_before, op_after, op_middle], by="Letter")