Resampling

systematica.generic.resampling

resample_nb

resample_nb(
    close: numpy.ndarray,
    index: numpy.ndarray,
    freq: numpy.timedelta64,
) ‑> numpy.ndarray

Resample financial data to a specified frequency using a Numba-compiled function.

This function utilizes Numba’s Just-in-Time (JIT) compilation to optimize performance for resampling time series data, keeping the last value within each resampling period.

Parameters:

Name	Type	Default	Description
close	tp.Array2d	--	A 2-dimensional array of asset closing prices, where rows represent time and columns represent assets.
index	tp.Array1d	--	A 1-dimensional array of datetime values corresponding to the time index of close.
freq	np.timedelta64	--	A Numpy timedelta64 object representing the desired frequency for resampling. For example, np.timedelta64(1, 'D') for daily data, np.timedelta64(1, 'W') for weekly, etc.

Returns:

Type	Description
tp.Array2d	A 2-dimensional array of resampled closing prices, with rows corresponding to the resampled time periods and columns to the assets.

resample

resample(
    data: vectorbtpro.data.base.Data,
    freq: str,
    **kwargs,
) ‑> vectorbtpro.data.base.Data

Reasample data.

Parameters:

Name	Type	Default	Description
data	vbt.Data	--	Data object.
freq	str	--	Frequency. kwargs Feed dt.date_range. See vectorbtpro.

Returns:

Type	Description
vbt.Data	Resampled vbt Data object.