def from_data(cls: tp.Type[DataT],
data: tp.Data,
tz_localize: tp.Optional[tp.TimezoneLike] = None,
tz_convert: tp.Optional[tp.TimezoneLike] = None,
missing_index: tp.Optional[str] = None,
missing_columns: tp.Optional[str] = None,
wrapper_kwargs: tp.KwargsLike = None,
**kwargs) -> DataT:
"""Create a new `Data` instance from (aligned) data.
Args:
data (dict): Dictionary of array-like objects keyed by symbol.
tz_localize (timezone_like): If the index is tz-naive, convert to a timezone.
See `vectorbt.utils.datetime_.to_timezone`.
tz_convert (timezone_like): Convert the index from one timezone to another.
See `vectorbt.utils.datetime_.to_timezone`.
missing_index (str): See `Data.align_index`.
missing_columns (str): See `Data.align_columns`.
wrapper_kwargs (dict): Keyword arguments passed to `vectorbt.base.array_wrapper.ArrayWrapper`.
**kwargs: Keyword arguments passed to the `__init__` method.
For defaults, see `data` in `vectorbt._settings.settings`."""
from vectorbt._settings import settings
data_cfg = settings['data']
# Get global defaults
if tz_localize is None:
tz_localize = data_cfg['tz_localize']
if tz_convert is None:
tz_convert = data_cfg['tz_convert']
if missing_index is None:
missing_index = data_cfg['missing_index']
if missing_columns is None:
missing_columns = data_cfg['missing_columns']
if wrapper_kwargs is None:
wrapper_kwargs = {}
data = data.copy()
for k, v in data.items():
# Convert array to pandas
if not isinstance(v, (pd.Series, pd.DataFrame)):
v = np.asarray(v)
if v.ndim == 1:
v = pd.Series(v)
else:
v = pd.DataFrame(v)
# Perform operations with datetime-like index
if isinstance(v.index, pd.DatetimeIndex):
if tz_localize is not None:
if not is_tz_aware(v.index):
v = v.tz_localize(to_timezone(tz_localize))
if tz_convert is not None:
v = v.tz_convert(to_timezone(tz_convert))
v.index.freq = v.index.inferred_freq
data[k] = v
# Align index and columns
data = cls.align_index(data, missing=missing_index)
data = cls.align_columns(data, missing=missing_columns)
# Create new instance
symbols = list(data.keys())
wrapper = ArrayWrapper.from_obj(data[symbols[0]], **wrapper_kwargs)
return cls(
wrapper,
data,
tz_localize=tz_localize,
tz_convert=tz_convert,
missing_index=missing_index,
missing_columns=missing_columns,
**kwargs
)