def update(self, **kwargs):
"""Update the data using `Data.update_symbol`.
Args:
**kwargs: Passed to `Data.update_symbol`.
If two symbols require different keyword arguments, pass `symbol_dict` for each argument.
!!! note
Returns a new `Data` instance."""
new_data = dict()
for k, v in self.data.items():
# Select keyword arguments for this symbol
_kwargs = self.select_symbol_kwargs(k, kwargs)
# Download new data for this symbol
new_obj = self.update_symbol(k, **_kwargs)
# Convert array to pandas
if not isinstance(new_obj, (pd.Series, pd.DataFrame)):
new_obj = np.asarray(new_obj)
index = pd.RangeIndex(start=v.index[-1],
stop=v.index[-1] + new_obj.shape[0],
step=1)
if new_obj.ndim == 1:
new_obj = pd.Series(new_obj, index=index)
else:
new_obj = pd.DataFrame(new_obj, index=index)
# Perform operations with datetime-like index
if isinstance(new_obj.index, pd.DatetimeIndex):
if self.tz_localize is not None:
if not is_tz_aware(new_obj.index):
new_obj = new_obj.tz_localize(
to_timezone(self.tz_localize))
if self.tz_convert is not None:
new_obj = new_obj.tz_convert(to_timezone(self.tz_convert))
new_data[k] = new_obj
# Align index and columns
new_data = self.align_index(new_data, missing=self.missing_index)
new_data = self.align_columns(new_data, missing=self.missing_columns)
# Concatenate old and new data
for k, v in new_data.items():
if isinstance(self.data[k], pd.Series):
if isinstance(v, pd.DataFrame):
v = v[self.data[k].name]
else:
v = v[self.data[k].columns]
v = pd.concat((self.data[k], v), axis=0)
v = v[~v.index.duplicated(keep='last')]
if isinstance(v.index, pd.DatetimeIndex):
v.index.freq = v.index.inferred_freq
new_data[k] = v
# Create new instance
new_index = new_data[self.symbols[0]].index
return self.copy(wrapper=self.wrapper.copy(index=new_index),
data=new_data)
def from_data(cls,
data,
tz_localize=None,
tz_convert=None,
missing_index=None,
missing_columns=None,
wrapper_kwargs=None,
**kwargs):
"""Create a new `Data` instance from (aligned) data.
Args:
data (dict): Dictionary of array-like objects keyed by symbol.
tz_localize (any): If the index is tz-naive, convert to a timezone.
See `vectorbt.utils.datetime.to_timezone`.
tz_convert (any): Convert the index from one timezone to another.
See `vectorbt.utils.datetime.to_timezone`.
Defaults to `tz_convert` in `vectorbt.settings.data`.
missing_index (str): See `Data.align_index`.
Defaults to `missing_index` in `vectorbt.settings.data`.
missing_columns (str): See `Data.align_columns`.
Defaults to `missing_columns` in `vectorbt.settings.data`.
wrapper_kwargs (dict): Keyword arguments passed to `vectorbt.base.array_wrapper.ArrayWrapper`.
**kwargs: Keyword arguments passed to the `__init__` method."""
from vectorbt import settings
# Get global defaults
if tz_localize is None:
tz_localize = settings.data['tz_localize']
if tz_convert is None:
tz_convert = settings.data['tz_convert']
if missing_index is None:
missing_index = settings.data['missing_index']
if missing_columns is None:
missing_columns = settings.data['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)