def from_ts(cls: tp.Type[RangesT],
ts: tp.ArrayLike,
gap_value: tp.Optional[tp.Scalar] = None,
attach_ts: bool = True,
wrapper_kwargs: tp.KwargsLike = None,
**kwargs) -> RangesT:
"""Build `Ranges` from time series `ts`.
Searches for sequences of
* True values in boolean data (False acts as a gap),
* positive values in integer data (-1 acts as a gap), and
* non-NaN values in any other data (NaN acts as a gap).
`**kwargs` will be passed to `Ranges.__init__`."""
if wrapper_kwargs is None:
wrapper_kwargs = {}
ts_pd = to_pd_array(ts)
ts_arr = to_2d_array(ts_pd)
if gap_value is None:
if np.issubdtype(ts_arr.dtype, np.bool_):
gap_value = False
elif np.issubdtype(ts_arr.dtype, np.integer):
gap_value = -1
else:
gap_value = np.nan
records_arr = nb.find_ranges_nb(ts_arr, gap_value)
wrapper = ArrayWrapper.from_obj(ts_pd, **wrapper_kwargs)
return cls(wrapper,
records_arr,
ts=ts_pd if attach_ts else None,
**kwargs)
def from_obj(cls: tp.Type[ArrayWrapperT], obj: tp.ArrayLike, *args,
**kwargs) -> ArrayWrapperT:
"""Derive metadata from an object."""
pd_obj = to_pd_array(obj)
index = index_fns.get_index(pd_obj, 0)
columns = index_fns.get_index(pd_obj, 1)
ndim = pd_obj.ndim
return cls(index, columns, ndim, *args, **kwargs)
def from_ts(cls: tp.Type[DrawdownsT], ts: tp.ArrayLike, idx_field: str = 'end_idx', **kwargs) -> DrawdownsT:
"""Build `Drawdowns` from time series `ts`.
`**kwargs` such as `freq` will be passed to `Drawdowns.__init__`."""
pd_ts = to_pd_array(ts)
records_arr = nb.find_drawdowns_nb(pd_ts.vbt.to_2d_array())
wrapper = ArrayWrapper.from_obj(pd_ts, **kwargs)
return cls(wrapper, records_arr, pd_ts, idx_field=idx_field)
def from_ts(cls: tp.Type[DrawdownsT],
ts: tp.ArrayLike,
attach_ts: bool = True,
wrapper_kwargs: tp.KwargsLike = None,
**kwargs) -> DrawdownsT:
"""Build `Drawdowns` from time series `ts`.
`**kwargs` will be passed to `Drawdowns.__init__`."""
ts_pd = to_pd_array(ts)
records_arr = nb.get_drawdowns_nb(to_2d_array(ts_pd))
wrapper = ArrayWrapper.from_obj(ts_pd, **merge_dicts({},
wrapper_kwargs))
return cls(wrapper,
records_arr,
ts=ts_pd if attach_ts else None,
**kwargs)
def plot_as_markers(
self,
y: tp.Optional[tp.ArrayLike] = None,
**kwargs
) -> tp.Union[tp.BaseFigure, plotting.Scatter]: # pragma: no cover
"""Plot Series as markers.
Args:
y (array_like): Y-axis values to plot markers on.
**kwargs: Keyword arguments passed to `vectorbt.generic.accessors.GenericAccessor.scatterplot`.
## Example
```python-repl
>>> ts = pd.Series([1, 2, 3, 2, 1], index=sig.index)
>>> fig = ts.vbt.lineplot()
>>> sig['b'].vbt.signals.plot_as_entry_markers(y=ts, fig=fig)
>>> (~sig['b']).vbt.signals.plot_as_exit_markers(y=ts, fig=fig)
```
"""
from vectorbt._settings import settings
plotting_cfg = settings['plotting']
if y is None:
y = pd.Series.vbt.empty_like(self._obj, 1)
else:
y = reshape_fns.to_pd_array(y)
return y[self._obj].vbt.scatterplot(**merge_dicts(
dict(trace_kwargs=dict(marker=dict(
symbol='circle',
color=plotting_cfg['contrast_color_schema']['blue'],
size=7,
line=dict(width=1,
color=adjust_lightness(
plotting_cfg['contrast_color_schema']
['blue']))))), kwargs))