def generate_stop_loss(self,
ts,
stops,
trailing=False,
relative=True,
as_columns=None,
broadcast_kwargs={}):
"""See `vectorbt.signals.nb.generate_stop_loss_nb`.
Arguments will be broadcasted using `vectorbt.utils.reshape_fns.broadcast`
with `broadcast_kwargs`. Argument `stops` can be either a single number, an array of
numbers, or a 3D array, where each matrix corresponds to a single configuration.
Use `as_columns` as a top-level column level.
Example:
For each entry in `signals`, set stop loss for 10% and 20% below the price `ts`:
```python-repl
>>> print(signals.vbt.signals.generate_stop_loss(ts, [0.1, 0.2]))
stop_loss 0.1 0.2
a b c a b c
2018-01-01 False False False False False False
2018-01-02 False True False False False False
2018-01-03 False False False False False False
2018-01-04 False True True False True True
2018-01-05 False False False False False False
```"""
entries = self._obj
checks.assert_type(ts, (pd.Series, pd.DataFrame))
entries, ts = reshape_fns.broadcast(entries,
ts,
**broadcast_kwargs,
writeable=True)
stops = reshape_fns.broadcast_to_array_of(stops,
entries.vbt.to_2d_array())
exits = nb.generate_stop_loss_nb(entries.vbt.to_2d_array(),
ts.vbt.to_2d_array(), stops, trailing,
relative)
# Build column hierarchy
if as_columns is not None:
param_columns = as_columns
else:
name = 'trail_stop' if trailing else 'stop_loss'
param_columns = index_fns.index_from_values(stops, name=name)
columns = index_fns.combine_indexes(param_columns, entries.vbt.columns)
return entries.vbt.wrap_array(exits, columns=columns)
def build_column_hierarchy(param_list, level_names, ts_columns):
"""For each parameter in `param_list`, create a new column level with parameter values.
Combine this level with columns `ts_columns` using Cartesian product."""
checks.assert_same_shape(param_list, level_names, axis=0)
param_indexes = [
index_fns.index_from_values(param_list[i], name=level_names[i])
for i in range(len(param_list))
]
param_columns = None
for param_index in param_indexes:
if param_columns is None:
param_columns = param_index
else:
param_columns = index_fns.stack_indexes(param_columns, param_index)
if param_columns is not None:
return index_fns.combine_indexes(param_columns, ts_columns)
return ts_columns
def compare(obj,
other,
compare_func,
multiple=False,
name=None,
as_columns=None,
**kwargs):
"""Compares `obj` to `other` to generate signals.
Both will be broadcasted together. Set `multiple` to `True` to compare with multiple arguments. In this case,
a new column level will be created with the name `name`.
See `vectorbt.utils.accessors.Base_Accessor.combine_with`."""
if multiple:
if as_columns is None:
as_columns = index_fns.index_from_values(other, name=name)
return obj.vbt.combine_with_multiple(other,
combine_func=compare_func,
as_columns=as_columns,
concat=True,
**kwargs)
return obj.vbt.combine_with(other, combine_func=compare_func, **kwargs)