def generate_stop_loss_exits(self,
ts,
stops,
trailing=False,
first=True,
keys=None,
broadcast_kwargs={}):
"""See `vectorbt.signals.nb.generate_stop_loss_exits_nb`.
Arguments will be broadcasted using `vectorbt.base.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 `keys` as the outermost level.
Example:
For each entry in `sig`, set stop loss for 10% and 20% below the entry price:
```python-repl
>>> ts = pd.Series([1, 2, 3, 2, 1])
>>> print(sig.vbt.signals.generate_stop_loss_exits(ts, [0.1, 0.5]))
stop_loss 0.1 0.5
a b c a b c
2020-01-01 False False False False False False
2020-01-02 False False False False False False
2020-01-03 False False False False False False
2020-01-04 False True True False False False
2020-01-05 False False False False False True
>>> print(sig.vbt.signals.generate_stop_loss_exits(ts, [0.1, 0.5], trailing=True))
trail_stop 0.1 0.5
a b c a b c
2020-01-01 False False False False False False
2020-01-02 False False False False False False
2020-01-03 False False False False False False
2020-01-04 True True True False False False
2020-01-05 False False False True False True
```"""
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_exits_nb(entries.vbt.to_2d_array(),
ts.vbt.to_2d_array(),
stops,
trailing=trailing,
first=first)
# Build column hierarchy
if keys is not None:
param_columns = keys
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(exits, columns=columns)
def generate_take_profit_exits(self, ts, stops, first=True, iteratively=False, keys=None, broadcast_kwargs={}):
"""Generate take profit exits.
See `vectorbt.signals.nb.generate_tp_ex_iter_nb` if `iteratively` is `True`, otherwise see
`vectorbt.signals.nb.generate_tp_ex_nb`.
Arguments will be broadcasted using `vectorbt.base.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 `keys` as the outermost level.
Example:
For each entry in `sig`, set take profit for 10% and 20% above the entry price:
```python-repl
>>> ts = pd.Series([1, 2, 3, 4, 5])
>>> sig.vbt.signals.generate_take_profit_exits(ts, [0.1, 0.5])
take_profit 0.1 0.5
a b c a b c
2020-01-01 False False False False False False
2020-01-02 True True False True True False
2020-01-03 False False False False False False
2020-01-04 False True True False False False
2020-01-05 False False False False False True
```"""
entries = self._obj
checks.assert_type(ts, (pd.Series, pd.DataFrame))
broadcast_kwargs = merge_kwargs(dict(require_kwargs=dict(requirements='W')), broadcast_kwargs)
entries, ts = reshape_fns.broadcast(entries, ts, **broadcast_kwargs)
stops = reshape_fns.broadcast_to_array_of(stops, entries.vbt.to_2d_array())
# Build column hierarchy
if keys is not None:
param_columns = keys
else:
param_columns = index_fns.index_from_values(stops, name='take_profit')
columns = index_fns.combine_indexes(param_columns, entries.vbt.columns)
# Perform generation
if iteratively:
new_entries, exits = nb.generate_tp_ex_iter_nb(
entries.vbt.to_2d_array(),
ts.vbt.to_2d_array(),
stops)
return entries.vbt.wrap(new_entries, columns=columns), entries.vbt.wrap(exits, columns=columns)
else:
exits = nb.generate_tp_ex_nb(
entries.vbt.to_2d_array(),
ts.vbt.to_2d_array(),
stops,
first=first)
return entries.vbt.wrap(exits, columns=columns)