def generate_both(cls, shape, entry_choice_func_nb, exit_choice_func_nb,
entry_args, exit_args, **kwargs):
"""See `vectorbt.signals.nb.generate_enex_nb`.
`**kwargs` will be passed to pandas constructor.
Example:
Generate entry and exit signals one after another:
```python-repl
>>> @njit
... def entry_choice_func_nb(col, from_i, to_i, wait1):
... next_pos = col + from_i + wait1
... if next_pos < to_i:
... return np.array([next_pos])
... return np.empty(0, dtype=np.int_)
>>> @njit
... def exit_choice_func_nb(col, from_i, to_i, wait2):
... next_pos = col + from_i + wait2
... if next_pos < to_i:
... return np.array([next_pos])
... return np.empty(0, dtype=np.int_)
>>> en, ex = pd.DataFrame.vbt.signals.generate_both(
... (5, 3), entry_choice_func_nb, exit_choice_func_nb, (0,), (1,),
... index=sig.index, columns=sig.columns)
>>> en
a b c
2020-01-01 True False False
2020-01-02 False True False
2020-01-03 False False True
2020-01-04 True False False
2020-01-05 False False False
>>> ex
a b c
2020-01-01 False False False
2020-01-02 False False False
2020-01-03 True False False
2020-01-04 False False False
2020-01-05 False True False
```"""
checks.assert_numba_func(entry_choice_func_nb)
checks.assert_numba_func(exit_choice_func_nb)
if not isinstance(shape, tuple):
shape = (shape, 1)
elif isinstance(shape, tuple) and len(shape) == 1:
shape = (shape[0], 1)
result1, result2 = nb.generate_enex_nb(
shape,
entry_choice_func_nb,
exit_choice_func_nb,
entry_args,
exit_args
)
if cls.is_series():
if shape[1] > 1:
raise ValueError("Use DataFrame accessor")
return pd.Series(result1[:, 0], **kwargs), pd.Series(result2[:, 0], **kwargs)
return pd.DataFrame(result1, **kwargs), pd.DataFrame(result2, **kwargs)
def apply_nb(i, shape, entry_wait, exit_wait, entry_input_list,
exit_input_list, entry_in_output_tuples,
exit_in_output_tuples, entry_param_tuples,
exit_param_tuples, entry_args, exit_args):
return generate_enex_nb(
shape, entry_wait, exit_wait, entry_choice_func,
(*entry_input_list, *entry_in_output_tuples[i],
*entry_param_tuples[i], *entry_args), exit_choice_func,
(*exit_input_list, *exit_in_output_tuples[i],
*exit_param_tuples[i], *exit_args))
def generate_both(cls, shape, entry_choice_func_nb, exit_choice_func_nb,
entry_args=None, exit_args=None, entry_wait=1, exit_wait=1, **kwargs):
"""See `vectorbt.signals.nb.generate_enex_nb`.
`**kwargs` will be passed to pandas constructor.
## Example
Generate entry and exit signals one after another. Each column increment
the number of ticks to wait before placing the exit signal.
```python-repl
>>> @njit
... def entry_choice_func_nb(from_i, to_i, col, temp_idx_arr):
... temp_idx_arr[0] = from_i
... return temp_idx_arr[:1] # array with one signal
>>> @njit
... def exit_choice_func_nb(from_i, to_i, col, temp_idx_arr):
... wait = col
... temp_idx_arr[0] = from_i + wait
... if temp_idx_arr[0] < to_i:
... return temp_idx_arr[:1] # array with one signal
... return temp_idx_arr[:0] # empty array
>>> temp_idx_arr = np.empty((1,), dtype=np.int_) # reuse memory
>>> en, ex = pd.DataFrame.vbt.signals.generate_both(
... (5, 3), entry_choice_func_nb, exit_choice_func_nb,
... entry_args=(temp_idx_arr,), exit_args=(temp_idx_arr,),
... index=sig.index, columns=sig.columns)
>>> en
a b c
2020-01-01 True True True
2020-01-02 False False False
2020-01-03 True False False
2020-01-04 False True False
2020-01-05 True False True
>>> ex
a b c
2020-01-01 False False False
2020-01-02 True False False
2020-01-03 False True False
2020-01-04 True False True
2020-01-05 False False False
```
"""
checks.assert_numba_func(entry_choice_func_nb)
checks.assert_numba_func(exit_choice_func_nb)
if entry_args is None:
entry_args = ()
if exit_args is None:
exit_args = ()
if not isinstance(shape, tuple):
shape = (shape, 1)
elif isinstance(shape, tuple) and len(shape) == 1:
shape = (shape[0], 1)
result1, result2 = nb.generate_enex_nb(
shape,
entry_wait, exit_wait,
entry_choice_func_nb, entry_args,
exit_choice_func_nb, exit_args
)
if cls.is_series():
if shape[1] > 1:
raise ValueError("Use DataFrame accessor")
return pd.Series(result1[:, 0], **kwargs), pd.Series(result2[:, 0], **kwargs)
return pd.DataFrame(result1, **kwargs), pd.DataFrame(result2, **kwargs)
