def generate_random_both(cls, shape, n=None, entry_prob=None, exit_prob=None, seed=None,
entry_wait=1, exit_wait=1, **kwargs):
"""Generate entry and exit signals randomly and iteratively.
If `n` is set, see `vectorbt.signals.nb.generate_rand_enex_nb`.
If `entry_prob` and `exit_prob` are set, see `vectorbt.signals.nb.generate_rand_enex_by_prob_nb`.
For arguments, see `SignalsAccessor.generate_random`.
## Example
For each column, generate two entries and exits randomly:
```python-repl
>>> en, ex = pd.DataFrame.vbt.signals.generate_random_both(
... (5, 3), n=2, seed=42, 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 True False
2020-01-04 False False True
2020-01-05 False False False
>>> ex
a b c
2020-01-01 False False False
2020-01-02 True True True
2020-01-03 False False False
2020-01-04 False True False
2020-01-05 True False True
```
For each column and time step, pick entry with 50% probability and exit right after:
```python-repl
>>> en, ex = pd.DataFrame.vbt.signals.generate_random_both(
... (5, 3), entry_prob=0.5, exit_prob=1.,
... seed=42, 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 False False False
2020-01-04 False False True
2020-01-05 True False False
>>> ex
a b c
2020-01-01 False False False
2020-01-02 True True False
2020-01-03 False False True
2020-01-04 False True False
2020-01-05 True False True
```
"""
flex_2d = True
if not isinstance(shape, tuple):
flex_2d = False
shape = (shape, 1)
elif isinstance(shape, tuple) and len(shape) == 1:
flex_2d = False
shape = (shape[0], 1)
if n is not None:
n = np.broadcast_to(n, shape[1])
entries, exits = nb.generate_rand_enex_nb(shape, n, entry_wait, exit_wait, seed=seed)
elif entry_prob is not None and exit_prob is not None:
entry_prob = np.broadcast_to(entry_prob, shape)
exit_prob = np.broadcast_to(exit_prob, shape)
entries, exits = nb.generate_rand_enex_by_prob_nb(
shape, entry_prob, exit_prob, entry_wait, exit_wait, flex_2d, seed=seed)
else:
raise ValueError("At least n, or entry_prob and exit_prob should be set")
if cls.is_series():
if shape[1] > 1:
raise ValueError("Use DataFrame accessor")
return pd.Series(entries[:, 0], **kwargs), pd.Series(exits[:, 0], **kwargs)
return pd.DataFrame(entries, **kwargs), pd.DataFrame(exits, **kwargs)
def generate_random_both(cls, shape, n=None, entry_prob=None, exit_prob=None, seed=None, **kwargs):
"""Generate entry and exit signals randomly and iteratively.
If `n` is set, see `vectorbt.signals.nb.generate_rand_enex_nb`.
If `prob` is set, see `vectorbt.signals.nb.generate_rand_enex_by_prob_nb`.
`entry_prob` and `exit_prob` must be either a single number or an array that will be
broadcast to match `shape`. `**kwargs` will be passed to pandas constructor.
Example:
For each column, generate two entries and exits randomly:
```python-repl
>>> en, ex = pd.DataFrame.vbt.signals.generate_random_both(
... (5, 3), n=2, seed=42, 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 True False
2020-01-04 False False True
2020-01-05 False False False
>>> ex
a b c
2020-01-01 False False False
2020-01-02 True True True
2020-01-03 False False False
2020-01-04 False True False
2020-01-05 True False True
```
For each column and time step, pick entry with 50% probability and exit right after:
```python-repl
>>> en, ex = pd.DataFrame.vbt.signals.generate_random_both(
... (5, 3), entry_prob=0.5, exit_prob=1.,
... seed=42, index=sig.index, columns=sig.columns)
>>> en
a b c
2020-01-01 True True False
2020-01-02 False False True
2020-01-03 False True False
2020-01-04 True False True
2020-01-05 False False False
>>> ex
a b c
2020-01-01 False False False
2020-01-02 True True False
2020-01-03 False False True
2020-01-04 False True False
2020-01-05 True False True
```"""
if not isinstance(shape, tuple):
shape = (shape, 1)
elif isinstance(shape, tuple) and len(shape) == 1:
shape = (shape[0], 1)
if n is not None:
entries, exits = nb.generate_rand_enex_nb(shape, n, seed=seed)
elif entry_prob is not None and exit_prob is not None:
entry_prob = np.broadcast_to(entry_prob, shape)
exit_prob = np.broadcast_to(exit_prob, shape)
entries, exits = nb.generate_rand_enex_by_prob_nb(shape, entry_prob, exit_prob, seed=seed)
else:
raise ValueError("At least n, or entry_prob and exit_prob must be set")
if cls.is_series():
if shape[1] > 1:
raise ValueError("Use DataFrame accessor")
return pd.Series(entries[:, 0], **kwargs), pd.Series(exits[:, 0], **kwargs)
return pd.DataFrame(entries, **kwargs), pd.DataFrame(exits, **kwargs)