def generate_random(cls, shape, n=None, prob=None, seed=None, **kwargs):
"""Generate signals randomly.
If `n` is set, see `vectorbt.signals.nb.generate_rand_nb`.
If `prob` is set, see `vectorbt.signals.nb.generate_rand_by_prob_nb`.
`n` should be either a scalar or an array that will broadcast to the number of columns.
`prob` should be either a single number or an array that will broadcast to match `shape`.
`**kwargs` will be passed to pandas constructor.
## Example
For each column, generate a variable number of signals:
```python-repl
>>> pd.DataFrame.vbt.signals.generate_random((5, 3), n=[0, 1, 2],
... seed=42, index=sig.index, columns=sig.columns)
a b c
2020-01-01 False False True
2020-01-02 False False True
2020-01-03 False False False
2020-01-04 False True False
2020-01-05 False False False
```
For each column and time step, pick a signal with 50% probability:
```python-repl
>>> pd.DataFrame.vbt.signals.generate_random((5, 3), prob=0.5,
... seed=42, index=sig.index, columns=sig.columns)
a b c
2020-01-01 True True True
2020-01-02 False True False
2020-01-03 False False False
2020-01-04 False False True
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 and prob is not None:
raise ValueError("Either n or prob should be set")
if n is not None:
n = np.broadcast_to(n, shape[1])
result = nb.generate_rand_nb(shape, n, seed=seed)
elif prob is not None:
prob = np.broadcast_to(prob, shape)
result = nb.generate_rand_by_prob_nb(shape, prob, flex_2d, seed=seed)
else:
raise ValueError("At least n or prob should be set")
if cls.is_series():
if shape[1] > 1:
raise ValueError("Use DataFrame accessor")
return pd.Series(result[:, 0], **kwargs)
return pd.DataFrame(result, **kwargs)
def generate_random_by_prob(cls, shape, prob, seed=None, **kwargs):
"""See `vectorbt.signals.nb.generate_rand_by_prob_nb`.
`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 and time step, pick a signal with 0.5 probability:
```python-repl
>>> print(pd.DataFrame.vbt.signals.generate_random_by_prob((5, 3), 0.5,
... seed=42, index=sig.index, columns=sig.columns))
a b c
2020-01-01 True True True
2020-01-02 False True False
2020-01-03 False False False
2020-01-04 False False True
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)
probs = np.broadcast_to(prob, shape)
result = nb.generate_rand_by_prob_nb(shape, probs, seed=seed)
if cls.is_series():
return pd.Series(result[:, 0], **kwargs)
return pd.DataFrame(result, **kwargs)
