def generate_exits(self, exit_choice_func_nb, *args, wait=1, wrap_kwargs=None):
"""See `vectorbt.signals.nb.generate_ex_nb`.
## Example
Fill all space after signals in `sig`:
```python-repl
>>> @njit
... def exit_choice_func_nb(from_i, to_i, col, temp_range):
... return temp_range[from_i:to_i]
>>> temp_range = np.arange(sig.shape[0]) # reuse memory
>>> sig.vbt.signals.generate_exits(exit_choice_func_nb, temp_range)
a b c
2020-01-01 False False False
2020-01-02 True True False
2020-01-03 True False False
2020-01-04 True True True
2020-01-05 True False True
```
"""
checks.assert_numba_func(exit_choice_func_nb)
exits = nb.generate_ex_nb(self.to_2d_array(), wait, exit_choice_func_nb, *args)
return self.wrapper.wrap(exits, **merge_dicts({}, wrap_kwargs))
def groupby_apply(self, by, apply_func_nb, *args, on_matrix=False, **kwargs):
"""See `vectorbt.timeseries.nb.groupby_apply_nb` and
`vectorbt.timeseries.nb.groupby_apply_matrix_nb` for `on_matrix=True`.
For `by`, see [pandas.DataFrame.groupby](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html).
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.timeseries.groupby_apply([1, 1, 2, 2, 3],
... mean_nb))
a b c
1 1.5 4.5 1.5
2 3.5 2.5 2.5
3 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.timeseries.groupby_apply([1, 1, 2, 2, 3],
... mean_matrix_nb, on_matrix=True))
a b c
1 2.500000 2.500000 2.500000
2 2.833333 2.833333 2.833333
3 2.333333 2.333333 2.333333
```"""
checks.assert_numba_func(apply_func_nb)
regrouped = self._obj.groupby(by, axis=0, **kwargs)
groups = Dict()
for i, (k, v) in enumerate(regrouped.indices.items()):
groups[i] = np.asarray(v)
if on_matrix:
result = nb.groupby_apply_matrix_nb(self.to_2d_array(), groups, apply_func_nb, *args)
else:
result = nb.groupby_apply_nb(self.to_2d_array(), groups, apply_func_nb, *args)
return self.wrap_array(result, index=list(regrouped.indices.keys()))
def map_reduce_between(self,
other=None,
map_func_nb=None,
map_args=None,
reduce_func_nb=None,
reduce_args=None,
broadcast_kwargs=None):
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Both will be broadcast using `vectorbt.base.reshape_fns.broadcast`
with `broadcast_kwargs`.
Note that `map_args` and `reduce_args` won't be broadcast.
## Example
Get average distance between signals in `sig`:
```python-repl
>>> distance_map_nb = njit(lambda from_i, to_i, col: to_i - from_i)
>>> mean_reduce_nb = njit(lambda col, a: np.nanmean(a))
>>> sig.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb,
... reduce_func_nb=mean_reduce_nb)
a NaN
b 2.0
c 1.0
dtype: float64
```
"""
if broadcast_kwargs is None:
broadcast_kwargs = {}
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if map_args is None:
map_args = ()
if reduce_args is None:
reduce_args = ()
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
map_args, reduce_func_nb,
reduce_args)
if isinstance(self._obj, pd.Series):
return result[0]
return pd.Series(result, index=self.wrapper.columns)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
checks.assert_dtype(other, np.bool)
result = nb.map_reduce_between_two_nb(obj.vbt.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, map_args,
reduce_func_nb, reduce_args)
return obj.vbt.wrapper.wrap_reduced(result)
def rolling_apply(self, window, apply_func_nb, *args, on_matrix=False):
"""See `vectorbt.generic.nb.rolling_apply_nb` and
`vectorbt.generic.nb.rolling_apply_matrix_nb` for `on_matrix=True`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.rolling_apply(3, mean_nb))
a b c
2020-01-01 1.0 5.0 1.000000
2020-01-02 1.5 4.5 1.500000
2020-01-03 2.0 4.0 2.000000
2020-01-04 3.0 3.0 2.333333
2020-01-05 4.0 2.0 2.000000
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.rolling_apply(3, mean_matrix_nb, on_matrix=True))
a b c
2020-01-01 2.333333 2.333333 2.333333
2020-01-02 2.500000 2.500000 2.500000
2020-01-03 2.666667 2.666667 2.666667
2020-01-04 2.777778 2.777778 2.777778
2020-01-05 2.666667 2.666667 2.666667
```"""
checks.assert_numba_func(apply_func_nb)
if on_matrix:
result = nb.rolling_apply_matrix_nb(self.to_2d_array(), window,
apply_func_nb, *args)
else:
result = nb.rolling_apply_nb(self.to_2d_array(), window,
apply_func_nb, *args)
return self.wrap(result)
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 expanding_apply(self, apply_func_nb, *args, on_matrix=False):
"""See `vectorbt.timeseries.nb.expanding_apply_nb` and
`vectorbt.timeseries.nb.expanding_apply_matrix_nb` for `on_matrix=True`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.timeseries.expanding_apply(mean_nb))
a b c
2018-01-01 1.0 5.0 1.0
2018-01-02 1.5 4.5 1.5
2018-01-03 2.0 4.0 2.0
2018-01-04 2.5 3.5 2.0
2018-01-05 3.0 3.0 1.8
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.timeseries.expanding_apply(
... mean_matrix_nb, on_matrix=True))
a b c
2018-01-01 2.333333 2.333333 2.333333
2018-01-02 2.500000 2.500000 2.500000
2018-01-03 2.666667 2.666667 2.666667
2018-01-04 2.666667 2.666667 2.666667
2018-01-05 2.600000 2.600000 2.600000
```"""
checks.assert_numba_func(apply_func_nb)
if on_matrix:
result = nb.expanding_apply_matrix_nb(self.to_2d_array(),
apply_func_nb, *args)
else:
result = nb.expanding_apply_nb(self.to_2d_array(), apply_func_nb,
*args)
return self.wrap(result)
def map_reduce_partitions(self, map_func_nb=None, map_args=None,
reduce_func_nb=None, reduce_args=None):
"""See `vectorbt.signals.nb.map_reduce_partitions_nb`.
## Example
Get average length of each partition in `sig`:
```python-repl
>>> distance_map_nb = njit(lambda from_i, to_i, col: to_i - from_i)
>>> mean_reduce_nb = njit(lambda col, a: np.nanmean(a))
>>> sig.vbt.signals.map_reduce_partitions(
... map_func_nb=distance_map_nb,
... reduce_func_nb=mean_reduce_nb)
a 1.0
b 1.0
c 3.0
dtype: float64
```
"""
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if map_args is None:
map_args = ()
if reduce_args is None:
reduce_args = ()
result = nb.map_reduce_partitions_nb(
self.to_2d_array(),
map_func_nb, map_args,
reduce_func_nb, reduce_args
)
return self.wrapper.wrap_reduced(result)
def reduce(self,
reduce_func_nb,
*args,
idx_arr=None,
to_array=False,
to_idx=False,
idx_labeled=True,
default_val=np.nan,
group_by=None,
**kwargs):
"""Reduce mapped array by column.
If `to_array` is False and `to_idx` is False, see `vectorbt.records.nb.reduce_mapped_nb`.
If `to_array` is False and `to_idx` is True, see `vectorbt.records.nb.reduce_mapped_to_idx_nb`.
If `to_array` is True and `to_idx` is False, see `vectorbt.records.nb.reduce_mapped_to_array_nb`.
If `to_array` is True and `to_idx` is True, see `vectorbt.records.nb.reduce_mapped_to_idx_array_nb`.
If `to_idx` is True, must pass `idx_arr`. Set `idx_labeled` to False to return raw positions instead
of labels. Use `default_val` to set the default value. Set `group_by` to False to disable grouping.
`**kwargs` will be passed to `vectorbt.base.array_wrapper.ArrayWrapper.wrap_reduced`."""
# Perform checks
checks.assert_numba_func(reduce_func_nb)
if idx_arr is None:
if self.idx_arr is None:
if to_idx:
raise ValueError("Must pass idx_arr")
idx_arr = self.idx_arr
# Perform main computation
col_map = self.col_mapper.get_col_map(group_by=group_by)
if not to_array:
if not to_idx:
out = nb.reduce_mapped_nb(self.values, col_map, default_val,
reduce_func_nb, *args)
else:
out = nb.reduce_mapped_to_idx_nb(self.values, col_map, idx_arr,
default_val, reduce_func_nb,
*args)
else:
if not to_idx:
out = nb.reduce_mapped_to_array_nb(self.values, col_map,
default_val, reduce_func_nb,
*args)
else:
out = nb.reduce_mapped_to_idx_array_nb(self.values, col_map,
idx_arr, default_val,
reduce_func_nb, *args)
# Perform post-processing
if to_idx:
nan_mask = np.isnan(out)
if idx_labeled:
out = out.astype(np.object)
out[~nan_mask] = self.wrapper.index[out[~nan_mask].astype(
np.int_)]
else:
out[nan_mask] = -1
out = out.astype(np.int_)
return self.wrapper.wrap_reduced(out, group_by=group_by, **kwargs)
def generate(cls, shape, choice_func_nb, *args, **kwargs):
"""See `vectorbt.signals.nb.generate_nb`.
`**kwargs` will be passed to pandas constructor.
Example:
Generate random signals manually:
```python-repl
>>> @njit
... def choice_func_nb(col, from_i, to_i):
... return col + from_i
>>> pd.DataFrame.vbt.signals.generate((5, 3),
... choice_func_nb, index=sig.index, columns=sig.columns)
a b c
2020-01-01 True False False
2020-01-02 False True False
2020-01-03 False False True
2020-01-04 False False False
2020-01-05 False False False
```"""
checks.assert_numba_func(choice_func_nb)
if not isinstance(shape, tuple):
shape = (shape, 1)
elif isinstance(shape, tuple) and len(shape) == 1:
shape = (shape[0], 1)
result = nb.generate_nb(shape, choice_func_nb, *args)
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 apply_mapper(self, map_func_nb, *args):
"""Apply `map_func_nb` on each position using `vectorbt.portfolio.nb.map_positions_nb`
and filter the results with `pos_filters`.
This way, all time series created on top of positions will be automatically filtered."""
checks.assert_numba_func(map_func_nb)
# Apply map
result = nb.map_positions_nb(self.portfolio.shares.vbt.to_2d_array(),
self.pos_status, map_func_nb, *args)
result = self.wrap_array(result)
# Apply passed filters
for pos_filter in self.pos_filters:
if isfunction(pos_filter):
filter_func_nb = pos_filter
args = ()
else:
filter_func_nb = pos_filter[0]
if len(pos_filter) > 1:
args = pos_filter[1:]
else:
args = ()
checks.assert_numba_func(filter_func_nb)
result = result.vbt.timeseries.filter(filter_func_nb, *args)
return result
def generate(cls, shape, choice_func_nb, *args, **kwargs):
"""See `vectorbt.signals.nb.generate_nb`.
`**kwargs` will be passed to pandas constructor.
Example:
Generate random signals manually:
```python-repl
>>> @njit
... def choice_func_nb(col, from_i, to_i):
... return np.random.choice(np.arange(from_i, to_i+1), replace=False)
>>> print(pd.DataFrame.vbt.signals.generate((5, 3),
... choice_func_nb, index=index, columns=columns))
a b c
2018-01-01 False False False
2018-01-02 False False False
2018-01-03 False True False
2018-01-04 True False False
2018-01-05 False False True
```"""
checks.assert_numba_func(choice_func_nb)
if not isinstance(shape, tuple):
shape = (shape, 1)
elif isinstance(shape, tuple) and len(shape) == 1:
shape = (shape[0], 1)
result = nb.generate_nb(shape, choice_func_nb, *args)
if cls.is_series():
return pd.Series(result[:, 0], **kwargs)
return pd.DataFrame(result, **kwargs)
def groupby_apply(self, by, apply_func_nb, *args, on_matrix=False, **kwargs):
"""See `vectorbt.generic.nb.groupby_apply_nb` and
`vectorbt.generic.nb.groupby_apply_matrix_nb` for `on_matrix=True`.
For `by`, see `pd.DataFrame.groupby`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> df.vbt.groupby_apply([1, 1, 2, 2, 3], mean_nb)
a b c
1 1.5 4.5 1.5
2 3.5 2.5 2.5
3 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> df.vbt.groupby_apply([1, 1, 2, 2, 3], mean_matrix_nb, on_matrix=True)
a b c
1 2.500000 2.500000 2.500000
2 2.833333 2.833333 2.833333
3 2.333333 2.333333 2.333333
```"""
checks.assert_numba_func(apply_func_nb)
regrouped = self._obj.groupby(by, axis=0, **kwargs)
groups = Dict()
for i, (k, v) in enumerate(regrouped.indices.items()):
groups[i] = np.asarray(v)
if on_matrix:
result = nb.groupby_apply_matrix_nb(self.to_2d_array(), groups, apply_func_nb, *args)
else:
result = nb.groupby_apply_nb(self.to_2d_array(), groups, apply_func_nb, *args)
return self.wrap_reduced(result, index=list(regrouped.indices.keys()))
def map(self,
map_func_nb: tp.RecordMapFunc,
*args,
idx_field: tp.Optional[str] = None,
value_map: tp.Optional[tp.ValueMapLike] = None,
group_by: tp.GroupByLike = None,
**kwargs) -> MappedArray:
"""Map each record to a scalar value. Returns mapped array.
See `vectorbt.records.nb.map_records_nb`."""
checks.assert_numba_func(map_func_nb)
mapped_arr = nb.map_records_nb(self.values, map_func_nb, *args)
if idx_field is None:
idx_field = self.idx_field
if idx_field is not None:
idx_arr = self.values[idx_field]
else:
idx_arr = None
return MappedArray(self.wrapper,
mapped_arr,
self.values['col'],
id_arr=self.values['id'],
idx_arr=idx_arr,
value_map=value_map,
**kwargs).regroup(group_by)
def map(self,
map_func_nb,
*args,
idx_field=None,
value_map=None,
group_by=None,
**kwargs):
"""Map each record to a scalar value. Returns mapped array.
See `vectorbt.records.nb.map_records_nb`."""
checks.assert_numba_func(map_func_nb)
mapped_arr = nb.map_records_nb(self.values, map_func_nb, *args)
if idx_field is None:
idx_field = self.idx_field
if idx_field is not None:
idx_arr = self.values[idx_field]
else:
idx_arr = None
return MappedArray(self.wrapper,
mapped_arr,
self.values['col'],
id_arr=self.values['id'],
idx_arr=idx_arr,
value_map=value_map,
**kwargs).regroup(group_by)
def reduce(self,
reduce_func_nb: tp.ReduceFunc,
*args,
idx_arr: tp.Optional[tp.Array1d] = None,
returns_array: bool = False,
returns_idx: bool = False,
to_index: bool = True,
fill_value: tp.Scalar = np.nan,
group_by: tp.GroupByLike = None,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeriesFrame:
"""Reduce mapped array by column/group.
If `returns_array` is False and `returns_idx` is False, see `vectorbt.records.nb.reduce_mapped_nb`.
If `returns_array` is False and `returns_idx` is True, see `vectorbt.records.nb.reduce_mapped_to_idx_nb`.
If `returns_array` is True and `returns_idx` is False, see `vectorbt.records.nb.reduce_mapped_to_array_nb`.
If `returns_array` is True and `returns_idx` is True, see `vectorbt.records.nb.reduce_mapped_to_idx_array_nb`.
If `returns_idx` is True, must pass `idx_arr`. Set `to_index` to False to return raw positions instead
of labels. Use `fill_value` to set the default value. Set `group_by` to False to disable grouping.
"""
# Perform checks
checks.assert_numba_func(reduce_func_nb)
if idx_arr is None:
if self.idx_arr is None:
if returns_idx:
raise ValueError("Must pass idx_arr")
idx_arr = self.idx_arr
# Perform main computation
col_map = self.col_mapper.get_col_map(group_by=group_by)
if not returns_array:
if not returns_idx:
out = nb.reduce_mapped_nb(self.values, col_map, fill_value,
reduce_func_nb, *args)
else:
out = nb.reduce_mapped_to_idx_nb(self.values, col_map, idx_arr,
fill_value, reduce_func_nb,
*args)
else:
if not returns_idx:
out = nb.reduce_mapped_to_array_nb(self.values, col_map,
fill_value, reduce_func_nb,
*args)
else:
out = nb.reduce_mapped_to_idx_array_nb(self.values, col_map,
idx_arr, fill_value,
reduce_func_nb, *args)
# Perform post-processing
wrap_kwargs = merge_dicts(
dict(name_or_index='reduce' if not returns_array else None,
to_index=returns_idx and to_index,
fillna=-1 if returns_idx else None,
dtype=np.int_ if returns_idx else None), wrap_kwargs)
return self.wrapper.wrap_reduced(out, group_by=group_by, **wrap_kwargs)
def test_assert_numba_func(self):
def test_func(x):
return x
@njit
def test_func_nb(x):
return x
checks.assert_numba_func(test_func_nb)
with pytest.raises(Exception) as e_info:
checks.assert_numba_func(test_func)
def map(self,
map_func_nb: tp.RecordMapFunc, *args,
dtype: tp.Optional[tp.DTypeLike] = None,
**kwargs) -> MappedArray:
"""Map each record to a scalar value. Returns mapped array.
See `vectorbt.records.nb.map_records_nb`.
`**kwargs` are passed to `Records.map_array`."""
checks.assert_numba_func(map_func_nb)
mapped_arr = nb.map_records_nb(self.values, map_func_nb, *args)
mapped_arr = np.asarray(mapped_arr, dtype=dtype)
return self.map_array(mapped_arr, **kwargs)
def reduce_records(self, reduce_func_nb, *args):
"""Perform a reducing operation over the records of each column.
See `vectorbt.portfolio.nb.reduce_records_nb`."""
checks.assert_numba_func(reduce_func_nb)
return self.wrapper.wrap_reduced(
nb.reduce_records_nb(
self._records,
len(self.wrapper.columns),
self.col_field,
reduce_func_nb,
*args))
def generate_iteratively(cls, shape, choice_func1_nb, choice_func2_nb,
*args, **kwargs):
"""See `vectorbt.signals.nb.generate_iteratively_nb`.
`**kwargs` will be passed to pandas constructor.
Example:
Generate entry and exit signals one after another:
```python-repl
>>> @njit
... def choice_func1_nb(col, from_i, to_i):
... return np.array([from_i])
>>> @njit
... def choice_func2_nb(col, from_i, to_i):
... return np.array([from_i])
>>> entries, exits = pd.DataFrame.vbt.signals.generate_iteratively(
... (5, 3), choice_func1_nb, choice_func2_nb,
... index=sig.index, columns=sig.columns)
>>> print(entries)
a b c
2020-01-01 True True True
2020-01-02 False False False
2020-01-03 True True True
2020-01-04 False False False
2020-01-05 True True True
>>> print(exits)
a b c
2020-01-01 False False False
2020-01-02 True True True
2020-01-03 False False False
2020-01-04 True True True
2020-01-05 False False False
```"""
checks.assert_numba_func(choice_func1_nb)
checks.assert_numba_func(choice_func2_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_iteratively_nb(shape, choice_func1_nb,
choice_func2_nb, *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 map_reduce_between(self,
*args,
other=None,
map_func_nb=None,
reduce_func_nb=None,
broadcast_kwargs={}):
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Arguments will be broadcasted using `vectorbt.utils.reshape_fns.broadcast`
with `broadcast_kwargs`.
Example:
Get maximum distance between signals in `signals`:
```python-repl
>>> distance_map_nb = njit(lambda col, prev_i, next_i: next_i - prev_i)
>>> max_reduce_nb = njit(lambda col, a: np.nanmax(a))
>>> print(signals.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb, reduce_func_nb=max_reduce_nb))
a 3.0
b 3.0
c NaN
dtype: float64
```"""
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
reduce_func_nb, *args)
if isinstance(self._obj, pd.Series):
return result[0]
return pd.Series(result, index=self.columns)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
other.vbt.signals.validate()
result = nb.map_reduce_between_two_nb(self.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, reduce_func_nb,
*args)
if isinstance(obj, pd.Series):
return result[0]
return pd.Series(result, index=obj.vbt.columns)
def map_records_to_matrix(self, map_func_nb, *args):
"""Map each record to a value that is then stored in a matrix.
See `vectorbt.portfolio.nb.map_records_to_matrix_nb`."""
checks.assert_numba_func(map_func_nb)
return self.wrapper.wrap(
nb.map_records_to_matrix_nb(
self._records,
(len(self.wrapper.index), len(self.wrapper.columns)),
self.col_field,
self.row_field,
map_func_nb,
*args))
def test_assert_numba_func(self):
def test_func(x):
return x
@njit
def test_func_nb(x):
return x
checks.assert_numba_func(test_func_nb)
try:
checks.assert_numba_func(test_func)
raise Exception
except:
pass
def resample_apply(self,
freq,
apply_func_nb,
*args,
on_matrix=False,
**kwargs):
"""See `vectorbt.timeseries.nb.groupby_apply_nb` and
`vectorbt.timeseries.nb.groupby_apply_matrix_nb` for `on_matrix=True`.
For `freq`, see `pandas.DataFrame.resample`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.timeseries.resample_apply('2d', mean_nb))
a b c
2018-01-01 1.5 4.5 1.5
2018-01-03 3.5 2.5 2.5
2018-01-05 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.timeseries.resample_apply('2d',
... mean_matrix_nb, on_matrix=True))
a b c
2018-01-01 2.500000 2.500000 2.500000
2018-01-03 2.833333 2.833333 2.833333
2018-01-05 2.333333 2.333333 2.333333
```"""
checks.assert_numba_func(apply_func_nb)
resampled = self._obj.resample(freq, axis=0, **kwargs)
groups = Dict()
for i, (k, v) in enumerate(resampled.indices.items()):
groups[i] = np.asarray(v)
if on_matrix:
result = nb.groupby_apply_matrix_nb(self.to_2d_array(), groups,
apply_func_nb, *args)
else:
result = nb.groupby_apply_nb(self.to_2d_array(), groups,
apply_func_nb, *args)
result_obj = self.wrap_array(result,
index=list(resampled.indices.keys()))
resampled_arr = np.full(
(resampled.ngroups, self.to_2d_array().shape[1]), np.nan)
resampled_obj = self.wrap_array(resampled_arr,
index=pd.Index(list(
resampled.groups.keys()),
freq=freq))
resampled_obj.loc[result_obj.index] = result_obj.values
return resampled_obj
def map_reduce_between(self,
*args,
other=None,
map_func_nb=None,
reduce_func_nb=None,
broadcast_kwargs={}):
"""See `vectorbt.signals.nb.map_reduce_between_nb`.
If `other` specified, see `vectorbt.signals.nb.map_reduce_between_two_nb`.
Arguments will be broadcasted using `vectorbt.base.reshape_fns.broadcast`
with `broadcast_kwargs`.
Example:
Get average distance between signals in `sig`:
```python-repl
>>> distance_map_nb = njit(lambda col, from_i, to_i: to_i - from_i)
>>> mean_reduce_nb = njit(lambda col, a: np.nanmean(a))
>>> print(sig.vbt.signals.map_reduce_between(
... map_func_nb=distance_map_nb, reduce_func_nb=mean_reduce_nb))
a NaN
b 2.0
c 1.0
dtype: float64
```"""
checks.assert_not_none(map_func_nb)
checks.assert_not_none(reduce_func_nb)
checks.assert_numba_func(map_func_nb)
checks.assert_numba_func(reduce_func_nb)
if other is None:
# One input array
result = nb.map_reduce_between_nb(self.to_2d_array(), map_func_nb,
reduce_func_nb, *args)
if isinstance(self._obj, pd.Series):
return result[0]
return pd.Series(result, index=self.columns)
else:
# Two input arrays
obj, other = reshape_fns.broadcast(self._obj, other,
**broadcast_kwargs)
checks.assert_dtype(other, np.bool_)
result = nb.map_reduce_between_two_nb(self.to_2d_array(),
other.vbt.to_2d_array(),
map_func_nb, reduce_func_nb,
*args)
return self.wrap_reduced(result)
def map(self, map_func_nb, *args, idx_arr=None):
"""Map each record to a scalar value. Returns `MappedArray`.
See `vectorbt.records.nb.map_records_nb`."""
checks.assert_numba_func(map_func_nb)
mapped_arr = nb.map_records_nb(self.records_arr, map_func_nb, *args)
if idx_arr is None:
if self.idx_field is not None:
idx_arr = self.records_arr[self.idx_field]
else:
idx_arr = None
return MappedArray(mapped_arr,
self.records_arr['col'],
self.wrapper,
idx_arr=idx_arr)
def reduce_to_array(self, reduce_func_nb, *args, **kwargs):
"""See `vectorbt.generic.nb.reduce_to_array_nb`.
`**kwargs` will be passed to `vectorbt.base.array_wrapper.ArrayWrapper.wrap_reduced`.
Example:
```python-repl
>>> min_max_nb = njit(lambda col, a: np.array([np.nanmin(a), np.nanmax(a)]))
>>> df.vbt.reduce_to_array(min_max_nb, index=['min', 'max'])
a b c
min 1.0 1.0 1.0
max 5.0 5.0 3.0
```"""
checks.assert_numba_func(reduce_func_nb)
out = nb.reduce_to_array_nb(self.to_2d_array(), reduce_func_nb, *args)
return self.wrap_reduced(out, **kwargs)
def reduce_to_array(self, reduce_func_nb, *args, **kwargs):
"""See `vectorbt.timeseries.nb.reduce_to_array_nb`.
`**kwargs` will be passed to `TimeSeries_Accessor.wrap_reduced_array`.
Example:
```python-repl
>>> min_max_nb = njit(lambda col, a: np.array([np.nanmin(a), np.nanmax(a)]))
>>> print(df.vbt.timeseries.reduce_to_array(min_max_nb, index=['min', 'max']))
a b c
min 1.0 1.0 1.0
max 5.0 5.0 3.0
```"""
checks.assert_numba_func(reduce_func_nb)
result = nb.reduce_to_array_nb(self.to_2d_array(), reduce_func_nb, *args)
return self.wrap_reduced_array(result, **kwargs)
def reduce(self, reduce_func_nb, *args, **kwargs):
"""See `vectorbt.timeseries.nb.reduce_nb`.
`**kwargs` will be passed to `TimeSeries_Accessor.wrap_reduced`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, a: np.nanmean(a))
>>> print(df.vbt.timeseries.reduce(mean_nb))
a 3.0
b 3.0
c 1.8
dtype: float64
```"""
checks.assert_numba_func(reduce_func_nb)
result = nb.reduce_nb(self.to_2d_array(), reduce_func_nb, *args)
return self.wrap_reduced(result, **kwargs)
def filter(self, filter_func_nb, *args):
"""See `vectorbt.timeseries.nb.filter_nb`.
Example:
```python-repl
>>> greater_nb = njit(lambda col, i, a: a > 2)
>>> print(df.vbt.timeseries.filter(greater_nb))
a b c
2018-01-01 NaN 5.0 NaN
2018-01-02 NaN 4.0 NaN
2018-01-03 3.0 3.0 3.0
2018-01-04 4.0 NaN NaN
2018-01-05 5.0 NaN NaN
```"""
checks.assert_numba_func(filter_func_nb)
result = nb.filter_nb(self.to_2d_array(), filter_func_nb, *args)
return self.wrap(result)
def applymap(self, apply_func_nb, *args):
"""See `vectorbt.timeseries.nb.applymap_nb`.
Example:
```python-repl
>>> multiply_nb = njit(lambda col, i, a: a ** 2)
>>> print(df.vbt.timeseries.applymap(multiply_nb))
a b c
2018-01-01 1.0 25.0 1.0
2018-01-02 4.0 16.0 4.0
2018-01-03 9.0 9.0 9.0
2018-01-04 16.0 4.0 4.0
2018-01-05 25.0 1.0 1.0
```"""
checks.assert_numba_func(apply_func_nb)
result = nb.applymap_nb(self.to_2d_array(), apply_func_nb, *args)
return self.wrap(result)
