def __init__(self, index, columns, ndim, freq=None, column_only_select=None,
group_select=None, grouped_ndim=None, **kwargs):
config = dict(
index=index,
columns=columns,
ndim=ndim,
freq=freq,
column_only_select=column_only_select,
group_select=group_select,
grouped_ndim=grouped_ndim,
)
checks.assert_not_none(index)
checks.assert_not_none(columns)
checks.assert_not_none(ndim)
if not isinstance(index, pd.Index):
index = pd.Index(index)
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
self._index = index
self._columns = columns
self._ndim = ndim
self._freq = freq
self._column_only_select = column_only_select
self._group_select = group_select
self._grouper = ColumnGrouper(columns, **kwargs)
self._grouped_ndim = grouped_ndim
PandasIndexer.__init__(self)
Configured.__init__(self, **merge_dicts(config, self._grouper._config))
def __init__(self, wrapper, mapped_arr, col_arr, idx_arr=None):
Configured.__init__(
self,
wrapper=wrapper,
mapped_arr=mapped_arr,
col_arr=col_arr,
idx_arr=idx_arr
)
checks.assert_type(wrapper, ArrayWrapper)
if not isinstance(mapped_arr, np.ndarray):
mapped_arr = np.asarray(mapped_arr)
if not isinstance(col_arr, np.ndarray):
col_arr = np.asarray(col_arr)
checks.assert_shape_equal(mapped_arr, col_arr, axis=0)
if idx_arr is not None:
if not isinstance(idx_arr, np.ndarray):
idx_arr = np.asarray(idx_arr)
checks.assert_shape_equal(mapped_arr, idx_arr, axis=0)
self._wrapper = wrapper
self._mapped_arr = mapped_arr
self._col_arr = col_arr
self._idx_arr = idx_arr
PandasIndexer.__init__(self, _mapped_array_indexing_func)
def __init__(self, wrapper: ArrayWrapper, **kwargs) -> None:
checks.assert_instance_of(wrapper, ArrayWrapper)
self._wrapper = wrapper
Configured.__init__(self, wrapper=wrapper, **kwargs)
PandasIndexer.__init__(self)
AttrResolver.__init__(self)
def __init__(self,
main_price,
init_capital,
orders,
cash,
shares,
freq=None,
year_freq=None,
levy_alpha=None,
risk_free=None,
required_return=None,
cutoff=None,
factor_returns=None,
incl_unrealized_stats=False):
# Perform checks
checks.assert_type(main_price, (pd.Series, pd.DataFrame))
if checks.is_frame(main_price):
checks.assert_type(init_capital, pd.Series)
checks.assert_same(main_price.columns, init_capital.index)
else:
checks.assert_ndim(init_capital, 0)
checks.assert_same_meta(main_price, cash)
checks.assert_same_meta(main_price, shares)
# Store passed arguments
self._main_price = main_price
self._init_capital = init_capital
self._orders = orders
self._cash = cash
self._shares = shares
self._incl_unrealized_stats = incl_unrealized_stats
freq = main_price.vbt(freq=freq).freq
if freq is None:
raise ValueError(
"Couldn't parse the frequency of index. You must set `freq`.")
self._freq = freq
year_freq = main_price.vbt.returns(year_freq=year_freq).year_freq
if freq is None:
raise ValueError("You must set `year_freq`.")
self._year_freq = year_freq
# Parameters
self._levy_alpha = defaults.portfolio[
'levy_alpha'] if levy_alpha is None else levy_alpha
self._risk_free = defaults.portfolio[
'risk_free'] if risk_free is None else risk_free
self._required_return = defaults.portfolio[
'required_return'] if required_return is None else required_return
self._cutoff = defaults.portfolio[
'cutoff'] if cutoff is None else cutoff
self._factor_returns = defaults.portfolio[
'factor_returns'] if factor_returns is None else factor_returns
# Supercharge
PandasIndexer.__init__(self, _indexing_func)
self.wrapper = ArrayWrapper.from_obj(main_price, freq=freq)
def __init__(self, records_arr, ts, freq=None, idx_field='end_idx'):
Records.__init__(self,
records_arr,
ArrayWrapper.from_obj(ts, freq=freq),
idx_field=idx_field)
PandasIndexer.__init__(self, _indexing_func)
if not all(field in records_arr.dtype.names
for field in drawdown_dt.names):
raise ValueError(
"Records array must have all fields defined in drawdown_dt")
self.ts = ts
def __init__(self, records_arr, main_price, freq=None, idx_field='idx'):
Records.__init__(self,
records_arr,
ArrayWrapper.from_obj(main_price, freq=freq),
idx_field=idx_field)
PandasIndexer.__init__(self, _indexing_func)
if not all(field in records_arr.dtype.names
for field in order_dt.names):
raise ValueError(
"Records array must have all fields defined in order_dt")
self.main_price = main_price
def __init__(self,
index,
columns,
ndim,
freq=None,
column_only_select=None,
group_select=None,
grouped_ndim=None,
group_by=None,
allow_enable=True,
allow_disable=True,
allow_modify=True):
Configured.__init__(self,
index=index,
columns=columns,
ndim=ndim,
freq=freq,
column_only_select=column_only_select,
group_select=group_select,
grouped_ndim=grouped_ndim,
group_by=group_by,
allow_enable=allow_enable,
allow_disable=allow_disable,
allow_modify=allow_modify)
checks.assert_not_none(index)
checks.assert_not_none(columns)
checks.assert_not_none(ndim)
if not isinstance(index, pd.Index):
index = pd.Index(index)
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
self._index = index
self._columns = columns
self._ndim = ndim
self._freq = freq
self._column_only_select = column_only_select
self._group_select = group_select
self._grouper = ColumnGrouper(columns,
group_by=group_by,
allow_enable=allow_enable,
allow_disable=allow_disable,
allow_modify=allow_modify)
self._grouped_ndim = grouped_ndim
PandasIndexer.__init__(self,
_indexing_func,
column_only_select=column_only_select,
group_select=group_select)
def __init__(self, wrapper, records_arr, ts, idx_field='end_idx'):
Records.__init__(self, wrapper, records_arr, idx_field=idx_field)
Configured.__init__(self,
wrapper=wrapper,
records_arr=records_arr,
ts=ts,
idx_field=idx_field)
self._ts = ts
if not all(field in records_arr.dtype.names
for field in drawdown_dt.names):
raise ValueError(
"Records array must have all fields defined in drawdown_dt")
PandasIndexer.__init__(self, _indexing_func)
def __init__(self, wrapper, records_arr, close, idx_field='exit_idx'):
Records.__init__(self, wrapper, records_arr, idx_field=idx_field)
Configured.__init__(self,
wrapper=wrapper,
records_arr=records_arr,
close=close,
idx_field=idx_field)
self.close = close
if not all(field in records_arr.dtype.names
for field in event_dt.names):
raise ValueError(
"Records array must have all fields defined in event_dt")
PandasIndexer.__init__(self, _indexing_func)
def __init__(self, records_arr, wrapper, idx_field=None):
if not isinstance(records_arr, np.ndarray):
records_arr = np.asarray(records_arr)
checks.assert_not_none(records_arr.dtype.fields)
checks.assert_value_in('col', records_arr.dtype.names)
checks.assert_type(wrapper, ArrayWrapper)
if idx_field is not None:
checks.assert_value_in(idx_field, records_arr.dtype.names)
else:
if 'idx' in records_arr.dtype.names:
idx_field = 'idx'
self.records_arr = records_arr
self.wrapper = wrapper
self.idx_field = idx_field
PandasIndexer.__init__(self, _records_indexing_func)
def __init__(self, mapped_arr, col_arr, wrapper, idx_arr=None):
if not isinstance(mapped_arr, np.ndarray):
mapped_arr = np.asarray(mapped_arr)
if not isinstance(col_arr, np.ndarray):
col_arr = np.asarray(col_arr)
checks.assert_same_shape(mapped_arr, col_arr, axis=0)
checks.assert_type(wrapper, ArrayWrapper)
if idx_arr is not None:
if not isinstance(idx_arr, np.ndarray):
idx_arr = np.asarray(idx_arr)
checks.assert_same_shape(mapped_arr, idx_arr, axis=0)
self.mapped_arr = mapped_arr
self.col_arr = col_arr
self.wrapper = wrapper
self.idx_arr = idx_arr
PandasIndexer.__init__(self, _mapped_indexing_func)
def __init__(self, ts_list, output_list, param_list, mapper_list,
name):
perform_init_checks(ts_list, output_list, param_list, mapper_list,
name)
for i, ts_name in enumerate(ts_names):
setattr(self, f'_{ts_name}', ts_list[i])
self.wrapper = ArrayWrapper.from_obj(ts_list[0])
for i, output_name in enumerate(output_names):
setattr(self, f'_{output_name}', output_list[i])
for i, param_name in enumerate(param_names):
setattr(self, f'_{param_name}_array', param_list[i])
setattr(self, f'_{param_name}_mapper', mapper_list[i])
if len(param_names) > 1:
setattr(self, '_tuple_mapper', mapper_list[-1])
setattr(self, '_name', name)
# Initialize indexers
PandasIndexer.__init__(self, indexing_func)
ParamIndexer.__init__(self, mapper_list, indexing_func)
def __init__(self,
index: tp.IndexLike,
columns: tp.IndexLike,
ndim: int,
freq: tp.Optional[tp.FrequencyLike] = None,
column_only_select: tp.Optional[bool] = None,
group_select: tp.Optional[bool] = None,
grouped_ndim: tp.Optional[int] = None,
**kwargs) -> None:
config = dict(
index=index,
columns=columns,
ndim=ndim,
freq=freq,
column_only_select=column_only_select,
group_select=group_select,
grouped_ndim=grouped_ndim,
)
checks.assert_not_none(index)
checks.assert_not_none(columns)
checks.assert_not_none(ndim)
if not isinstance(index, pd.Index):
index = pd.Index(index)
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
self._index = index
self._columns = columns
self._ndim = ndim
self._freq = freq
self._column_only_select = column_only_select
self._group_select = group_select
self._grouper = ColumnGrouper(columns, **kwargs)
self._grouped_ndim = grouped_ndim
PandasIndexer.__init__(self)
Configured.__init__(self, **merge_dicts(config, self._grouper._config))
def __init__(self, wrapper, **kwargs):
checks.assert_type(wrapper, ArrayWrapper)
self._wrapper = wrapper
Configured.__init__(self, wrapper=wrapper, **kwargs)
PandasIndexer.__init__(self)
def __init__(self, index, group_by=None):
self.index = index
self.group_by = None
self.group_by = self.resolve_group_by(group_by=group_by)
PandasIndexer.__init__(self, _indexing_func)
