def test_long_short_build_with_group(self):
x = self.x[:, 0].flatten()
calc_weights = long_short_build(x, groups=self.groups).flatten()
expected_weights = pd.Series(x).groupby(self.groups).apply(lambda s: s / np.abs(s).sum())
np.testing.assert_array_almost_equal(calc_weights, expected_weights)
calc_weights = long_short_build(self.x, groups=self.groups)
expected_weights = pd.DataFrame(self.x).groupby(self.groups).apply(lambda s: s / np.abs(s).sum(axis=0))
np.testing.assert_array_almost_equal(calc_weights, expected_weights)
def test_long_short_build(self):
x = self.x[:, 0].flatten()
calc_weights = long_short_build(x).flatten()
expected_weights = x / np.abs(x).sum()
np.testing.assert_array_almost_equal(calc_weights, expected_weights)
calc_weights = long_short_build(self.x, leverage=2)
expected_weights = self.x / np.abs(self.x).sum(axis=0) * 2
np.testing.assert_array_almost_equal(calc_weights, expected_weights)
def test_long_short_build_with_masks(self):
x = self.x[:, 0].flatten()
masked_x = x.copy()
masked_x[~self.masks] = 0.
leverage = np.abs(masked_x).sum()
calc_weights = long_short_build(x, masks=self.masks, leverage=leverage).flatten()
expected_weights = x.copy()
expected_weights[~self.masks] = 0.
np.testing.assert_array_almost_equal(calc_weights, expected_weights)
def build_portfolio(er: np.ndarray,
builder: Optional[str] = 'long_short',
**kwargs) -> np.ndarray:
builder = builder.lower()
if builder == 'ls' or builder == 'long_short':
return long_short_build(er, **kwargs).flatten()
elif builder == 'rank':
return rank_build(er, **kwargs).flatten()
elif builder == 'percent':
return percent_build(er, **kwargs).flatten()
elif builder == 'linear_prog' or builder == 'linear':
status, _, weight = linear_build(er, **kwargs)
if status != 'optimal':
raise ValueError(
'linear programming optimizer in status: {0}'.format(status))
else:
return weight
def er_portfolio_analysis(
er: np.ndarray,
industry: np.ndarray,
dx_return: np.ndarray,
constraints: Optional[Constraints] = None,
detail_analysis=True,
benchmark: Optional[np.ndarray] = None,
is_tradable: Optional[np.ndarray] = None,
method='risk_neutral',
**kwargs) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
er = er.flatten()
def create_constraints(benchmark, **kwargs):
if 'lbound' in kwargs:
lbound = kwargs['lbound'].copy()
del kwargs['lbound']
else:
lbound = 0.
if 'ubound' in kwargs:
ubound = kwargs['ubound'].copy()
del kwargs['ubound']
else:
ubound = 0.01 + benchmark
if is_tradable is not None:
ubound[~is_tradable] = np.minimum(lbound, ubound)[~is_tradable]
risk_lbound, risk_ubound = constraints.risk_targets()
cons_exp = constraints.risk_exp
return lbound, ubound, cons_exp, risk_lbound, risk_ubound
if benchmark is not None and method == 'risk_neutral':
lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints(
benchmark, **kwargs)
turn_over_target = kwargs.get('turn_over_target')
current_position = kwargs.get('current_position')
status, _, weights = linear_build(er,
risk_constraints=cons_exp,
lbound=lbound,
ubound=ubound,
risk_target=(risk_lbound,
risk_ubound),
turn_over_target=turn_over_target,
current_position=current_position)
if status != 'optimal':
raise ValueError(
'linear programming optimizer in status: {0}'.format(status))
elif method == 'rank':
weights = rank_build(
er, use_rank=kwargs['use_rank'],
masks=is_tradable).flatten() * benchmark.sum() / kwargs['use_rank']
elif method == 'ls' or method == 'long_short':
weights = long_short_build(er).flatten()
elif method == 'mv' or method == 'mean_variance':
lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints(
benchmark, **kwargs)
cov = kwargs['cov']
if 'lam' in kwargs:
lam = kwargs['lam']
else:
lam = 1.
status, _, weights = mean_variance_builder(er,
cov=cov,
bm=benchmark,
lbound=lbound,
ubound=ubound,
risk_exposure=cons_exp,
risk_target=(risk_lbound,
risk_ubound),
lam=lam)
if status != 'optimal':
raise ValueError(
'mean variance optimizer in status: {0}'.format(status))
else:
raise ValueError("Unknown building type ({0})".format(method))
if detail_analysis:
analysis = simple_settle(weights, dx_return, industry, benchmark)
else:
analysis = None
return pd.DataFrame({'weight': weights,
'industry': industry,
'er': er}), \
analysis