def test_simples_settle(self):
calc_ret = simple_settle(self.weights, self.ret_series)
ret_series = self.ret_series.reshape((-1, 1))
expected_ret = (self.weights * ret_series).sum(axis=0)
np.testing.assert_array_almost_equal(calc_ret, expected_ret)
ret_series = np.random.randn(self.n_samples, 1)
calc_ret = simple_settle(self.weights, ret_series)
expected_ret = (self.weights * ret_series).sum(axis=0)
np.testing.assert_array_almost_equal(calc_ret, expected_ret)
def benchmark_simple_settle_with_group(n_samples: int, n_portfolios: int,
n_loops: int, n_groups: int) -> None:
print("-" * 60)
print("Starting simple settle with group-by values benchmarking")
print(
"Parameters(n_samples: {0}, n_portfolios: {1}, n_loops: {2}, n_groups: {3})"
.format(n_samples, n_portfolios, n_loops, n_groups))
weights = np.random.randn(n_samples, n_portfolios)
ret_series = np.random.randn(n_samples)
groups = np.random.randint(n_groups, size=n_samples)
start = dt.datetime.now()
for _ in range(n_loops):
calc_ret = simple_settle(weights, ret_series, groups=groups)
impl_model_time = dt.datetime.now() - start
print('{0:20s}: {1}'.format('Implemented model', impl_model_time))
start = dt.datetime.now()
ret_series.shape = -1, 1
for _ in range(n_loops):
ret_mat = weights * ret_series
exp_ret = pd.DataFrame(ret_mat).groupby(groups).sum().values
benchmark_model_time = dt.datetime.now() - start
np.testing.assert_array_almost_equal(calc_ret, exp_ret)
print('{0:20s}: {1}'.format('Benchmark model', benchmark_model_time))
def benchmark_simple_settle(n_samples: int, n_portfolios: int,
n_loops: int) -> None:
print("-" * 60)
print("Starting simple settle benchmarking")
print("Parameters(n_samples: {0}, n_portfolios: {1}, n_loops: {2})".format(
n_samples, n_portfolios, n_loops))
weights = np.random.randn(n_samples, n_portfolios)
ret_series = np.random.randn(n_samples)
start = dt.datetime.now()
for _ in range(n_loops):
calc_ret = simple_settle(weights, ret_series)
impl_model_time = dt.datetime.now() - start
print('{0:20s}: {1}'.format('Implemented model', impl_model_time))
start = dt.datetime.now()
ret_series.shape = -1, 1
for _ in range(n_loops):
exp_ret = (weights * ret_series).sum(axis=0)
benchmark_model_time = dt.datetime.now() - start
np.testing.assert_array_almost_equal(calc_ret, exp_ret)
print('{0:20s}: {1}'.format('Benchmark model', benchmark_model_time))
def test_simples_settle(self):
calc_ret = simple_settle(self.weights, self.ret_series)
ret_series = self.ret_series.reshape((-1, 1))
expected_ret = self.weights @ ret_series
self.assertAlmostEqual(calc_ret['er'][0], expected_ret[0])
def test_simple_settle_with_group(self):
calc_ret = simple_settle(self.weights, self.ret_series, self.groups)
ret_series = self.ret_series.reshape((-1, 1))
ret_mat = self.weights * ret_series
expected_ret = pd.DataFrame(ret_mat).groupby(self.groups).sum().values
np.testing.assert_array_almost_equal(calc_ret, expected_ret)
ret_series = np.random.randn(self.n_samples, 1)
calc_ret = simple_settle(self.weights, ret_series, self.groups)
ret_mat = self.weights * ret_series
expected_ret = pd.DataFrame(ret_mat).groupby(self.groups).sum().values
np.testing.assert_array_almost_equal(calc_ret, expected_ret)
def test_simple_settle_with_group(self):
calc_ret = simple_settle(self.weights, self.ret_series, self.groups)
ret_series = self.weights * self.ret_series
expected_ret = pd.Series(ret_series).groupby(self.groups).sum().values
np.testing.assert_array_almost_equal(calc_ret['er'].values[:-1],
expected_ret)
self.assertAlmostEqual(calc_ret['er'].values[-1], expected_ret.sum())
def er_portfolio_analysis(
er: np.ndarray,
industry: np.ndarray,
dx_return: np.ndarray,
constraints: Optional[Union[LinearConstraints, 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 = np.maximum(0., benchmark - 0.01)
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 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_builder(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_builder(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))
elif method == 'tv' or method == 'target_vol':
lbound, ubound, cons_exp, risk_lbound, risk_ubound = create_constraints(
benchmark, **kwargs)
cov = kwargs['cov']
if 'target_vol' in kwargs:
target_vol = kwargs['target_vol']
else:
target_vol = 1.
status, _, weights = target_vol_builder(er,
cov=cov,
bm=benchmark,
lbound=lbound,
ubound=ubound,
risk_exposure=cons_exp,
risk_target=(risk_lbound,
risk_ubound),
vol_low=0,
vol_high=target_vol)
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
