def create_scenario(weights_bandwidth=0.02,
target_vol=0.01,
method='risk_neutral'):
industry_names = industry_list('sw_adj', 1)
constraint_risk = [
'EARNYILD', 'LIQUIDTY', 'GROWTH', 'SIZE', 'BETA', 'MOMENTUM'
]
total_risk_names = constraint_risk + industry_names + [
'benchmark', 'total'
]
b_type = []
l_val = []
u_val = []
for name in total_risk_names:
if name == 'benchmark':
b_type.append(BoundaryType.RELATIVE)
l_val.append(0.8)
u_val.append(1.001)
elif name == 'total':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.001)
u_val.append(.001)
elif name == 'EARNYILD':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.001)
u_val.append(0.60)
elif name == 'GROWTH':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.20)
u_val.append(0.20)
elif name == 'MOMENTUM':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.10)
u_val.append(0.20)
elif name == 'SIZE':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.20)
u_val.append(0.20)
elif name == 'LIQUIDTY':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.25)
u_val.append(0.25)
else:
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(-0.01)
u_val.append(0.01)
bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)
running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,
rebalance_method=method,
bounds=bounds,
target_vol=target_vol,
turn_over_target=0.4)
ret_df, positions = strategy.run(running_setting)
return ret_df
def test_create_box_bounds_single_value(self):
names = ['a', 'b', 'c']
b_type = BoundaryType.RELATIVE
l_val = 0.8
u_val = 1.1
bounds = create_box_bounds(names, b_type, l_val, u_val)
for key, bound in bounds.items():
l_bound = bound.lower
u_bound = bound.upper
self.assertEqual(l_bound.b_type, b_type)
self.assertEqual(u_bound.b_type, b_type)
self.assertAlmostEqual(l_bound.val, l_val)
self.assertAlmostEqual(u_bound.val, u_val)
def test_create_box_bounds_multiple_values(self):
names = ['a', 'b', 'c']
b_type = BoundaryType.RELATIVE
l_val = [0.9, 0.8, 1.1]
u_val = [1.1, 1.2, 1.3]
bounds = create_box_bounds(names, b_type, l_val, u_val)
for i, name in enumerate(names):
bound = bounds[name]
l_bound = bound.lower
u_bound = bound.upper
self.assertEqual(l_bound.b_type, b_type)
self.assertEqual(u_bound.b_type, b_type)
self.assertAlmostEqual(l_bound.val, l_val[i])
self.assertAlmostEqual(u_bound.val, u_val[i])
def test_linear_constraints(self):
cons_mat = np.random.randn(100, 3)
backbone = np.random.randn(100)
names = ['a', 'b', 'c']
cons_mat = pd.DataFrame(cons_mat, columns=names)
b_type = BoundaryType.ABSOLUTE
l_val = -0.8
u_val = 1.1
bounds = create_box_bounds(names, b_type, l_val, u_val)
constraints = LinearConstraints(bounds=bounds,
cons_mat=cons_mat,
backbone=backbone)
l_bounds, u_bounds = constraints.risk_targets()
risk_exp = constraints.risk_exp
for i, name in enumerate(names):
center = risk_exp[:, i] @ backbone
self.assertAlmostEqual(center + l_val, l_bounds[i])
self.assertAlmostEqual(center + u_val, u_bounds[i])
for name in total_risk_names:
if name == 'total':
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(.0)
u_val.append(.0)
elif name == 'benchmark':
b_type.append(BoundaryType.RELATIVE)
l_val.append(0.8)
u_val.append(1.0)
else:
b_type.append(BoundaryType.MAXABSREL)
l_val.append((0.00, 0.0))
u_val.append((0.00, 0.0))
bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)
running_setting = RunningSetting(lbound=None,
ubound=None,
weights_bandwidth=0.01,
rebalance_method='risk_neutral',
bounds=bounds,
target_vol=0.05,
turn_over_target=0.4)
strategy = Strategy(alpha_model,
data_meta,
universe=universe,
start_date=start_date,
end_date=end_date,
freq=freq,
