def test_to_generics_two_generics_key_error():
wts1 = pd.DataFrame([[0.5, 0], [0.5, 0.5], [0, 0.5]],
index=['CLX16', 'CLZ16', 'CLF17'],
columns=[0, 1])
# COZ16 is mistyped as CLO16 resulting in no weights for the instrument
# COZ16
wts2 = pd.DataFrame([[0.5, 0], [0.5, 0.5], [0, 0.5]],
index=['COX16', 'CLO16', 'COF17'],
columns=[0, 1])
wts = {"CL": wts1, "CO": wts2}
instrs = pd.Series(
[10, 20, 10, 10, 20, 10],
index=["CLX16", "CLZ16", "CLF17", "COX16", "COZ16", "COF17"])
with pytest.raises(KeyError):
mappings.to_generics(instrs, wts)
def test_to_generics_two_generics_minimize_error_integer_soln():
wts = pd.DataFrame([[0.5, 0], [0.5, 0.5], [0, 0.5]],
index=['CLX16', 'CLZ16', 'CLF17'],
columns=[0, 1])
instrs = pd.Series([10, 25, 11], index=["CLX16", "CLZ16", "CLF17"])
generics = mappings.to_generics(instrs, wts)
exp_generics = pd.Series([22.0, 24.0], index=[0, 1])
assert_series_equal(generics, exp_generics)
def test_to_generics_two_generics_zero_generics_weight():
# scenario where one generic has 0 weight, tests for bug where result
# has epsilon weight on CL1
wts = pd.DataFrame([[0, 1]], index=["CLZ16"], columns=["CL1", "CL2"])
notional = pd.Series([-13900.0], index=["CLZ16"])
generics = mappings.to_generics(notional, wts)
exp_generics = pd.Series([-13900.0], index=["CL2"])
assert_series_equal(generics, exp_generics)
def test_to_generics_two_generics_exact_soln_negative():
wts = pd.DataFrame([[0.5, 0], [0.5, 0.5], [0, 0.5]],
index=['CLX16', 'CLZ16', 'CLF17'],
columns=[0, 1])
instrs = pd.Series([10, 0, -10], index=["CLX16", "CLZ16", "CLF17"])
generics = mappings.to_generics(instrs, wts)
exp_generics = pd.Series([20.0, -20.0], index=[0, 1])
assert_series_equal(generics, exp_generics)
def test_to_generics_three_generics_non_exact_soln():
wts = pd.DataFrame(
[[0.5, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0, 0, 0.5]],
index=['CLX16', 'CLZ16', 'CLF17', 'CLG17'],
columns=[0, 1, 2])
instrs = pd.Series([10, 21, 20, 13],
index=["CLX16", "CLZ16", "CLF17", "CLG17"])
generics = mappings.to_generics(instrs, wts)
exp_generics = pd.Series([22.0, 18.0, 24.0], index=[0, 1, 2])
assert_series_equal(generics, exp_generics)
def test_to_generics_two_generics_multi_asset():
wts1 = pd.DataFrame([[0.5, 0], [0.5, 0.5], [0, 0.5]],
index=['CLX16', 'CLZ16', 'CLF17'],
columns=["CL0", "CL1"])
wts2 = pd.DataFrame([[0.5, 0], [0.5, 0.5], [0, 0.5]],
index=['COX16', 'COZ16', 'COF17'],
columns=["CO0", "CO1"])
wts = {"CL": wts1, "CO": wts2}
instrs = pd.Series(
[10, 20, 10, 10, 20, 10],
index=["CLX16", "CLZ16", "CLF17", "COX16", "COZ16", "COF17"])
generics = mappings.to_generics(instrs, wts)
exp_generics = pd.Series([20.0, 20.0, 20.0, 20.0],
index=["CL0", "CL1", "CO0", "CO1"])
assert_series_equal(generics, exp_generics)
