def test_capm_with_benchmark():
df = get_data()
mkt_df = get_benchmark_data()
mu = expected_returns.capm_return(df,
market_prices=mkt_df,
compounding=True)
assert isinstance(mu, pd.Series)
assert list(mu.index) == list(df.columns)
assert mu.notnull().all()
assert mu.dtype == "float64"
correct_mu = np.array([
0.09115799375654746,
0.09905386632033128,
0.05676282405265752,
0.06291827346436336,
0.13147799781014877,
0.10239088012000815,
0.1311567086884512,
0.07339649698626659,
0.1301248935078549,
0.07620949056643983,
0.07629095442513395,
0.12163575425541985,
0.10400070536161658,
0.0781736030988492,
0.09185177050469516,
0.10245700691271296,
0.11268307946677197,
0.07870087187919145,
0.1275598841214107,
0.09536788741392595,
])
np.testing.assert_array_almost_equal(mu.values, correct_mu)
def test_capm_with_benchmark():
df = get_data()
mkt_df = get_benchmark_data()
mu = expected_returns.capm_return(df,
market_prices=mkt_df,
compounding=True)
assert isinstance(mu, pd.Series)
assert list(mu.index) == list(df.columns)
assert mu.notnull().all()
assert mu.dtype == "float64"
correct_mu = np.array([
0.10903299,
0.11891232,
0.0659977,
0.07369941,
0.15948144,
0.12308759,
0.15907944,
0.08680978,
0.15778843,
0.0903294,
0.09043133,
0.14716681,
0.12510181,
0.0927869,
0.10990104,
0.12317033,
0.13596521,
0.09344662,
0.15457909,
0.11430041,
])
np.testing.assert_array_almost_equal(mu.values, correct_mu)
def test_return_model_additional_kwargs():
df = get_data()
mkt_prices = get_benchmark_data()
mu1 = expected_returns.return_model(
df, method="capm_return", market_prices=mkt_prices, risk_free_rate=0.03
)
mu2 = expected_returns.capm_return(
df, market_prices=mkt_prices, risk_free_rate=0.03
)
pd.testing.assert_series_equal(mu1, mu2)
