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updating in advance of deprecations

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Robert Martin
2022-11-26 10:47:21 -05:00
parent 00d7d391c8
commit 88433eab88
4 changed files with 14 additions and 17 deletions
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20
tests/test_black_litterman.py
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@@ -239,8 +239,8 @@ def test_bl_cov_default():
def test_market_risk_aversion(): def test_market_risk_aversion():
prices = pd.read_csv( prices = pd.read_csv(
resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True resource("spy_prices.csv"), parse_dates=True, index_col=0
) ).squeeze("columns")
delta = black_litterman.market_implied_risk_aversion(prices) delta = black_litterman.market_implied_risk_aversion(prices)
assert np.round(delta, 5) == 2.68549 assert np.round(delta, 5) == 2.68549
@@ -263,8 +263,8 @@ def test_bl_weights():
bl = BlackLittermanModel(S, absolute_views=viewdict) bl = BlackLittermanModel(S, absolute_views=viewdict)
prices = pd.read_csv( prices = pd.read_csv(
resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True resource("spy_prices.csv"), parse_dates=True, index_col=0
) ).squeeze("columns")
delta = black_litterman.market_implied_risk_aversion(prices) delta = black_litterman.market_implied_risk_aversion(prices)
bl.bl_weights(delta) bl.bl_weights(delta)
@@ -316,8 +316,8 @@ def test_market_implied_prior():
S = risk_models.sample_cov(df) S = risk_models.sample_cov(df)
prices = pd.read_csv( prices = pd.read_csv(
resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True resource("spy_prices.csv"), parse_dates=True, index_col=0
) ).squeeze("columns")
delta = black_litterman.market_implied_risk_aversion(prices) delta = black_litterman.market_implied_risk_aversion(prices)
mcaps = get_market_caps() mcaps = get_market_caps()
@@ -375,8 +375,8 @@ def test_bl_market_prior():
S = risk_models.sample_cov(df) S = risk_models.sample_cov(df)
prices = pd.read_csv( prices = pd.read_csv(
resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True resource("spy_prices.csv"), parse_dates=True, index_col=0
) ).squeeze("columns")
delta = black_litterman.market_implied_risk_aversion(prices) delta = black_litterman.market_implied_risk_aversion(prices)
@@ -468,8 +468,8 @@ def test_bl_tau():
S = risk_models.sample_cov(df) S = risk_models.sample_cov(df)
prices = pd.read_csv( prices = pd.read_csv(
resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True resource("spy_prices.csv"), parse_dates=True, index_col=0
) ).squeeze("columns")
delta = black_litterman.market_implied_risk_aversion(prices) delta = black_litterman.market_implied_risk_aversion(prices)

8
tests/test_efficient_frontier.py
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@@ -24,7 +24,6 @@ def test_data_source():
assert isinstance(df, pd.DataFrame) assert isinstance(df, pd.DataFrame)
assert df.shape[1] == 20 assert df.shape[1] == 20
assert len(df) == 7126 assert len(df) == 7126
assert df.index.is_all_dates
def test_returns_dataframe(): def test_returns_dataframe():
@@ -33,7 +32,6 @@ def test_returns_dataframe():
assert isinstance(returns_df, pd.DataFrame) assert isinstance(returns_df, pd.DataFrame)
assert returns_df.shape[1] == 20 assert returns_df.shape[1] == 20
assert len(returns_df) == 7125 assert len(returns_df) == 7125
assert returns_df.index.is_all_dates
assert not ((returns_df > 1) & returns_df.notnull()).any().any() assert not ((returns_df > 1) & returns_df.notnull()).any().any()
@@ -393,6 +391,9 @@ def test_max_sharpe_error():
with pytest.raises(TypeError): with pytest.raises(TypeError):
ef.max_sharpe() ef.max_sharpe()
with pytest.raises(ValueError):
ef.max_sharpe(risk_free_rate=max(ef.expected_returns + 0.01))
def test_max_sharpe_risk_free_warning(): def test_max_sharpe_risk_free_warning():
ef = setup_efficient_frontier() ef = setup_efficient_frontier()
@@ -1072,9 +1073,6 @@ def test_efficient_return():
def test_efficient_return_error(): def test_efficient_return_error():
ef = setup_efficient_frontier() ef = setup_efficient_frontier()
max_ret = ef.expected_returns.max() max_ret = ef.expected_returns.max()
with pytest.raises(ValueError):
ef.efficient_return(-0.1)
with pytest.raises(ValueError): with pytest.raises(ValueError):
# This return is too high # This return is too high
ef.efficient_return(max_ret + 0.01) ef.efficient_return(max_ret + 0.01)

1
tests/test_expected_returns.py
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@@ -11,7 +11,6 @@ def test_returns_dataframe():
assert isinstance(returns_df, pd.DataFrame) assert isinstance(returns_df, pd.DataFrame)
assert returns_df.shape[1] == 20 assert returns_df.shape[1] == 20
assert len(returns_df) == 7125 assert len(returns_df) == 7125
assert returns_df.index.is_all_dates
assert not ((returns_df > 1) & returns_df.notnull()).any().any() assert not ((returns_df > 1) & returns_df.notnull()).any().any()

2
tests/test_hrp.py
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@@ -30,7 +30,7 @@ def test_hrp_portfolio():
# uncomment this line if you want generating a new file # uncomment this line if you want generating a new file
# pd.Series(w).to_csv(resource("weights_hrp.csv")) # pd.Series(w).to_csv(resource("weights_hrp.csv"))
x = pd.read_csv(resource("weights_hrp.csv"), squeeze=True, index_col=0) x = pd.read_csv(resource("weights_hrp.csv"), index_col=0).squeeze("columns")
pd.testing.assert_series_equal(x, pd.Series(w), check_names=False, rtol=1e-2) pd.testing.assert_series_equal(x, pd.Series(w), check_names=False, rtol=1e-2)
assert isinstance(w, dict) assert isinstance(w, dict)