mirror of
https://github.com/robertmartin8/PyPortfolioOpt.git
synced 2022-11-27 18:02:41 +03:00
updating in advance of deprecations
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@@ -239,8 +239,8 @@ def test_bl_cov_default():
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def test_market_risk_aversion():
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prices = pd.read_csv(
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resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True
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)
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resource("spy_prices.csv"), parse_dates=True, index_col=0
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).squeeze("columns")
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delta = black_litterman.market_implied_risk_aversion(prices)
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assert np.round(delta, 5) == 2.68549
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@@ -263,8 +263,8 @@ def test_bl_weights():
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bl = BlackLittermanModel(S, absolute_views=viewdict)
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prices = pd.read_csv(
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resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True
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)
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resource("spy_prices.csv"), parse_dates=True, index_col=0
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).squeeze("columns")
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delta = black_litterman.market_implied_risk_aversion(prices)
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bl.bl_weights(delta)
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@@ -316,8 +316,8 @@ def test_market_implied_prior():
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S = risk_models.sample_cov(df)
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prices = pd.read_csv(
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resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True
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)
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resource("spy_prices.csv"), parse_dates=True, index_col=0
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).squeeze("columns")
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delta = black_litterman.market_implied_risk_aversion(prices)
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mcaps = get_market_caps()
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@@ -375,8 +375,8 @@ def test_bl_market_prior():
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S = risk_models.sample_cov(df)
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prices = pd.read_csv(
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resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True
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)
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resource("spy_prices.csv"), parse_dates=True, index_col=0
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).squeeze("columns")
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delta = black_litterman.market_implied_risk_aversion(prices)
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@@ -468,8 +468,8 @@ def test_bl_tau():
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S = risk_models.sample_cov(df)
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prices = pd.read_csv(
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resource("spy_prices.csv"), parse_dates=True, index_col=0, squeeze=True
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)
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resource("spy_prices.csv"), parse_dates=True, index_col=0
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).squeeze("columns")
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delta = black_litterman.market_implied_risk_aversion(prices)
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@@ -24,7 +24,6 @@ def test_data_source():
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assert isinstance(df, pd.DataFrame)
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assert df.shape[1] == 20
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assert len(df) == 7126
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assert df.index.is_all_dates
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def test_returns_dataframe():
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@@ -33,7 +32,6 @@ def test_returns_dataframe():
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assert isinstance(returns_df, pd.DataFrame)
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assert returns_df.shape[1] == 20
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assert len(returns_df) == 7125
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assert returns_df.index.is_all_dates
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assert not ((returns_df > 1) & returns_df.notnull()).any().any()
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@@ -393,6 +391,9 @@ def test_max_sharpe_error():
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with pytest.raises(TypeError):
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ef.max_sharpe()
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with pytest.raises(ValueError):
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ef.max_sharpe(risk_free_rate=max(ef.expected_returns + 0.01))
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def test_max_sharpe_risk_free_warning():
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ef = setup_efficient_frontier()
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@@ -1072,9 +1073,6 @@ def test_efficient_return():
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def test_efficient_return_error():
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ef = setup_efficient_frontier()
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max_ret = ef.expected_returns.max()
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with pytest.raises(ValueError):
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ef.efficient_return(-0.1)
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with pytest.raises(ValueError):
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# This return is too high
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ef.efficient_return(max_ret + 0.01)
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@@ -11,7 +11,6 @@ def test_returns_dataframe():
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assert isinstance(returns_df, pd.DataFrame)
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assert returns_df.shape[1] == 20
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assert len(returns_df) == 7125
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assert returns_df.index.is_all_dates
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assert not ((returns_df > 1) & returns_df.notnull()).any().any()
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@@ -30,7 +30,7 @@ def test_hrp_portfolio():
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# uncomment this line if you want generating a new file
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# pd.Series(w).to_csv(resource("weights_hrp.csv"))
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x = pd.read_csv(resource("weights_hrp.csv"), squeeze=True, index_col=0)
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x = pd.read_csv(resource("weights_hrp.csv"), index_col=0).squeeze("columns")
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pd.testing.assert_series_equal(x, pd.Series(w), check_names=False, rtol=1e-2)
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assert isinstance(w, dict)
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