improved tests

Author: robertmartin8

docs/ExpectedReturns.rst

@@ -50,8 +50,6 @@ superior models and feed them into the optimizer.
 
     .. autofunction:: returns_from_prices
 
-    .. autofunction:: log_returns_from_prices
-
     .. autofunction:: prices_from_returns
 
 

pypfopt/expected_returns.py

@@ -43,22 +43,6 @@ def returns_from_prices(prices, log_returns=False):
         return prices.pct_change().dropna(how="all")
 
 
-def log_returns_from_prices(prices):
-    """
-    Calculate the log returns given prices.
-
-    :param prices: adjusted (daily) closing prices of the asset, each row is a
-                   date and each column is a ticker/id.
-    :type prices: pd.DataFrame
-    :return: (daily) returns
-    :rtype: pd.DataFrame
-    """
-    warnings.warn(
-        "log_returns_from_prices is deprecated. Please use returns_from_prices(prices, log_returns=True)"
-    )
-    return np.log(1 + prices.pct_change()).dropna(how="all")
-
-
 def prices_from_returns(returns, log_returns=False):
     """
     Calculate the pseudo-prices given returns. These are not true prices because

tests/test_discrete_allocation.py

@@ -25,7 +25,7 @@ def test_get_latest_prices_error():
 def test_remove_zero_positions():
     raw = {"MA": 14, "FB": 12, "XOM": 0, "PFE": 51, "BABA": 5, "GOOG": 0}
 
-    da = DiscreteAllocation({}, pd.Series())
+    da = DiscreteAllocation({}, pd.Series(dtype=float))
     assert da._remove_zero_positions(raw) == {"MA": 14, "FB": 12, "PFE": 51, "BABA": 5}
 
 

tests/test_efficient_frontier.py

@@ -582,9 +582,10 @@ def test_max_sharpe_risk_free_portfolio_performance():
     # max_sharpe
     ef = setup_efficient_frontier()
     ef.max_sharpe(risk_free_rate=0.05)
-    res = ef.portfolio_performance()
-    res2 = ef.portfolio_performance(risk_free_rate=0.05)
-    np.testing.assert_allclose(res, res2)
+    with pytest.warns(UserWarning):
+        res = ef.portfolio_performance()
+        res2 = ef.portfolio_performance(risk_free_rate=0.05)
+        np.testing.assert_allclose(res, res2)
 
 
 def test_min_vol_pair_constraint():

tests/test_expected_returns.py

@@ -57,9 +57,6 @@ def test_log_returns_from_prices():
     new_nan = log_rets.isnull().sum(axis=1).sum()
     assert new_nan == old_nan
     np.testing.assert_almost_equal(log_rets.iloc[-1, -1], 0.0001682740081102576)
-    # Test the deprecated function, until it is removed.
-    deprecated_log_rets = expected_returns.log_returns_from_prices(df)
-    np.testing.assert_allclose(deprecated_log_rets, log_rets)
 
 
 def test_mean_historical_returns_dummy():
@@ -74,11 +71,11 @@ def test_mean_historical_returns_dummy():
     )
     mean = expected_returns.mean_historical_return(data, frequency=1)
     test_answer = pd.Series([0.0061922, 0.0241137, 0.0122722, -0.0421775])
-    pd.testing.assert_series_equal(mean, test_answer, check_less_precise=4)
+    pd.testing.assert_series_equal(mean, test_answer, rtol=1e-3)
 
     mean = expected_returns.mean_historical_return(data, compounding=False, frequency=1)
     test_answer = pd.Series([0.0086560, 0.0250000, 0.0128697, -0.03632333])
-    pd.testing.assert_series_equal(mean, test_answer, check_less_precise=4)
+    pd.testing.assert_series_equal(mean, test_answer, rtol=1e-3)
 
 
 def test_mean_historical_returns():
@@ -142,10 +139,11 @@ def test_ema_historical_return():
     assert mean.notnull().all()
     assert mean.dtype == "float64"
     # Test the (warning triggering) case that input is not a dataFrame
-    mean_np = expected_returns.ema_historical_return(df.to_numpy())
-    mean_np.name = mean.name  # These will differ.
-    reset_mean = mean.reset_index(drop=True)  # Index labels would be tickers.
-    pd.testing.assert_series_equal(mean_np, reset_mean)
+    with pytest.warns(RuntimeWarning):
+        mean_np = expected_returns.ema_historical_return(df.to_numpy())
+        mean_np.name = mean.name  # These will differ.
+        reset_mean = mean.reset_index(drop=True)  # Index labels would be tickers.
+        pd.testing.assert_series_equal(mean_np, reset_mean)
 
 
 def test_ema_historical_return_frequency():
@@ -195,10 +193,11 @@ def test_capm_no_benchmark():
     )
     np.testing.assert_array_almost_equal(mu.values, correct_mu)
     # Test the (warning triggering) case that input is not a dataFrame
-    mu_np = expected_returns.capm_return(df.to_numpy())
-    mu_np.name = mu.name  # These will differ.
-    mu_np.index = mu.index  # Index labels would be tickers.
-    pd.testing.assert_series_equal(mu_np, mu)
+    with pytest.warns(RuntimeWarning):
+        mu_np = expected_returns.capm_return(df.to_numpy())
+        mu_np.name = mu.name  # These will differ.
+        mu_np.index = mu.index  # Index labels would be tickers.
+        pd.testing.assert_series_equal(mu_np, mu)
 
 
 def test_capm_with_benchmark():