fixes #82 – pass None as expected returns

pypfopt/base_optimizer.py

@@ -374,6 +374,7 @@ def portfolio_performance(
         else:
             tickers = list(range(len(expected_returns)))
         new_weights = np.zeros(len(tickers))
+
         for i, k in enumerate(tickers):
             if k in weights:
                 new_weights[i] = weights[k]
@@ -385,25 +386,25 @@ def portfolio_performance(
         raise ValueError("Weights is None")
 
     sigma = np.sqrt(objective_functions.portfolio_variance(new_weights, cov_matrix))
-    mu = objective_functions.portfolio_return(
-        new_weights, expected_returns, negative=False
-    )
-    # new_weights.dot(expected_returns)
 
-    # sharpe = -objective_functions.negative_sharpe(
-    #     new_weights, expected_returns, cov_matrix, risk_free_rate=risk_free_rate
-    # )
+    if expected_returns is not None:
+        mu = objective_functions.portfolio_return(
+            new_weights, expected_returns, negative=False
+        )
 
-    sharpe = objective_functions.sharpe_ratio(
-        new_weights,
-        expected_returns,
-        cov_matrix,
-        risk_free_rate=risk_free_rate,
-        negative=False,
-    )
-
-    if verbose:
-        print("Expected annual return: {:.1f}%".format(100 * mu))
-        print("Annual volatility: {:.1f}%".format(100 * sigma))
-        print("Sharpe Ratio: {:.2f}".format(sharpe))
-    return mu, sigma, sharpe
+        sharpe = objective_functions.sharpe_ratio(
+            new_weights,
+            expected_returns,
+            cov_matrix,
+            risk_free_rate=risk_free_rate,
+            negative=False,
+        )
+        if verbose:
+            print("Expected annual return: {:.1f}%".format(100 * mu))
+            print("Annual volatility: {:.1f}%".format(100 * sigma))
+            print("Sharpe Ratio: {:.2f}".format(sharpe))
+        return mu, sigma, sharpe
+    else:
+        if verbose:
+            print("Annual volatility: {:.1f}%".format(100 * sigma))
+        return None, sigma, None

pypfopt/efficient_frontier.py

@@ -84,15 +84,19 @@ class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
         else:  # use integer labels
             tickers = list(range(len(expected_returns)))
 
-        if cov_matrix.shape != (len(expected_returns), len(expected_returns)):
-            raise ValueError("Covariance matrix does not match expected returns")
+        if expected_returns is not None:
+            if cov_matrix.shape != (len(expected_returns), len(expected_returns)):
+                raise ValueError("Covariance matrix does not match expected returns")
 
         super().__init__(len(tickers), tickers, weight_bounds)
 
     @staticmethod
     def _validate_expected_returns(expected_returns):
         if expected_returns is None:
-            raise ValueError("expected_returns must be provided")
+            warnings.warn(
+                "No expected returns provided. You may only use ef.min_volatility()"
+            )
+            return None
         elif isinstance(expected_returns, pd.Series):
             return expected_returns.values
         elif isinstance(expected_returns, list):

tests/test_efficient_frontier.py

@@ -65,6 +65,19 @@ def test_min_volatility():
     )
 
 
+def test_min_volatility_no_rets():
+    # Should work with no rets, see issue #82
+    df = get_data()
+    S = risk_models.sample_cov(df)
+    ef = EfficientFrontier(None, S)
+    w = ef.min_volatility()
+    assert isinstance(w, dict)
+    assert set(w.keys()) == set(ef.tickers)
+    np.testing.assert_almost_equal(ef.weights.sum(), 1)
+    assert all([i >= 0 for i in w.values()])
+    np.testing.assert_almost_equal(ef.portfolio_performance()[1], 0.15915084514118694)
+
+
 def test_min_volatility_tx_costs():
     # Baseline
     ef = setup_efficient_frontier()