updated docs

docs/Roadmap.rst

@@ -45,6 +45,11 @@ have any other feature requests, please raise them using GitHub
 
 Fixed minor issues in CLA: weight bound bug, ``efficient_frontier`` needed weights to be called, ``set_weights`` not needed.
 
+1.0.2
+-----
+
+Fixed small but important bug where passing ``expected_returns=None`` fails. According to the docs, users
+should be able to only pass covariance if they want to only optimise min volatility.
 
 0.5.0
 =====

pypfopt/base_optimizer.py

@@ -351,8 +351,8 @@ def portfolio_performance(
     After optimising, calculate (and optionally print) the performance of the optimal
     portfolio. Currently calculates expected return, volatility, and the Sharpe ratio.
 
-    :param expected_returns: expected returns for each asset. Set to None if
-                             optimising for volatility only.
+    :param expected_returns: expected returns for each asset. Can be None if
+                             optimising for volatility only (but not recommended).
     :type expected_returns: np.ndarray or pd.Series
     :param cov_matrix: covariance of returns for each asset
     :type cov_matrix: np.array or pd.DataFrame

pypfopt/efficient_frontier.py

@@ -55,8 +55,8 @@ class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
 
     def __init__(self, expected_returns, cov_matrix, weight_bounds=(0, 1), gamma=0):
         """
-        :param expected_returns: expected returns for each asset. Set to None if
-                                 optimising for volatility only.
+        :param expected_returns: expected returns for each asset. Can be None if
+                                optimising for volatility only (but not recommended).
         :type expected_returns: pd.Series, list, np.ndarray
         :param cov_matrix: covariance of returns for each asset
         :type cov_matrix: pd.DataFrame or np.array