improves risk_free_rate usability #238

pypfopt/base_optimizer.py

@@ -45,6 +45,7 @@ class BaseOptimizer:
             self.tickers = list(range(n_assets))
         else:
             self.tickers = tickers
+        self._risk_free_rate = None
         # Outputs
         self.weights = None
 
@@ -358,8 +359,8 @@ class BaseConvexOptimizer(BaseOptimizer):
     ):
         """
         Optimise some objective function using the scipy backend. This can
-        support nonconvex objectives and nonlinear constraints, but often gets stuck
-        at local minima. This method is not recommended – caveat emptor. Example::
+        support nonconvex objectives and nonlinear constraints, but may get stuck
+        at local minima. Example::
 
             # Market-neutral efficient risk
             constraints = [

pypfopt/efficient_frontier.py

@@ -191,6 +191,7 @@ class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
         """
         if not isinstance(risk_free_rate, (int, float)):
             raise ValueError("risk_free_rate should be numeric")
+        self._risk_free_rate = risk_free_rate
 
         # max_sharpe requires us to make a variable transformation.
         # Here we treat w as the transformed variable.
@@ -379,6 +380,9 @@ class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
         :return: expected return, volatility, Sharpe ratio.
         :rtype: (float, float, float)
         """
+        if self._risk_free_rate is not None:
+            risk_free_rate = self._risk_free_rate
+
         return base_optimizer.portfolio_performance(
             self.weights,
             self.expected_returns,

tests/test_efficient_frontier.py

@@ -502,6 +502,16 @@ def test_max_sharpe_risk_free_rate():
     assert new_sharpe >= initial_sharpe
 
 
+def test_max_sharpe_risk_free_portfolio_performance():
+    # Issue #238 - portfolio perf should use the same rf as
+    # 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)
+
+
 def test_min_vol_pair_constraint():
     ef = setup_efficient_frontier()
     ef.min_volatility()