default risk free rate to 0.0, fixes robertmartin8#594

pypfopt/base_optimizer.py

@@ -62,6 +62,9 @@ def _make_output_weights(self, weights=None):
         if weights is None:
             weights = self.weights
 
+        # Convert numpy float64 to plain Python float
+        weights = [float(w) for w in weights]
+
         return collections.OrderedDict(zip(self.tickers, weights))
 
     def set_weights(self, input_weights):
@@ -510,7 +513,7 @@ def nonconvex_objective(
 
 
 def portfolio_performance(
-    weights, expected_returns, cov_matrix, verbose=False, risk_free_rate=0.02
+    weights, expected_returns, cov_matrix, verbose=False, risk_free_rate=0.0
 ):
     """
     After optimising, calculate (and optionally print) the performance of the optimal
@@ -525,7 +528,7 @@ def portfolio_performance(
     :type weights: list, np.array or dict, optional
     :param verbose: whether performance should be printed, defaults to False
     :type verbose: bool, optional
-    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02
+    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0
     :type risk_free_rate: float, optional
     :raises ValueError: if weights have not been calculated yet
     :return: expected return, volatility, Sharpe ratio.

pypfopt/black_litterman.py

@@ -17,7 +17,7 @@
 
 
 def market_implied_prior_returns(
-    market_caps, risk_aversion, cov_matrix, risk_free_rate=0.02
+    market_caps, risk_aversion, cov_matrix, risk_free_rate=0.0
 ):
     r"""
     Compute the prior estimate of returns implied by the market weights.
@@ -34,7 +34,7 @@ def market_implied_prior_returns(
     :type risk_aversion: positive float
     :param cov_matrix: covariance matrix of asset returns
     :type cov_matrix: pd.DataFrame
-    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                            You should use the appropriate time period, corresponding
                            to the covariance matrix.
     :type risk_free_rate: float, optional
@@ -52,7 +52,7 @@ def market_implied_prior_returns(
     return risk_aversion * cov_matrix.dot(mkt_weights) + risk_free_rate
 
 
-def market_implied_risk_aversion(market_prices, frequency=252, risk_free_rate=0.02):
+def market_implied_risk_aversion(market_prices, frequency=252, risk_free_rate=0.0):
     r"""
     Calculate the market-implied risk-aversion parameter (i.e market price of risk)
     based on market prices. For example, if the market has excess returns of 10% a year
@@ -68,7 +68,7 @@ def market_implied_risk_aversion(market_prices, frequency=252, risk_free_rate=0.
     :param frequency: number of time periods in a year, defaults to 252 (the number
                       of trading days in a year)
     :type frequency: int, optional
-    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                             The period of the risk-free rate should correspond to the
                             frequency of expected returns.
     :type risk_free_rate: float, optional
@@ -168,7 +168,7 @@ def __init__(
         :param market_caps: (kwarg) market caps for the assets, required if pi="market"
         :type market_caps: np.ndarray, pd.Series, optional
         :param risk_free_rate: (kwarg) risk_free_rate is needed in some methods
-        :type risk_free_rate: float, defaults to 0.02
+        :type risk_free_rate: float, defaults to 0.0
         """
         if sys.version_info[1] == 5:  # pragma: no cover
             warnings.warn(
@@ -268,7 +268,7 @@ def _set_pi(self, pi, **kwargs):
                     "Please pass a series/array of market caps via the market_caps keyword argument"
                 )
             market_caps = kwargs.get("market_caps")
-            risk_free_rate = kwargs.get("risk_free_rate", 0)
+            risk_free_rate = kwargs.get("risk_free_rate", 0.0)
 
             market_prior = market_implied_prior_returns(
                 market_caps, self.risk_aversion, self._raw_cov_matrix, risk_free_rate
@@ -406,7 +406,7 @@ def bl_returns(self):
         try:
             solution = np.linalg.solve(self._A, b)
         except np.linalg.LinAlgError as e:
-            if 'Singular matrix' in str(e):
+            if "Singular matrix" in str(e):
                 solution = np.linalg.lstsq(self._A, b, rcond=None)[0]
             else:
                 raise e
@@ -432,7 +432,7 @@ def bl_cov(self):
         try:
             M_solution = np.linalg.solve(self._A, b)
         except np.linalg.LinAlgError as e:
-            if 'Singular matrix' in str(e):
+            if "Singular matrix" in str(e):
                 M_solution = np.linalg.lstsq(self._A, b, rcond=None)[0]
             else:
                 raise e
@@ -465,7 +465,7 @@ def bl_weights(self, risk_aversion=None):
         try:
             weight_solution = np.linalg.solve(A, b)
         except np.linalg.LinAlgError as e:
-            if 'Singular matrix' in str(e):
+            if "Singular matrix" in str(e):
                 weight_solution = np.linalg.lstsq(self._A, b, rcond=None)[0]
             else:
                 raise e
@@ -477,15 +477,15 @@ def optimize(self, risk_aversion=None):
         """Alias for bl_weights for consistency with other methods."""
         return self.bl_weights(risk_aversion)
 
-    def portfolio_performance(self, verbose=False, risk_free_rate=0.02):
+    def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
         """
         After optimising, calculate (and optionally print) the performance of the optimal
         portfolio. Currently calculates expected return, volatility, and the Sharpe ratio.
         This method uses the BL posterior returns and covariance matrix.
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional

pypfopt/cla.py

@@ -443,14 +443,14 @@ def set_weights(self, _):
         # Overrides parent method since set_weights does nothing.
         raise NotImplementedError("set_weights does nothing for CLA")
 
-    def portfolio_performance(self, verbose=False, risk_free_rate=0.02):
+    def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
         """
         After optimising, calculate (and optionally print) the performance of the optimal
         portfolio. Currently calculates expected return, volatility, and the Sharpe ratio.
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02
+        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0
         :type risk_free_rate: float, optional
         :raises ValueError: if weights have not been calculated yet
         :return: expected return, volatility, Sharpe ratio.

pypfopt/efficient_frontier/efficient_cdar.py

@@ -107,7 +107,7 @@ def _validate_beta(beta):
     def min_volatility(self):
         raise NotImplementedError("Please use min_cdar instead.")
 
-    def max_sharpe(self, risk_free_rate=0.02):
+    def max_sharpe(self, risk_free_rate=0.0):
         raise NotImplementedError("Method not available in EfficientCDaR.")
 
     def max_quadratic_utility(self, risk_aversion=1, market_neutral=False):

pypfopt/efficient_frontier/efficient_cvar.py

@@ -107,7 +107,7 @@ def _validate_beta(beta):
     def min_volatility(self):
         raise NotImplementedError("Please use min_cvar instead.")
 
-    def max_sharpe(self, risk_free_rate=0.02):
+    def max_sharpe(self, risk_free_rate=0.0):
         raise NotImplementedError("Method not available in EfficientCVaR.")
 
     def max_quadratic_utility(self, risk_aversion=1, market_neutral=False):

pypfopt/efficient_frontier/efficient_frontier.py

@@ -220,15 +220,15 @@ def _max_return(self, return_value=True):
         else:
             return res
 
-    def max_sharpe(self, risk_free_rate=0.02):
+    def max_sharpe(self, risk_free_rate=0.0):
         """
         Maximise the Sharpe Ratio. The result is also referred to as the tangency portfolio,
         as it is the portfolio for which the capital market line is tangent to the efficient frontier.
 
         This is a convex optimization problem after making a certain variable substitution. See
         `Cornuejols and Tutuncu (2006) <http://web.math.ku.dk/~rolf/CT_FinOpt.pdf>`_ for more.
 
-        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional
@@ -419,14 +419,14 @@ def efficient_return(self, target_return, market_neutral=False):
             self._make_weight_sum_constraint(market_neutral)
         return self._solve_cvxpy_opt_problem()
 
-    def portfolio_performance(self, verbose=False, risk_free_rate=0.02):
+    def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
         """
         After optimising, calculate (and optionally print) the performance of the optimal
         portfolio. Currently calculates expected return, volatility, and the Sharpe ratio.
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional

pypfopt/efficient_frontier/efficient_semivariance.py

@@ -103,7 +103,7 @@ def __init__(
     def min_volatility(self):
         raise NotImplementedError("Please use min_semivariance instead.")
 
-    def max_sharpe(self, risk_free_rate=0.02):
+    def max_sharpe(self, risk_free_rate=0.0):
         raise NotImplementedError("Method not available in EfficientSemivariance")
 
     def min_semivariance(self, market_neutral=False):
@@ -246,14 +246,14 @@ def efficient_return(self, target_return, market_neutral=False):
             self._make_weight_sum_constraint(market_neutral)
         return self._solve_cvxpy_opt_problem()
 
-    def portfolio_performance(self, verbose=False, risk_free_rate=0.02):
+    def portfolio_performance(self, verbose=False, risk_free_rate=0.0):
         """
         After optimising, calculate (and optionally print) the performance of the optimal
         portfolio, specifically: expected return, semideviation, Sortino ratio.
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional

pypfopt/expected_returns.py

@@ -199,7 +199,7 @@ def capm_return(
     prices,
     market_prices=None,
     returns_data=False,
-    risk_free_rate=0.02,
+    risk_free_rate=0.0,
     compounding=True,
     frequency=252,
     log_returns=False,
@@ -221,7 +221,7 @@ def capm_return(
     :type market_prices: pd.DataFrame, optional
     :param returns_data: if true, the first arguments are returns instead of prices.
     :type returns_data: bool, defaults to False.
-    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                            You should use the appropriate time period, corresponding
                            to the frequency parameter.
     :type risk_free_rate: float, optional

pypfopt/hierarchical_portfolio.py

@@ -173,15 +173,15 @@ def optimize(self, linkage_method="single"):
         self.set_weights(weights)
         return weights
 
-    def portfolio_performance(self, verbose=False, risk_free_rate=0.02, frequency=252):
+    def portfolio_performance(self, verbose=False, risk_free_rate=0.0, frequency=252):
         """
         After optimising, calculate (and optionally print) the performance of the optimal
         portfolio. Currently calculates expected return, volatility, and the Sharpe ratio
         assuming returns are daily
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+        :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional

pypfopt/objective_functions.py

@@ -89,7 +89,7 @@ def portfolio_return(w, expected_returns, negative=True):
     return _objective_value(w, sign * mu)
 
 
-def sharpe_ratio(w, expected_returns, cov_matrix, risk_free_rate=0.02, negative=True):
+def sharpe_ratio(w, expected_returns, cov_matrix, risk_free_rate=0.0, negative=True):
     """
     Calculate the (negative) Sharpe ratio of a portfolio
 
@@ -99,7 +99,7 @@ def sharpe_ratio(w, expected_returns, cov_matrix, risk_free_rate=0.02, negative=
     :type expected_returns: np.ndarray
     :param cov_matrix: covariance matrix
     :type cov_matrix: np.ndarray
-    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02.
+    :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.0.
                            The period of the risk-free rate should correspond to the
                            frequency of expected returns.
     :type risk_free_rate: float, optional

tests/test_black_litterman.py

@@ -158,7 +158,7 @@ def test_bl_equal_prior():
 
     bl.bl_weights()
     np.testing.assert_allclose(
-        bl.portfolio_performance(),
+        bl.portfolio_performance(risk_free_rate=0.02),
         (0.1877432247395778, 0.3246889329226965, 0.5166274785827545),
     )
 
@@ -244,18 +244,18 @@ def test_market_risk_aversion():
     prices = pd.read_csv(
         resource("spy_prices.csv"), parse_dates=True, index_col=0
     ).squeeze("columns")
-    delta = market_implied_risk_aversion(prices)
+    delta = market_implied_risk_aversion(prices, risk_free_rate=0.02)
     assert np.round(delta, 5) == 2.68549
 
     # check it works for df
     prices = pd.read_csv(resource("spy_prices.csv"), parse_dates=True, index_col=0)
-    delta = market_implied_risk_aversion(prices)
+    delta = market_implied_risk_aversion(prices, risk_free_rate=0.02)
     assert np.round(delta.iloc[0], 5) == 2.68549
 
     # Check it raises for other types.
     list_invalid = [100.0, 110.0, 120.0, 130.0]
     with pytest.raises(TypeError):
-        delta = market_implied_risk_aversion(list_invalid)
+        delta = market_implied_risk_aversion(list_invalid, risk_free_rate=0.02)
 
 
 def test_bl_weights():
@@ -321,10 +321,10 @@ def test_market_implied_prior():
     prices = pd.read_csv(
         resource("spy_prices.csv"), parse_dates=True, index_col=0
     ).squeeze("columns")
-    delta = market_implied_risk_aversion(prices)
+    delta = market_implied_risk_aversion(prices, risk_free_rate=0.02)
 
     mcaps = get_market_caps()
-    pi = market_implied_prior_returns(mcaps, delta, S)
+    pi = market_implied_prior_returns(mcaps, delta, S, risk_free_rate=0.02)
     assert isinstance(pi, pd.Series)
     assert list(pi.index) == list(df.columns)
     assert pi.notnull().all()
@@ -358,7 +358,7 @@ def test_market_implied_prior():
     )
 
     mcaps = pd.Series(mcaps)
-    pi2 = market_implied_prior_returns(mcaps, delta, S)
+    pi2 = market_implied_prior_returns(mcaps, delta, S, risk_free_rate=0.02)
     pd.testing.assert_series_equal(pi, pi2, check_exact=False)
 
     # Test alternate syntax
@@ -368,8 +368,9 @@ def test_market_implied_prior():
         market_caps=mcaps,
         absolute_views={"AAPL": 0.1},
         risk_aversion=delta,
+        risk_free_rate=0.02,
     )
-    pi = market_implied_prior_returns(mcaps, delta, S, risk_free_rate=0)
+    pi = market_implied_prior_returns(mcaps, delta, S, risk_free_rate=0.02)
     np.testing.assert_array_almost_equal(bl.pi, pi.values.reshape(-1, 1))
 
 
@@ -381,14 +382,14 @@ def test_bl_market_prior():
         resource("spy_prices.csv"), parse_dates=True, index_col=0
     ).squeeze("columns")
 
-    delta = market_implied_risk_aversion(prices)
+    delta = market_implied_risk_aversion(prices, risk_free_rate=0.02)
 
     mcaps = get_market_caps()
 
     with pytest.warns(RuntimeWarning):
         market_implied_prior_returns(mcaps, delta, S.values)
 
-    prior = market_implied_prior_returns(mcaps, delta, S)
+    prior = market_implied_prior_returns(mcaps, delta, S, risk_free_rate=0.02)
 
     viewdict = {"GOOG": 0.40, "AAPL": -0.30, "FB": 0.30, "BABA": 0}
     bl = BlackLittermanModel(S, pi=prior, absolute_views=viewdict)
@@ -401,11 +402,11 @@ def test_bl_market_prior():
         )
 
     with pytest.raises(ValueError):
-        bl.portfolio_performance()
+        bl.portfolio_performance(risk_free_rate=0.02)
 
     bl.bl_weights(delta)
     np.testing.assert_allclose(
-        bl.portfolio_performance(),
+        bl.portfolio_performance(risk_free_rate=0.02),
         (0.2580693114409672, 0.265445955488424, 0.8968654692926723),
     )
     # Check that bl.cov() has been called and used
@@ -657,11 +658,11 @@ def test_idzorek_with_priors():
     np.testing.assert_almost_equal(rets["AAPL"], -0.3)
 
     with pytest.raises(ValueError):
-        bl.portfolio_performance()
+        bl.portfolio_performance(risk_free_rate=0.02)
 
     bl.bl_weights()
     np.testing.assert_allclose(
-        bl.portfolio_performance(),
+        bl.portfolio_performance(risk_free_rate=0.02),
         (0.943431295405105, 0.5361412623208567, 1.722365653051476),
     )
     # Check that bl.cov() has been called and used