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updated HRPOpt to prefer the supplied covariance matrix #638

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updated HRPOpt to prefer the supplied covariance matrix #638

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11 changes: 7 additions & 4 deletions pypfopt/hierarchical_portfolio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@ def optimize(self, linkage_method="single"):
if linkage_method not in sch._LINKAGE_METHODS:
raise ValueError("linkage_method must be one recognised by scipy")

if self.returns is None:
if self.cov_matrix is not None:
cov = self.cov_matrix
corr = risk_models.cov_to_corr(self.cov_matrix).round(6)
else:
Expand All @@ -176,7 +176,7 @@ def optimize(self, linkage_method="single"):
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
portfolio. Currently, calculates expected return, volatility, and the Sharpe ratio
assuming returns are daily

:param verbose: whether performance should be printed, defaults to False
Expand All @@ -192,9 +192,12 @@ def portfolio_performance(self, verbose=False, risk_free_rate=0.0, frequency=252
:return: expected return, volatility, Sharpe ratio.
:rtype: (float, float, float)
"""
if self.returns is None:
if self.cov_matrix is not None:
cov = self.cov_matrix
mu = None
if self.returns is not None:
mu = self.returns.mean() * frequency
else:
mu = None
else:
cov = self.returns.cov() * frequency
mu = self.returns.mean() * frequency
Expand Down