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Fix minor mismatches of type hints.
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@@ -85,14 +85,14 @@ class PortfolioOptimizer:
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if u is not None:
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assert len(u) == len(S), "`u` has mismatched shape"
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if isinstance(u, pd.Series):
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assert all(u.index == index), "`u` has mismatched index"
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assert u.index.equals(index), "`u` has mismatched index"
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u = u.values
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# transform initial weights
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if w0 is not None:
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assert len(w0) == len(S), "`w0` has mismatched shape"
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if isinstance(w0, pd.Series):
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assert all(w0.index == index), "`w0` has mismatched index"
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assert w0.index.equals(index), "`w0` has mismatched index"
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w0 = w0.values
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# scale alpha to match volatility
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@@ -175,7 +175,7 @@ class PortfolioOptimizer:
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"""
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return self._solve(len(S), self._get_objective_rp(S), *self._get_constrains(w0))
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def _get_objective_gmv(self, S: np.ndarray) -> np.ndarray:
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def _get_objective_gmv(self, S: np.ndarray) -> Callable:
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"""global minimum variance optimization objective
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Optimization objective
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@@ -187,7 +187,7 @@ class PortfolioOptimizer:
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return func
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def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> np.ndarray:
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def _get_objective_mvo(self, S: np.ndarray, u: np.ndarray = None) -> Callable:
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"""mean-variance optimization objective
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Optimization objective
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@@ -201,7 +201,7 @@ class PortfolioOptimizer:
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return func
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def _get_objective_rp(self, S: np.ndarray) -> np.ndarray:
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def _get_objective_rp(self, S: np.ndarray) -> Callable:
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"""risk-parity optimization objective
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Optimization objective
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