misc refactors

pypfopt/__init__.py

@@ -6,7 +6,8 @@ from .black_litterman import (
 from .cla import CLA
 from .discrete_allocation import get_latest_prices, DiscreteAllocation
 from .efficient_frontier import EfficientFrontier
-from .hierarchical_risk_parity import HRPOpt
+from .hierarchical_portfolios import HRPOpt
+from .risk_models import CovarianceShrinkage
 
 __all__ = [
     "market_implied_prior_returns",
@@ -17,4 +18,5 @@ __all__ = [
     "DiscreteAllocation",
     "EfficientFrontier",
     "HRPOpt",
+    "CovarianceShrinkage",
 ]

pypfopt/base_optimizer.py

@@ -205,7 +205,7 @@ class BaseConvexOptimizer(BaseOptimizer):
         Add a new term into the objective function. This term must be convex,
         and built from cvxpy atomic functions.
 
-        Example:
+        Example::
 
             def L1_norm(w, k=1):
                 return k * cp.norm(w, 1)
@@ -222,7 +222,7 @@ class BaseConvexOptimizer(BaseOptimizer):
         Add a new constraint to the optimisation problem. This constraint must be linear and
         must be either an equality or simple inequality.
 
-        Examples:
+        Examples::
 
             ef.add_constraint(lambda x : x[0] == 0.02)
             ef.add_constraint(lambda x : x >= 0.01)
@@ -242,7 +242,7 @@ class BaseConvexOptimizer(BaseOptimizer):
     def convex_objective(self, custom_objective, weights_sum_to_one=True, **kwargs):
         """
         Optimise a custom convex objective function. Constraints should be added with
-        ``ef.add_constraint()``. Optimiser arguments *must* be passed as keyword-args. Example:
+        ``ef.add_constraint()``. Optimiser arguments *must* be passed as keyword-args. Example::
 
             # Could define as a lambda function instead
             def logarithmic_barrier(w, cov_matrix, k=0.1):
@@ -283,7 +283,7 @@ 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:
+        at local minima. This method is not recommended – caveat emptor. Example::
 
             # Market-neutral efficient risk
             constraints = [

pypfopt/black_litterman.py

@@ -87,7 +87,7 @@ class BlackLittermanModel(base_optimizer.BaseOptimizer):
 
     - Inputs:
 
-        - ``cov_matrix`` - pd.DataFrame
+        - ``cov_matrix`` - np.ndarray
         - ``n_assets`` - int
         - ``tickers`` - str list
         - ``Q`` - np.ndarray
@@ -341,9 +341,9 @@ class BlackLittermanModel(base_optimizer.BaseOptimizer):
         if self.posterior_cov is None:
             self.posterior_cov = self.bl_cov()
         return base_optimizer.portfolio_performance(
+            self.weights,
             self.posterior_rets,
             self.posterior_cov,
-            self.weights,
             verbose,
             risk_free_rate,
         )

pypfopt/cla.py

@@ -21,8 +21,8 @@ class CLA(base_optimizer.BaseOptimizer):
         - ``n_assets`` - int
         - ``tickers`` - str list
         - ``mean`` - np.ndarray
-        - ``cov_matrix`` - pd.DataFrame
-        - ``expected_returns`` - pd.Series
+        - ``cov_matrix`` - np.ndarray
+        - ``expected_returns`` - np.ndarray
         - ``lb`` - np.ndarray
         - ``ub`` - np.ndarray
 
@@ -447,9 +447,9 @@ class CLA(base_optimizer.BaseOptimizer):
         :rtype: (float, float, float)
         """
         return base_optimizer.portfolio_performance(
+            self.weights,
             self.expected_returns,
             self.cov_matrix,
-            self.weights,
             verbose,
             risk_free_rate,
         )