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misc refactors

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robertmartin8
2020-03-17 16:47:10 +00:00
parent c5ce4cb353
commit 7654610b05
4 changed files with 38 additions and 36 deletions
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60
pypfopt/base_optimizer.py
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@@ -1,6 +1,6 @@
"""
The ``base_optimizer`` module houses the parent classes ``BaseOptimizer`` from which all
optimisers will inherit. ``BaseConvexOptimizer`` is thebase class for all ``cvxpy`` (and ``scipy``)
optimisers will inherit. ``BaseConvexOptimizer`` is the base class for all ``cvxpy`` (and ``scipy``)
optimisation.
Additionally, we define a general utility function ``portfolio_performance`` to
@@ -205,12 +205,12 @@ 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)
def L1_norm(w, k=1):
return k * cp.norm(w, 1)
ef.add_objective(L1_norm, k=2)
ef.add_objective(L1_norm, k=2)
:param new_objective: the objective to be added
:type new_objective: cp.Expression (i.e function of cp.Variable)
@@ -222,11 +222,11 @@ 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)
ef.add_constraint(lambda x: x <= np.array([0.01, 0.08, ..., 0.5]))
ef.add_constraint(lambda x : x[0] == 0.02)
ef.add_constraint(lambda x : x >= 0.01)
ef.add_constraint(lambda x: x <= np.array([0.01, 0.08, ..., 0.5]))
:param new_constraint: the constraint to be added
:type constraintfunc: lambda function
@@ -242,14 +242,14 @@ 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):
# 60 Years of Portfolio Optimisation, Kolm et al (2014)
return cp.quad_form(w, cov_matrix) - k * cp.sum(cp.log(w))
# Could define as a lambda function instead
def logarithmic_barrier(w, cov_matrix, k=0.1):
# 60 Years of Portfolio Optimisation, Kolm et al (2014)
return cp.quad_form(w, cov_matrix) - k * cp.sum(cp.log(w))
w = ef.convex_objective(logarithmic_barrier, cov_matrix=ef.cov_matrix)
w = ef.convex_objective(logarithmic_barrier, cov_matrix=ef.cov_matrix)
:param custom_objective: an objective function to be MINIMISED. This should be written using
cvxpy atoms Should map (w, **kwargs) -> float.
@@ -283,22 +283,22 @@ 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 = [
{"type": "eq", "fun": lambda w: np.sum(w)}, # weights sum to zero
{
"type": "eq",
"fun": lambda w: target_risk ** 2 - np.dot(w.T, np.dot(ef.cov_matrix, w)),
}, # risk = target_risk
]
ef.nonconvex_objective(
lambda w, mu: -w.T.dot(mu), # min negative return (i.e maximise return)
objective_args=(ef.expected_returns,),
weights_sum_to_one=False,
constraints=constraints,
)
# Market-neutral efficient risk
constraints = [
{"type": "eq", "fun": lambda w: np.sum(w)}, # weights sum to zero
{
"type": "eq",
"fun": lambda w: target_risk ** 2 - np.dot(w.T, np.dot(ef.cov_matrix, w)),
}, # risk = target_risk
]
ef.nonconvex_objective(
lambda w, mu: -w.T.dot(mu), # min negative return (i.e maximise return)
objective_args=(ef.expected_returns,),
weights_sum_to_one=False,
constraints=constraints,
)
:param objective_function: an objective function to be MINIMISED. This function
should map (weight, args) -> cost