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migrated max_sharpe to cvxpy

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robertmartin8
2020-03-15 12:59:01 +00:00
parent a6b8df3bcb
commit 8b337fa3af
6 changed files with 390 additions and 248 deletions
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43
pypfopt/base_optimizer.py
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@@ -128,7 +128,10 @@ class BaseConvexOptimizer(BaseOptimizer):
self._w = cp.Variable(n_assets)
self._objective = None
self._additional_objectives = []
self._additional_constraints_raw = []
self._constraints = []
self._lower_bounds = None
self._upper_bounds = None
self._map_bounds_to_constraints(weight_bounds)
def _map_bounds_to_constraints(self, test_bounds):
@@ -147,8 +150,8 @@ class BaseConvexOptimizer(BaseOptimizer):
test_bounds[0], (float, int)
):
bounds = np.array(test_bounds, dtype=np.float)
lower = np.nan_to_num(bounds[:, 0], nan=-np.inf)
upper = np.nan_to_num(bounds[:, 1], nan=np.inf)
self._lower_bounds = np.nan_to_num(bounds[:, 0], nan=-np.inf)
self._upper_bounds = np.nan_to_num(bounds[:, 1], nan=np.inf)
else:
# Otherwise this must be a pair.
if len(test_bounds) != 2 or not isinstance(test_bounds, (tuple, list)):
@@ -161,13 +164,15 @@ class BaseConvexOptimizer(BaseOptimizer):
# Replace None values with the appropriate infinity.
if np.isscalar(lower) or lower is None:
lower = -np.inf if lower is None else lower
self._lower_bounds = np.array([lower] * self.n_assets)
upper = np.inf if upper is None else upper
self._upper_bounds = np.array([upper] * self.n_assets)
else:
lower = np.nan_to_num(lower, nan=-np.inf)
upper = np.nan_to_num(upper, nan=np.inf)
self._lower_bounds = np.nan_to_num(lower, nan=-np.inf)
self._upper_bounds = np.nan_to_num(upper, nan=np.inf)
self._constraints.append(self._w >= lower)
self._constraints.append(self._w <= upper)
self._constraints.append(self._w >= self._lower_bounds)
self._constraints.append(self._w <= self._upper_bounds)
@staticmethod
def _make_scipy_bounds():
@@ -178,7 +183,7 @@ class BaseConvexOptimizer(BaseOptimizer):
def portfolio_performance(
expected_returns, cov_matrix, weights, verbose=False, risk_free_rate=0.02
weights, expected_returns, cov_matrix, verbose=False, risk_free_rate=0.02
):
"""
After optimising, calculate (and optionally print) the performance of the optimal
@@ -186,9 +191,9 @@ def portfolio_performance(
:param expected_returns: expected returns for each asset. Set to None if
optimising for volatility only.
:type expected_returns: pd.Series, list, np.ndarray
:type expected_returns: np.ndarray or pd.Series
:param cov_matrix: covariance of returns for each asset
:type cov_matrix: pd.DataFrame or np.array
:type cov_matrix: np.array or pd.DataFrame
:param weights: weights or assets
:type weights: list, np.array or dict, optional
:param verbose: whether performance should be printed, defaults to False
@@ -218,11 +223,23 @@ def portfolio_performance(
raise ValueError("Weights is None")
sigma = np.sqrt(objective_functions.portfolio_variance(new_weights, cov_matrix))
mu = new_weights.dot(expected_returns)
sharpe = -objective_functions.negative_sharpe(
new_weights, expected_returns, cov_matrix, risk_free_rate=risk_free_rate
mu = objective_functions.portfolio_return(
new_weights, expected_returns, negative=False
)
# new_weights.dot(expected_returns)
# sharpe = -objective_functions.negative_sharpe(
# new_weights, expected_returns, cov_matrix, risk_free_rate=risk_free_rate
# )
sharpe = objective_functions.sharpe_ratio(
new_weights,
expected_returns,
cov_matrix,
risk_free_rate=risk_free_rate,
negative=False,
)
if verbose:
print("Expected annual return: {:.1f}%".format(100 * mu))
print("Annual volatility: {:.1f}%".format(100 * sigma))