Coverage for src/cvxmarkowitz/portfolios/min_var.py: 100%

1# Copyright 2023 Stanford University Convex Optimization Group 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14"""Minimum-variance portfolio builder.""" 

15 

16from __future__ import annotations 

17 

18from dataclasses import dataclass 

19 

20import cvxpy as cp 

21 

22from cvxmarkowitz.builder import Builder 

23from cvxmarkowitz.names import ConstraintName as C 

24 

25 

26@dataclass(frozen=True) 

27class MinVar(Builder): 

28 """Construct a long-only, budget-constrained minimum-variance portfolio. 

29 

30 Example: 

31 >>> import numpy as np 

32 >>> from cvxmarkowitz.names import DataNames as D 

33 >>> problem = MinVar(assets=4).build() 

34 >>> problem.update( 

35 ... **{ 

36 ... D.CHOLESKY: np.linalg.cholesky(np.array([[1.0, 0.5], [0.5, 2.0]])).T, 

37 ... D.LOWER_BOUND_ASSETS: np.zeros(2), 

38 ... D.UPPER_BOUND_ASSETS: np.ones(2), 

39 ... D.VOLA_UNCERTAINTY: np.zeros(2), 

40 ... } 

41 ... ) 

42 >>> round(problem.solve(), 4) 

43 0.9354 

44 >>> round(float(problem.weights.sum()), 6) 

45 1.0 

46 """ 

47 

48 @property 

49 def objective(self) -> cp.Minimize: 

50 """Return the CVXPY objective for minimizing portfolio risk.""" 

51 return cp.Minimize(self.risk.estimate(self.variables)) 

52 

53 def __post_init__(self) -> None: 

54 """Set up default constraints for the minimum-variance portfolio.""" 

55 super().__post_init__() 

56 self.constraints[C.LONG_ONLY] = self.weights >= 0 

57 self.constraints[C.BUDGET] = cp.sum(self.weights) == 1.0