Coverage for src / cvx / markowitz / models / trading_costs.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"""Model for trading costs.""" 

15 

16from __future__ import annotations 

17 

18from dataclasses import dataclass 

19 

20import cvxpy as cp 

21import numpy as np 

22 

23from cvx.markowitz.model import Model 

24from cvx.markowitz.names import DataNames as D 

25from cvx.markowitz.types import Expressions, Matrix, Parameter, Variables # noqa: F401 

26from cvx.markowitz.utils.fill import fill_vector 

27 

28 

29@dataclass(frozen=True) 

30class TradingCosts(Model): 

31 """Model for trading costs.""" 

32 

33 def __post_init__(self) -> None: 

34 """Initialize trading cost parameters and previous-weights cache.""" 

35 self.parameter["power"] = cp.Parameter(shape=1, name="power", value=np.ones(1)) 

36 

37 # initial weights before rebalancing 

38 self.data["weights"] = cp.Parameter(shape=self.assets, name="weights", value=np.zeros(self.assets)) 

39 

40 def estimate(self, variables: Variables) -> cp.Expression: 

41 """Estimate trading costs for a rebalance. 

42 

43 Args: 

44 variables: Optimization variables, expected to contain D.WEIGHTS. 

45 

46 Returns: 

47 A convex expression representing the p-power cost of trades 

48 between current and previous weights. 

49 """ 

50 return cp.sum( 

51 cp.power( 

52 cp.abs(variables[D.WEIGHTS] - self.data["weights"]), 

53 p=self.parameter["power"], 

54 ) 

55 ) 

56 

57 def update(self, **kwargs: Matrix) -> None: 

58 """Update cached data values. 

59 

60 Expected keyword arguments: 

61 weights: Vector of previous weights used as the trading baseline. 

62 """ 

63 self.data["weights"].value = fill_vector(num=self.assets, x=kwargs["weights"])