Constraining a score¶
Suppose that for each asset you have some “score” – it could be an ESG metric, or some custom risk/return metric. It is simple to specify linear constraints, like “portfolio ESG score must be greater than x”: you simply create a vector of scores, add a constraint on the dot product of those scores with the portfolio weights, then optimize your objective:
esg_scores = [0.3, 0.1, 0.4, 0.1, 0.5, 0.9, 0.2] portfolio_min_score = 0.5 ef = EfficientFrontier(mu, S) ef.add_constraint(lambda w: esg_scores @ w >= portfolio_min_score) ef.min_volatility()
Constraining the number of assets¶
Unfortunately, cardinality constraints are not convex, making them difficult to implement.
However, we can treat it as a mixed-integer program and solve (provided you have access to a solver).
for small problems with less than 1000 variables and constraints, you can use the community version of CPLEX:
pip install cplex. In the below example, we limit the portfolio to at most 10 assets:
import cvxpy as cp ef = EfficientFrontier(mu, S, solver=cp.CPLEX) booleans = cp.Variable(len(ef.tickers), boolean=True) ef.add_constraint(lambda x: x <= booleans) ef.add_constraint(lambda x: cp.sum(booleans) <= 10) ef.min_volatility()
This does not play well with
max_sharpe, and needs to be modified for different bounds.
See this issue for further discussion.
Tracking error can either be used as an objective (as described in General Efficient Frontier) or as a constraint. This is an example of adding a tracking error constraint:
from objective functions import ex_ante_tracking_error benchmark_weights = ... # benchmark ef = EfficientFrontier(mu, S) ef.add_constraint(ex_ante_tracking_error, cov_matrix=ef.cov_matrix, benchmark_weights=benchmark_weights) ef.min_volatility()