removed CVaR entirely

pypfopt/objective_functions.py

@@ -23,7 +23,6 @@ Currently implemented:
 - Sharpe ratio
 - L2 regularisation (minimising this reduces nonzero weights)
 - Quadratic utility
-# - negative CVaR (expected shortfall). Caveat emptor: this is very buggy.
 """
 
 import numpy as np
@@ -151,34 +150,3 @@ def quadratic_utility(w, expected_returns, cov_matrix, risk_aversion, negative=T
     utility = mu - 0.5 * risk_aversion * variance
     return _objective_value(w, sign * utility)
 
-
-# def negative_cvar(weights, returns, s=10000, beta=0.95, random_state=None):
-#     """
-#     Calculate the negative CVaR. Though we want the "min CVaR portfolio", we
-#     actually need to maximise the expected return of the worst q% cases, thus
-#     we need this value to be negative.
-
-#     :param weights: asset weights of the portfolio
-#     :type weights: np.ndarray
-#     :param returns: asset returns
-#     :type returns: pd.DataFrame or np.ndarray
-#     :param s: number of bootstrap draws, defaults to 10000
-#     :type s: int, optional
-#     :param beta: "significance level" (i. 1 - q), defaults to 0.95
-#     :type beta: float, optional
-#     :param random_state: seed for random sampling, defaults to None
-#     :type random_state: int, optional
-#     :return: negative CVaR
-#     :rtype: float
-#     """
-#     import scipy.stats
-#     np.random.seed(seed=random_state)
-#     # Calcualte the returns given the weights
-#     portfolio_returns = (weights * returns).sum(axis=1)
-#     # Sample from the historical distribution
-#     dist = scipy.stats.gaussian_kde(portfolio_returns)
-#     sample = dist.resample(s)
-#     # Calculate the value at risk
-#     var = portfolio_returns.quantile(1 - beta)
-#     # Mean of all losses worse than the value at risk
-#     return -sample[sample < var].mean()