code: def var_cov_var_normal(P, c, mu=0, sigma=1):
      """
      Variance-covariance calculation of daily Value-at-Risk in a
      portfolio.
  
      Parameters
      ----------
      P : float
          Portfolio value.
      c : float
          Confidence level.
      mu : float, optional
          Mean.
  
      Returns
      -------
      float
          Variance-covariance.
      """
  
      alpha = sp.stats.norm.ppf(1 - c, mu, sigma)
      return P - P * (alpha + 1)
  
imports: from __future__ import division
  from collections import OrderedDict
  from functools import partial
  import empyrical
  import numpy
  import pandas
  import scipy
  import scipy.stats
  from sklearn import linear_model
  from .deprecate import deprecated
  from .interesting_periods import PERIODS
  from .txn import get_turnover
  from .utils import APPROX_BDAYS_PER_MONTH, APPROX_BDAYS_PER_YEAR
  from .utils import DAILY
label_desc: Use image node_id 1 for calling a global function with example usage: var_cov_var_normal(P, c, mu, sigma) and returns: unknown