text: # pylint: disable=missing-module-docstring
  import numpy as np
  import pandas as pd
  
  
  class RiskMetrics:
      """
      This class contains methods for calculating common risk metrics used in trading and asset management.
      """
  
      def __init__(self):
  
          pass
  
      @staticmethod
      def calculate_variance(covariance, weights):
          """
          Calculate the variance of a portfolio.
  
          :param covariance: (pd.DataFrame/np.matrix) Covariance matrix of assets
          :param weights: (list) List of asset weights
          :return: (float) Variance of a portfolio
          """
  
  
          pass
  
      @staticmethod
      def calculate_value_at_risk(returns, confidence_level=0.05):
          """
          Calculate the value at risk (VaR) of a portfolio/asset.
  
          :param returns: (pd.DataFrame/np.array) Historical returns for an asset / portfolio
          :param confidence_level: (float) Confidence level (alpha)
          :return: (float) VaR
          """
  
          pass
  
      def calculate_expected_shortfall(self, returns, confidence_level=0.05):
          """
          Calculate the expected shortfall (CVaR) of a portfolio/asset.
  
          :param returns: (pd.DataFrame/np.array) Historical returns for an asset / portfolio
          :param confidence_level: (float) Confidence level (alpha)
          :return: (float) Expected shortfall
          """
  
          pass
  
      @staticmethod
      def calculate_conditional_drawdown_risk(returns, confidence_level=0.05):
          """
          Calculate the conditional drawdown of risk (CDaR) of a portfolio/asset.
  
          :param returns: (pd.DataFrame/np.array) Historical returns for an asset / portfolio
          :param confidence_level: (float) Confidence level (alpha)
          :return: (float) Conditional drawdown risk
          """
  
          pass
  