code: def negative_sharpe(
      weights, expected_returns, cov_matrix, gamma=0, risk_free_rate=0.02
  ):
      
      mu = weights.dot(expected_returns)
      sigma = np.sqrt(np.dot(weights, np.dot(cov_matrix, weights.T)))
      L2_reg = gamma * (weights ** 2).sum()
      return -(mu - risk_free_rate) / sigma + L2_reg
docs: Calculate the negative Sharpe ratio of a portfolio
  
      :param weights: asset weights of the portfolio
      :type weights: np.ndarray
      :param expected_returns: expected return of each asset
      :type expected_returns: pd.Series
      :param cov_matrix: the covariance matrix of asset returns
      :type cov_matrix: pd.DataFrame
      :param gamma: L2 regularisation parameter, defaults to 0. Increase if you want more
                      non-negligible weights
      :type gamma: float, optional
      :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02
      :type risk_free_rate: float, optional
      :return: negative Sharpe ratio
      :rtype: float