code: def factor_alpha_beta(
      factor_data,
      returns=None,
      demeaned=True,
      group_adjust=False,
      equal_weight=False,
  ):
      """
      Compute the alpha (excess returns), alpha t-stat (alpha significance),
      and beta (market exposure) of a factor. A regression is run with
      the period wise factor universe mean return as the independent variable
      and mean period wise return from a portfolio weighted by factor values
      as the dependent variable.
  
      Parameters
      ----------
      factor_data : pd.DataFrame - MultiIndex
          A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
          containing the values for a single alpha factor, forward returns for
          each period, the factor quantile/bin that factor value belongs to, and
          (optionally) the group the asset belongs to.
          - See full explanation in utils.get_clean_factor_and_forward_returns
      returns : pd.DataFrame, optional
          Period wise factor returns. If this is None then it will be computed
          with 'factor_returns' function and the passed flags: 'demeaned',
          'group_adjust', 'equal_weight'
      demeaned : bool
          Control how to build factor returns used for alpha/beta computation
          -- see performance.factor_return for a full explanation
      group_adjust : bool
          Control how to build factor returns used for alpha/beta computation
          -- see performance.factor_return for a full explanation
      equal_weight : bool, optional
          Control how to build factor returns used for alpha/beta computation
          -- see performance.factor_return for a full explanation
  
      Returns
      -------
      alpha_beta : pd.Series
          A list containing the alpha, beta, a t-stat(alpha)
          for the given factor and forward returns.
      """
  
      if returns is None:
          returns = factor_returns(
              factor_data, demeaned, group_adjust, equal_weight
          )
  
      universe_ret = (
          factor_data.groupby(level="date")[
              utils.get_forward_returns_columns(factor_data.columns)
          ]
          .mean()
          .loc[returns.index]
      )
  
      if isinstance(returns, pd.Series):
          returns.name = universe_ret.columns.values[0]
          returns = pd.DataFrame(returns)
  
      alpha_beta = pd.DataFrame()
      for period in returns.columns.values:
          x = universe_ret[period].values
          y = returns[period].values
          x = add_constant(x)
  
          reg_fit = OLS(y, x).fit()
          try:
              alpha, beta = reg_fit.params
          except ValueError:
              alpha_beta.loc["Ann. alpha", period] = np.nan
              alpha_beta.loc["beta", period] = np.nan
          else:
              freq_adjust = pd.Timedelta("252Days") / pd.Timedelta(
                  period
              )
  
              alpha_beta.loc["Ann. alpha", period] = (
                  1 + alpha
              ) ** freq_adjust - 1
              alpha_beta.loc["beta", period] = beta
  
      return alpha_beta
  
imports: import pandas
  import numpy
  import warnings
  import empyrical
  from pandas.tseries.offsets import BDay
  from scipy import stats
  from statsmodels.regression.linear_model import OLS
  from statsmodels.tools.tools import add_constant
  from .None import utils
label_desc: Use image node_id 5 for calling a global function with example usage: factor_alpha_beta(factor_data, returns, demeaned, group_adjust, equal_weight) and returns: alpha_beta