code: def CAPM(self, benchmark, has_const=False, use_const=True):
          
  
          return ols.OLS(
              y=self, x=benchmark, has_const=has_const, use_const=use_const
          )
docs: Interface to OLS regression against `benchmark`.
  
          `self.alpha()`, `self.beta()` and several other methods
          stem from here.  For the full method set, see
          `pyfinance.ols.OLS`.
  
          Parameters
          ----------
          benchmark : {pd.Series, TSeries, pd.DataFrame, np.ndarray}
              The benchmark securitie(s) to which `self` is compared.
          has_const : bool, default False
              Specifies whether `benchmark` includes a user-supplied constant
              (a column vector).  If False, it is added at instantiation.
          use_const : bool, default True
              Whether to include an intercept term in the model output.  Note the
              difference between `has_const` and `use_const`: the former
              specifies whether a column vector of 1s is included in the
              input; the latter specifies whether the model itself
              should include a constant (intercept) term.  Exogenous
              data that is ~N(0,1) would have a constant equal to zero;
              specify use_const=False in this situation.
  
          Returns
          -------
          pyfinance.ols.OLS