def alpha(self, factor_returns, risk_free=0.): """Annualized alpha. Args: factor_returns (array_like): Benchmark return to compare returns against. Will broadcast. risk_free (float or array_like): Constant risk-free return throughout the period.""" factor_returns = reshape_fns.broadcast_to( reshape_fns.to_2d(factor_returns, raw=True), reshape_fns.to_2d(self._obj, raw=True)) risk_free = np.broadcast_to(risk_free, (len(self.columns),)) return self.wrap_reduced(nb.alpha_nb(self.to_2d_array(), factor_returns, self.ann_factor, risk_free))
def alpha(self, benchmark_rets, risk_free=0., wrap_kwargs=None): """Annualized alpha. Args: benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast. risk_free (float or array_like): Constant risk-free return throughout the period.""" benchmark_rets = reshape_fns.broadcast_to( reshape_fns.to_2d(benchmark_rets, raw=True), reshape_fns.to_2d(self._obj, raw=True)) wrap_kwargs = merge_dicts(dict(name_or_index='alpha'), wrap_kwargs) return self.wrapper.wrap_reduced(nb.alpha_nb( self.to_2d_array(), benchmark_rets, self.ann_factor, risk_free ), **wrap_kwargs)
def alpha(self, benchmark_rets: tp.ArrayLike, risk_free: float = 0., wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries: """Annualized alpha. Args: benchmark_rets (array_like): Benchmark return to compare returns against. Will broadcast. risk_free (float): Constant risk-free return throughout the period.""" benchmark_rets = broadcast_to(to_2d(benchmark_rets, raw=True), to_2d(self._obj, raw=True)) result = nb.alpha_nb(self.to_2d_array(), benchmark_rets, self.ann_factor, risk_free) wrap_kwargs = merge_dicts(dict(name_or_index='alpha'), wrap_kwargs) return self.wrapper.wrap_reduced(result, **wrap_kwargs)