def deflated_sharpe_ratio(
self,
risk_free: float = 0.,
var_sharpe: tp.Optional[float] = None,
nb_trials: tp.Optional[int] = None,
ddof: int = 0,
bias: bool = True,
wrap_kwargs: tp.KwargsLike = None) -> tp.MaybeSeries:
"""Deflated Sharpe Ratio (DSR).
Expresses the chance that the advertized strategy has a positive Sharpe ratio.
If `var_sharpe` is None, is calculated based on all columns.
If `nb_trials` is None, is set to the number of columns."""
sharpe_ratio = to_1d(self.sharpe_ratio(risk_free=risk_free), raw=True)
if var_sharpe is None:
var_sharpe = np.var(sharpe_ratio, ddof=ddof)
if nb_trials is None:
nb_trials = self.wrapper.shape_2d[1]
returns = to_2d(self._obj, raw=True)
nanmask = np.isnan(returns)
if nanmask.any():
returns = returns.copy()
returns[nanmask] = 0.
result = metrics.deflated_sharpe_ratio(
est_sharpe=sharpe_ratio / np.sqrt(self.ann_factor),
var_sharpe=var_sharpe / self.ann_factor,
nb_trials=nb_trials,
backtest_horizon=self.wrapper.shape_2d[0],
skew=skew(returns, axis=0, bias=bias),
kurtosis=kurtosis(returns, axis=0, bias=bias))
wrap_kwargs = merge_dicts(dict(name_or_index='deflated_sharpe_ratio'),
wrap_kwargs)
return self.wrapper.wrap_reduced(result, **wrap_kwargs)
def deflated_sharpe_ratio(self,
risk_free=0.,
var_sharpe=None,
nb_trials=None,
ddof=0,
bias=True):
"""Deflated Sharpe Ratio (DSR).
Expresses the chance that the advertized strategy has a positive Sharpe ratio.
If `var_sharpe` is None, is calculated based on all columns.
If `nb_trials` is None, is set to the number of columns."""
sharpe_ratio = reshape_fns.to_1d(
self.sharpe_ratio(risk_free=risk_free), raw=True)
if var_sharpe is None:
var_sharpe = np.var(sharpe_ratio, ddof=ddof)
if nb_trials is None:
nb_trials = self.shape_2d[1]
returns = reshape_fns.to_2d(self._obj, raw=True)
nanmask = np.isnan(returns)
if nanmask.any():
returns = returns.copy()
returns[nanmask] = 0.
return self.wrap_reduced(
metrics.deflated_sharpe_ratio(
est_sharpe=sharpe_ratio / np.sqrt(self.ann_factor),
var_sharpe=var_sharpe / self.ann_factor,
nb_trials=nb_trials,
backtest_horizon=self.shape_2d[0],
skew=skew(returns, axis=0, bias=bias),
kurtosis=kurtosis(returns, axis=0, bias=bias)))