786    for every drawdown period
 787    """
 788
 789    def _drawdown_details(drawdown): 790        # mark no drawdown
 791        no_dd = drawdown == 0
 792

 443    )
 444
 445
 446def probabilistic_adjusted_sortino_ratio( 447    series, rf=0.0, periods=252, annualize=False, smart=False
 448):
 449    return probabilistic_ratio(

 435    )
 436
 437
 438def probabilistic_sortino_ratio( 439    series, rf=0.0, periods=252, annualize=False, smart=False
 440):
 441    return probabilistic_ratio(

 427    return psr
 428
 429
 430def probabilistic_sharpe_ratio( 431    series, rf=0.0, periods=252, annualize=False, smart=False
 432):
 433    return probabilistic_ratio(

 390    return data / _sqrt(2)
 391
 392
 393def probabilistic_ratio( 394    series, rf=0.0, base="sharpe", periods=252, annualize=False, smart=False
 395):
 396

 358    return sortino(returns, rf, periods, annualize, True)
 359
 360
 361def rolling_sortino( 362    returns, rf=0, rolling_period=126, annualize=True, periods_per_year=252, **kwargs
 363):
 364    if rf != 0 and rolling_period is None:

 354    return res
 355
 356
 357def smart_sortino(returns, rf=0, periods=252, annualize=True): 358    return sortino(returns, rf, periods, annualize, True)
 359
 360

 303    return sharpe(returns, rf, periods, annualize, True)
 304
 305
 306def rolling_sharpe( 307    returns,
 308    rf=0.0,
 309    rolling_period=126,

 299    return res
 300
 301
 302def smart_sharpe(returns, rf=0.0, periods=252, annualize=True): 303    return sharpe(returns, rf, periods, annualize, True)
 304
 305

 234    return std
 235
 236
 237def rolling_volatility( 238    returns, rolling_period=126, periods_per_year=252, prepare_returns=True
 239):
 240    if prepare_returns:

 169def win_rate(returns, aggregate=None, compounded=True, prepare_returns=True):
 170    """Calculates the win ratio for a period"""
 171
 172    def _win_rate(series): 173        try:
 174            return len(series[series > 0]) / len(series[series != 0])
 175        except Exception:

 154    if prepare_returns:
 155        returns = _utils._prepare_returns(returns)
 156
 157    def _exposure(ret): 158        ex = len(ret[(~_np.isnan(ret)) & (ret != 0)]) / len(ret)
 159        return _ceil(ex * 100) / 100
 160

  47
  48
  49def distribution(returns, compounded=True, prepare_returns=True):
  50    def get_outliers(data):  51        # https://datascience.stackexchange.com/a/57199
  52        Q1 = data.quantile(0.25)
  53        Q3 = data.quantile(0.75)

  46    return returns.add(1).prod() - 1
  47
  48
  49def distribution(returns, compounded=True, prepare_returns=True):  50    def get_outliers(data):
  51        # https://datascience.stackexchange.com/a/57199
  52        Q1 = data.quantile(0.25)