def test_sharpe_ratio(self):
actual_sharpe_ratio = sharpe_ratio(self.test_returns_tms,
frequency=Frequency.DAILY)
expected_sharpe_ratio = 23.059159631739007
self.assertAlmostEqual(expected_sharpe_ratio,
actual_sharpe_ratio,
places=10)
def minTRL(returns_timeseries: QFSeries,
target_sharpe_ratio: float = 1.0,
confidence_level: float = 0.95) -> float:
"""
Computes the Minimum Track Record Length measure. The aim of computing is the possibility of answering the
following question: 'How long should a track record be in order to have statistical confidence that its Sharpe ratio
is above a given threshold?'
The concept of Minimum Track Record Length is presented by Bailey and Prado in 'The Sharpe Ratio Efficient Frontier'
"""
returns_series = returns_timeseries.to_simple_returns()
skewness = returns_series.skew()
kurtosis = returns_series.kurt()
sharpe_ratio_value = sharpe_ratio(returns_series,
frequency=Frequency.DAILY)
minTRL_value = 1 + ((1 - skewness * sharpe_ratio_value + (kurtosis - 1) / 4.0) * sharpe_ratio_value ** 2) * \
(stats.norm.ppf(confidence_level) / (sharpe_ratio_value - target_sharpe_ratio)) ** 2
return minTRL_value
def _calculate_ratios(self):
self.sharpe_ratio = sharpe_ratio(self.returns_tms, self.frequency)
self.omega_ratio = omega_ratio(self.returns_tms)
self.calmar_ratio = calmar_ratio(self.returns_tms, self.frequency)
self.gain_to_pain_ratio = gain_to_pain_ratio(self.returns_tms)
self.sorino_ratio = sorino_ratio(self.returns_tms, self.frequency)