def testCSZscoreSecurityValueHolderWithGroups(self):
benchmark = SecurityLatestValueHolder(x='close')
groups = SecurityLatestValueHolder(x='ind')
meanAdjustedHolder = CSZScoreSecurityValueHolder(benchmark, groups)
for i in range(len(self.datas['aapl']['close'])):
data = {'aapl': {Factors.CLOSE: self.datas['aapl'][Factors.CLOSE][i],
Factors.OPEN: self.datas['aapl'][Factors.OPEN][i],
'ind': 1.},
'ibm': {Factors.CLOSE: self.datas['ibm'][Factors.CLOSE][i],
Factors.OPEN: self.datas['ibm'][Factors.OPEN][i],
'ind': 1.},
'goog': {Factors.CLOSE: self.datas['goog'][Factors.CLOSE][i],
Factors.OPEN: self.datas['goog'][Factors.OPEN][i],
'ind': 2.},
'baba': {Factors.CLOSE: self.datas['baba'][Factors.CLOSE][i],
Factors.OPEN: self.datas['baba'][Factors.OPEN][i],
'ind': 2.}}
benchmark.push(data)
meanAdjustedHolder.push(data)
benchmarkValues = benchmark.value
groups = {'aapl': 1., 'ibm': 1., 'goog': 2., 'baba': 2.}
expected_rank = pd.Series(benchmarkValues.to_dict()).groupby(groups) \
.transform(lambda x: (x - x.mean()) / x.std(ddof=0))
np.testing.assert_array_almost_equal(expected_rank, meanAdjustedHolder.value.values)
def testCSZscoreSecurityValueHolder(self):
keys = list(range(1, 11))
values = list(range(10, 0, -1))
data = {}
for i, k in enumerate(keys):
data[k] = {}
data[k]['close'] = values[i]
quantile_value = CSZScoreSecurityValueHolder('close')
quantile_value.push(data)
calculated = quantile_value.value
data = np.linspace(10., 1., 10)
expected = (data - data.mean()) / data.std()
np.testing.assert_array_almost_equal(expected, calculated.values)
def CSZScore(x, groups=None):
return CSZScoreSecurityValueHolder(x, groups)
def CSZScore(dependency):
return CSZScoreSecurityValueHolder(dependency)