def testCSAverageSecurityValueHolderWithGroup(self):
benchmark = SecurityLatestValueHolder(x='close')
groups = SecurityLatestValueHolder(x='ind')
meanHolder = CSAverageSecurityValueHolder(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)
meanHolder.push(data)
benchmarkValues = benchmark.value
groups = {'aapl': 1., 'ibm': 1., 'goog': 2., 'baba': 2.}
expected_mean = pd.Series(benchmarkValues.to_dict()).groupby(groups).mean()
calculated_mean = meanHolder.value
for name in calculated_mean.index():
if name in ['aapl', 'ibm']:
self.assertAlmostEqual(calculated_mean[name], expected_mean[1])
else:
self.assertAlmostEqual(calculated_mean[name], expected_mean[2])
def testCSAverageSecurityValueHolder(self):
benchmark = SecurityLatestValueHolder(dependency='close')
meanHolder = CSAverageSecurityValueHolder(benchmark)
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]},
'ibm': {Factors.CLOSE: self.datas['ibm'][Factors.CLOSE][i],
Factors.OPEN: self.datas['ibm'][Factors.OPEN][i]}}
benchmark.push(data)
meanHolder.push(data)
benchmarkValues = benchmark.value
np.testing.assert_array_almost_equal(benchmarkValues.mean(), meanHolder.value.values)
def CSMean(x, groups=None):
return CSAverageSecurityValueHolder(x, groups)
def CSMean(dependency):
return CSAverageSecurityValueHolder(dependency)