def testSecurityMovingPositiveAverage(self):
window = 10
ma1 = SecurityMovingPositiveAverage(window, ['close'])
for i in range(len(self.aapl['close'])):
data = {'aapl': {'close': self.aapl['close'][i], 'open': self.aapl['open'][i]}}
data['ibm'] = {'close': self.ibm['close'][i], 'open': self.ibm['open'][i]}
ma1.push(data)
if i < 10:
start = 0
else:
start = i + 1 - window
value = ma1.value
for name in value.index:
sampled = list(filter(lambda x: x > 0, self.dataSet[name]['close'][start:(i + 1)]))
if len(sampled) > 0:
expected = np.mean(sampled)
else:
expected = 0.0
calculated = value[name]
self.assertAlmostEqual(expected, calculated, 12, 'at index {0}\n'
'expected: {1:.12f}\n'
'calculated: {2:.12f}'.format(i, expected, calculated))
with self.assertRaises(ValueError):
_ = SecurityMovingPositiveAverage(window, ['close', 'open'])
def testSecurityMovingPositiveAverage(self):
window = 10
ma1 = SecurityMovingPositiveAverage(window, ['close'])
for i in range(len(self.aapl['close'])):
data = dict(aapl=dict(close=self.aapl['close'][i],
open=self.aapl['open'][i]),
ibm=dict(close=self.ibm['close'][i],
open=self.ibm['open'][i]))
ma1.push(data)
if i < window:
start = 0
else:
start = i + 1 - window
value = ma1.value
for name in value.index():
sampled = list(
filter(lambda x: x > 0,
self.dataSet[name]['close'][start:(i + 1)]))
if len(sampled) > 0:
expected = np.mean(sampled)
else:
expected = 0.0
calculated = value[name]
self.assertAlmostEqual(
expected, calculated, 12, 'at index {0}\n'
'expected: {1:.12f}\n'
'calculated: {2:.12f}'.format(i, expected, calculated))
def testSecurityMovingPositiveAverage(self):
window = 10
ma1 = SecurityMovingPositiveAverage(window, ['close'], ['aapl', 'ibm'])
for i in range(len(self.aapl['close'])):
data = {
'aapl': {
'close': self.aapl['close'][i],
'open': self.aapl['open'][i]
}
}
data['ibm'] = {
'close': self.ibm['close'][i],
'open': self.ibm['open'][i]
}
ma1.push(data)
if i < 10:
start = 0
else:
start = i + 1 - window
value = ma1.value
for name in value.index:
sampled = list(
filter(lambda x: x > 0,
self.dataSet[name]['close'][start:(i + 1)]))
if len(sampled) > 0:
expected = np.mean(sampled)
else:
expected = 0.0
calculated = value[name]
self.assertAlmostEqual(
expected, calculated, 12, 'at index {0}\n'
'expected: {1:.12f}\n'
'calculated: {2:.12f}'.format(i, expected, calculated))
with self.assertRaises(ValueError):
_ = SecurityMovingPositiveAverage(window, ['close', 'open'],
['aapl', 'ibm'])