def testSecurityMovingMinimum(self):
window = 10
ma1 = SecurityMovingMin(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():
expected = np.min(self.dataSet[name]['close'][start:(i + 1)])
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):
_ = SecurityMovingMin(window, ['close', 'open'])
def testTransformWithoutCategory(self):
test_df = pd.DataFrame({'b': [4, 5, 6, 7, 6, 5, 4],
'c': [9, 8, 7, 6, 5, 4, 3]},
index=[1, 2, 3, 4, 5, 6, 7],
dtype=float)
expression = SecurityMovingMax(20, 'b') + SecurityMovingMin(20, 'c')
calculated = expression.transform(test_df, name='new_factor')
expected = [13., 13., 13., 13., 12., 11., 10.]
np.testing.assert_array_almost_equal(calculated['new_factor'], expected)
def MMIN(window, x='x'):
return SecurityMovingMin(window, x)
def MMIN(window, dependency='x'):
return SecurityMovingMin(window, dependency)