def setUp(self):
# create a simpler price history
self.trend_fixture(
[82, 79, 84.1, 85.6, 80.9, 81.2, 81.5, 81.7, 82.0, 82.2, 82.5]
)
# create a DMAC object for the stock
self.timeseries = TimeSeries(self.stock)
def setUp(self):
# create a price history
test = [round(random.uniform(80.01, 83.01), 2) for _ in range(20)]
random.shuffle(test)
self.trend_fixture(test)
# create a DMAC object for the stock
self.timeseries = TimeSeries(self.stock)
class StockDMACspot(StockDMACscenario):
"""Test time series and DMAC analysis' methods in single test cases"""
@classmethod
def setUpClass(cls):
cls.stock = Stock("GOOG")
def setUp(self):
# create a simpler price history
self.trend_fixture(
[82, 79, 84.1, 85.6, 80.9, 81.2, 81.5, 81.7, 82.0, 82.2, 82.5]
)
# create a DMAC object for the stock
self.timeseries = TimeSeries(self.stock)
def test_should_create_time_series(self):
# print(self.stock.price_history)
self.assertTrue(len(self.stock.price_history) > 10)
def test_should_raise_error_in_calculate_stma(self):
with self.assertRaises(ValueError):
self.timeseries.calculate_stma_ltma(date.today(), period='abc')
self.timeseries.calculate_stma_ltma(date.today(), period=1)
def test_should_calculate_stma(self):
stma = self.timeseries.calculate_stma_ltma(date.today(), period='short')
print('STMA: ', stma)
self.assertAlmostEqual(stma, 82.16, delta=0.0001)
def test_should_calculate_ltma(self):
ltma = self.timeseries.calculate_stma_ltma(date.today(), period='long')
print('LTMA: ', ltma)
self.assertAlmostEqual(ltma, 82.07, delta=0.0001)
def test_should_calculate_crossover(self):
t = self.timeseries.calculate_crossover(date.today())
print('signal, (short series), (long series) >>> ', t)
self.assertEqual(t[0], 0)
def tearDown(self):
del self.timeseries
@classmethod
def tearDownClass(cls):
del cls.stock
class StockDMACscenario(unittest.TestCase):
"""Test time series and DMAC analysis in a complete random scenario"""
@classmethod
def setUpClass(cls):
cls.stock = Stock("GOOG")
def trend_fixture(self, prices):
"""Create new prices in price history from a list of Float.
To be used in the tests below."""
return [
self.stock.update(date.today() + timedelta(days=-i), price=p)
for i, p in enumerate(prices)
]
def setUp(self):
# create a price history
test = [round(random.uniform(80.01, 83.01), 2) for _ in range(20)]
random.shuffle(test)
self.trend_fixture(test)
# create a DMAC object for the stock
self.timeseries = TimeSeries(self.stock)
def test_should_create_time_series(self):
# print(self.stock.price_history)
self.assertTrue(len(self.stock.price_history) > 11)
def test_should_calculate_crossover(self):
t = self.timeseries.calculate_crossover(date.today())
print('signal, (short series), (long series) >>> ', t)
def tearDown(self):
print(self.stock) if self.stock.price is not None else None
print(self.timeseries) if self.stock.price is not None else None
print('\n')
del self.timeseries
@classmethod
def tearDownClass(cls):
del cls.stock
