def test_resets_data(self):
"""Tests if the data is reset properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 3, 1)
stock_names = ["STOCK_1"]
value = 100
mean = 0.05
stdev = 0.025
dependency = GeneratedStockData(evaluation_functions=[str(value)])
data = RandomizedStockData(dependencies=["stock_data"],
mean=mean,
stdev=stdev)
dependency.prepare_data(from_date, to_date, stock_names, [])
data.prepare_data(from_date, to_date, stock_names, [dependency])
data_before_reset = data.data["STOCK_1"].copy()
self.assertTrue(data.ready)
data.reset([True])
self.assertFalse(data.ready)
data.prepare_data(from_date, to_date, stock_names, [dependency])
data_after_reset = data.data["STOCK_1"]
self.assertFalse(data_before_reset.equals(data_after_reset))
def test_prepare_data(self):
"""Tests if the data is prepared properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 2, 1)
stock_names = ["STOCK_1", "STOCK_2"]
data = GeneratedStockData(evaluation_functions=["100", "200"])
data.prepare_data(from_date, to_date, stock_names, [])
self.assertTrue(data.ready)
self.assertTrue((stock_names == data.data.columns.tolist()))
self.assertEqual(100, data[from_date][0])
self.assertEqual(200, data[from_date][1])
def test_resets_data(self):
"""Tests if the data is reset properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2020, 1, 1)
stock_names = ["STOCK_1"]
dependency = GeneratedStockData(evaluation_functions=["100"])
data = RunningAverageAnalysis(dependencies=["stock_data"])
dependency.prepare_data(from_date, to_date, stock_names, [])
data.prepare_data(from_date, to_date, stock_names, [dependency])
self.assertTrue(data.ready)
data.reset([True])
self.assertTrue(data.ready)
def test_reset(self):
"""Tests if the data is reset properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 1, 3)
stock_names = ["STOCK_1"]
value = 100
dependency = GeneratedStockData(evaluation_functions=[str(value)])
data = RelativeStockData(dependencies=["stock_data"])
dependency.prepare_data(from_date, to_date, stock_names, [])
data.prepare_data(from_date, to_date, stock_names, [dependency])
data.reset([True])
self.assertTrue(data.ready)
data.reset([False])
self.assertFalse(data.ready)
def test_prepare_data(self):
"""Tests if the data is prepared properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 1, 3)
stock_names = ["STOCK_1"]
value = 100
dependency = GeneratedStockData(evaluation_functions=[str(value)])
data = RelativeStockData(dependencies=["stock_data"])
dependency.prepare_data(from_date, to_date, stock_names, [])
dependency.data.iloc[1] = 200
data.prepare_data(from_date, to_date, stock_names, [dependency])
self.assertTrue(data.ready)
self.assertTrue((stock_names == data.data.columns.tolist()))
self.assertEqual(1, data.data.iloc[0].item())
self.assertEqual(-0.5, data[to_date].item())
def test_prepare_data(self):
"""Tests if the data is prepared properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 2, 1)
num_days = 5
stock_names = ["STOCK_1", "STOCK_2"]
expected_columns = [
"ra_5_stock_data_STOCK_1", "ra_5_stock_data_STOCK_2"
]
dependency = GeneratedStockData(evaluation_functions=["100"])
data = RunningAverageAnalysis(dependencies=["stock_data"],
num_days=num_days)
dependency.prepare_data(from_date, to_date, stock_names, [])
data.prepare_data(from_date, to_date, stock_names, [dependency])
self.assertTrue(data.ready)
self.assertTrue((expected_columns == data.data.columns.tolist()))
self.assertEqual(len(dependency) - num_days + 1, len(data))
self.assertEqual(100, data[to_date][0])
def test_prepare_data(self):
"""Tests if the data is prepared properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2020, 1, 1)
stock_names = ["STOCK_1"]
value = 100
mean = 0.05
stdev = 0.025
dependency = GeneratedStockData(evaluation_functions=[str(value)])
data = RandomizedStockData(dependencies=["stock_data"],
mean=mean,
stdev=stdev)
dependency.prepare_data(from_date, to_date, stock_names, [])
data.prepare_data(from_date, to_date, stock_names, [dependency])
self.assertTrue(data.ready)
self.assertTrue((stock_names == data.data.columns.tolist()))
self.assertTrue(
np.isclose(value * (1 + mean),
data.data["STOCK_1"].mean(),
rtol=0.1))
self.assertTrue(
np.isclose(value * stdev, data.data["STOCK_1"].std(), rtol=0.1))
def test_resets_data(self):
"""Tests if the data is reset properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 2, 1)
stock_names = ["STOCK_1", "STOCK_2"]
data = GeneratedStockData(evaluation_functions=["100"])
data.prepare_data(from_date, to_date, stock_names, [])
self.assertTrue(data.ready)
data.reset([])
self.assertTrue(data.ready)
def test_prepare_data(self):
"""Tests if the data is prepared properly.
"""
from_date = datetime(2016, 1, 1)
to_date = datetime(2016, 1, 1)
to_date_2 = datetime(2016, 1, 2)
stock_names = ["STOCK_1"]
generated_data_1 = GeneratedStockData(evaluation_functions=["1"])
generated_data_2 = GeneratedStockData(evaluation_functions=["2"])
dependencies = [generated_data_1, generated_data_2]
generated_data_1.prepare_data(from_date, to_date, stock_names, [])
generated_data_2.prepare_data(from_date, to_date_2, stock_names, [])
for operator in ["ge", "gt", "eq"]:
comparator = ComparatorAnalysis(dependencies=["x", "x"], operator=operator)
comparator.prepare_data(from_date, to_date, stock_names, dependencies)
self.assertEqual(0, comparator[from_date].item())
for operator in ["lt", "le", "ne"]:
comparator = ComparatorAnalysis(dependencies=["x", "x"], operator=operator)
comparator.prepare_data(from_date, to_date, stock_names, dependencies)
self.assertEqual(1, comparator[from_date].item())
def test_initializes(self):
"""Tests if initializes properly.
"""
data = GeneratedStockData(evaluation_functions=["100", "200"])
self.assertFalse(data.visible)
def test_requires_evaluation_functions(self):
"""Tests if raises exception when no evaluation functions are provided.
"""
with self.assertRaises(ValueError):
_ = GeneratedStockData()