def test_event_window(self):
algo = BatchTransformAlgorithm()
algo.run(self.source)
self.assertEqual(algo.history_return_price_class[:2],
[None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_price_decorator[:2],
[None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_price_market_aware[:2],
[None, None],
"First two iterations should return None")
# test overloaded class
for test_history in [algo.history_return_price_class,
algo.history_return_price_decorator]:
np.testing.assert_array_equal(
range(2, 8),
test_history[2].values.flatten()
)
np.testing.assert_array_equal(
range(2, 8),
test_history[3].values.flatten()
)
np.testing.assert_array_equal(
range(4, 12),
test_history[4].values.flatten()
)
def test_passing_of_args(self):
algo = BatchTransformAlgorithm(1,
kwarg='str',
sim_params=self.sim_params,
env=self.env)
algo.run(self.source)
self.assertEqual(algo.args, (1, ))
self.assertEqual(algo.kwargs, {'kwarg': 'str'})
expected_item = ((1, ), {'kwarg': 'str'})
self.assertEqual(
algo.history_return_args,
[
# 1990-01-01 - market holiday, no event
# 1990-01-02 - window not full
None,
# 1990-01-03 - window not full
None,
# 1990-01-04 - window now full, 3rd event
expected_item,
# 1990-01-05 - window now full
expected_item,
# 1990-01-08 - window now full
expected_item
])
def test_event_window(self):
algo = BatchTransformAlgorithm()
algo.run(self.source)
self.assertEqual(algo.history_return_price_class[:2], [None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_price_decorator[:2], [None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_price_market_aware[:2],
[None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_more_days_than_refresh[:3],
[None, None, None],
"First five iterations should return None")
self.assertTrue(
isinstance(algo.history_return_more_days_than_refresh[4],
pd.DataFrame), "Sixth iteration should not be None")
# test overloaded class
for test_history in [
algo.history_return_price_class,
algo.history_return_price_decorator
]:
np.testing.assert_array_equal(range(2, 8),
test_history[2].values.flatten())
np.testing.assert_array_equal(range(2, 8),
test_history[3].values.flatten())
np.testing.assert_array_equal(range(4, 12),
test_history[4].values.flatten())
def test_event_window(self):
algo = BatchTransformAlgorithm()
algo.run(self.source)
wl = algo.window_length
# The following assertion depend on window length of 3
self.assertEqual(wl, 3)
self.assertEqual(
algo.history_return_price_class[:wl], [None] * wl,
"First three iterations should return None." + "\n" +
"i.e. no returned values until window is full'" + "%s" %
(algo.history_return_price_class, ))
self.assertEqual(
algo.history_return_price_decorator[:wl], [None] * wl,
"First three iterations should return None." + "\n" +
"i.e. no returned values until window is full'" + "%s" %
(algo.history_return_price_decorator, ))
# After three Nones, the next value should be a data frame
self.assertTrue(
isinstance(algo.history_return_price_class[wl], pd.DataFrame))
# Test whether arbitrary fields can be added to datapanel
field = algo.history_return_arbitrary_fields[-1]
self.assertTrue('arbitrary' in field.items,
'datapanel should contain column arbitrary')
self.assertTrue(
all(field['arbitrary'].values.flatten() == [123] *
algo.window_length),
'arbitrary dataframe should contain only "test"')
for data in algo.history_return_sid_filter[wl:]:
self.assertIn(0, data.columns)
self.assertNotIn(1, data.columns)
for data in algo.history_return_field_filter[wl:]:
self.assertIn('price', data.items)
self.assertNotIn('ignore', data.items)
for data in algo.history_return_field_no_filter[wl:]:
self.assertIn('price', data.items)
self.assertIn('ignore', data.items)
for data in algo.history_return_ticks[wl:]:
self.assertTrue(isinstance(data, deque))
for data in algo.history_return_not_full:
self.assertIsNot(data, None)
# test overloaded class
for test_history in [
algo.history_return_price_class,
algo.history_return_price_decorator
]:
# starting at window length, the window should contain
# consecutive (of window length) numbers up till the end.
for i in range(algo.window_length, len(test_history)):
np.testing.assert_array_equal(
range(i - algo.window_length + 1, i + 1),
test_history[i].values.flatten())
def test_event_window(self):
algo = BatchTransformAlgorithm()
algo.run(self.source)
wl = algo.window_length
# The following assertion depend on window length of 3
self.assertEqual(wl, 3)
self.assertEqual(
algo.history_return_price_class[:wl],
[None] * wl,
"First three iterations should return None."
+ "\n"
+ "i.e. no returned values until window is full'"
+ "%s" % (algo.history_return_price_class,),
)
self.assertEqual(
algo.history_return_price_decorator[:wl],
[None] * wl,
"First three iterations should return None."
+ "\n"
+ "i.e. no returned values until window is full'"
+ "%s" % (algo.history_return_price_decorator,),
)
# After three Nones, the next value should be a data frame
self.assertTrue(isinstance(algo.history_return_price_class[wl], pd.DataFrame))
# Test whether arbitrary fields can be added to datapanel
field = algo.history_return_arbitrary_fields[-1]
self.assertTrue("arbitrary" in field.items, "datapanel should contain column arbitrary")
self.assertTrue(
all(field["arbitrary"].values.flatten() == [123] * algo.window_length),
'arbitrary dataframe should contain only "test"',
)
for data in algo.history_return_sid_filter[wl:]:
self.assertIn(0, data.columns)
self.assertNotIn(1, data.columns)
for data in algo.history_return_field_filter[wl:]:
self.assertIn("price", data.items)
self.assertNotIn("ignore", data.items)
for data in algo.history_return_field_no_filter[wl:]:
self.assertIn("price", data.items)
self.assertIn("ignore", data.items)
for data in algo.history_return_ticks[wl:]:
self.assertTrue(isinstance(data, deque))
for data in algo.history_return_not_full:
self.assertIsNot(data, None)
# test overloaded class
for test_history in [algo.history_return_price_class, algo.history_return_price_decorator]:
# starting at window length, the window should contain
# consecutive (of window length) numbers up till the end.
for i in range(algo.window_length, len(test_history)):
np.testing.assert_array_equal(
range(i - algo.window_length + 1, i + 1), test_history[i].values.flatten()
)
def test_event_window(self):
algo = BatchTransformAlgorithm()
algo.run(self.source)
wl = algo.window_length
self.assertEqual(algo.history_return_price_class[:wl],
[None] * wl,
"First two iterations should return None")
self.assertEqual(algo.history_return_price_decorator[:wl],
[None] * wl,
"First two iterations should return None")
self.assertTrue(isinstance(
algo.history_return_price_class[wl + 1],
pd.DataFrame)
)
# Test whether arbitrary fields can be added to datapanel
field = algo.history_return_arbitrary_fields[-1]
self.assertTrue(
'arbitrary' in field.items,
'datapanel should contain column arbitrary'
)
self.assertTrue(all(
field['arbitrary'].values.flatten() ==
[123] * algo.window_length),
'arbitrary dataframe should contain only "test"'
)
for data in algo.history_return_sid_filter[wl:]:
self.assertIn(0, data.columns)
self.assertNotIn(1, data.columns)
for data in algo.history_return_field_filter[wl:]:
self.assertIn('price', data.items)
self.assertNotIn('ignore', data.items)
for data in algo.history_return_field_no_filter[wl:]:
self.assertIn('price', data.items)
self.assertIn('ignore', data.items)
for data in algo.history_return_ticks[wl:]:
self.assertTrue(isinstance(data, deque))
for data in algo.history_return_not_full:
self.assertIsNot(data, None)
# test overloaded class
for test_history in [algo.history_return_price_class,
algo.history_return_price_decorator]:
# starting at window length, the window should contain
# consecutive (of window length) numbers up till the end.
for i in range(algo.window_length, len(test_history)):
np.testing.assert_array_equal(
range(i - algo.window_length + 1, i + 1),
test_history[i].values.flatten()
)
def test_passing_of_args(self):
algo = BatchTransformAlgorithm(1, kwarg='str')
self.assertEqual(algo.args, (1,))
self.assertEqual(algo.kwargs, {'kwarg': 'str'})
algo.run(self.source)
expected_item = ((1, ), {'kwarg': 'str'})
self.assertEqual(
algo.history_return_args,
[None, None, expected_item, expected_item,
expected_item, expected_item])
def test_event_window(self):
days = 50
algo = BatchTransformAlgorithm(days=days, refresh_period=days)
algo.run(self.data)
self.assertEqual(
algo.history_return_price_market_aware[:days], [None] * days,
"First {days} iterations should return None".format(days=days))
self.assertFalse(
algo.history_return_price_market_aware[days + 1] is None,
"Window is contains too many Nones.")
def test_passing_of_args(self):
algo = BatchTransformAlgorithm(1, kwarg='str')
self.assertEqual(algo.args, (1, ))
self.assertEqual(algo.kwargs, {'kwarg': 'str'})
algo.run(self.source)
expected_item = ((1, ), {'kwarg': 'str'})
self.assertEqual(algo.history_return_args, [
None, None, expected_item, expected_item, expected_item,
expected_item
])
def test_event_window(self):
days = 50
algo = BatchTransformAlgorithm(days=days, refresh_period=days)
algo.run(self.data)
self.assertEqual(algo.history_return_price_market_aware[:days],
[None] * days,
"First {days} iterations should return None"
.format(days=days))
self.assertFalse(algo.history_return_price_market_aware[days + 1]
is None,
"Window is contains too many Nones.")
def test_core_functionality(self):
algo = BatchTransformAlgorithm(sim_params=self.sim_params)
algo.run(self.source)
wl = algo.window_length
# The following assertion depend on window length of 3
self.assertEqual(wl, 3)
# If window_length is 3, there should be 2 None events, as the
# window fills up on the 3rd day.
n_none_events = 2
self.assertEqual(algo.history_return_price_class[:n_none_events],
[None] * n_none_events,
"First two iterations should return None." + "\n" +
"i.e. no returned values until window is full'" +
"%s" % (algo.history_return_price_class,))
def test_passing_of_args(self):
algo = BatchTransformAlgorithm(1, kwarg='str')
self.assertEqual(algo.args, (1,))
self.assertEqual(algo.kwargs, {'kwarg': 'str'})
algo.run(self.source)
expected_item = ((1, ), {'kwarg': 'str'})
self.assertEqual(
algo.history_return_args,
[
# 1990-01-03 - window not full
None,
# 1990-01-04 - window not full
None,
# 1990-01-05 - window not full, 3rd event
None,
# 1990-01-08 - window now full
expected_item
])
def test_event_window(self):
algo = BatchTransformAlgorithm()
algo.run(self.source)
self.assertEqual(algo.history_return_price_class[:2],
[None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_price_decorator[:2],
[None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_price_market_aware[:2],
[None, None],
"First two iterations should return None")
self.assertEqual(algo.history_return_more_days_than_refresh[:3],
[None, None, None],
"First five iterations should return None")
self.assertTrue(isinstance(
algo.history_return_more_days_than_refresh[4],
pd.DataFrame),
"Sixth iteration should not be None"
)
# test overloaded class
for test_history in [algo.history_return_price_class,
algo.history_return_price_decorator]:
np.testing.assert_array_equal(
range(2, 8),
test_history[2].values.flatten()
)
np.testing.assert_array_equal(
range(2, 8),
test_history[3].values.flatten()
)
np.testing.assert_array_equal(
range(4, 12),
test_history[4].values.flatten()
)
def test_passing_of_args(self):
algo = BatchTransformAlgorithm(1, kwarg='str',
sim_params=self.sim_params,
env=self.env)
algo.run(self.source)
self.assertEqual(algo.args, (1,))
self.assertEqual(algo.kwargs, {'kwarg': 'str'})
expected_item = ((1, ), {'kwarg': 'str'})
self.assertEqual(
algo.history_return_args,
[
# 1990-01-01 - market holiday, no event
# 1990-01-02 - window not full
None,
# 1990-01-03 - window not full
None,
# 1990-01-04 - window now full, 3rd event
expected_item,
# 1990-01-05 - window now full
expected_item,
# 1990-01-08 - window now full
expected_item
])
def test_core_functionality(self):
algo = BatchTransformAlgorithm(sim_params=self.sim_params)
algo.run(self.source)
wl = algo.window_length
# The following assertion depend on window length of 3
self.assertEqual(wl, 3)
# If window_length is 3, there should be 2 None events, as the
# window fills up on the 3rd day.
n_none_events = 2
self.assertEqual(algo.history_return_price_class[:n_none_events],
[None] * n_none_events,
"First two iterations should return None." + "\n" +
"i.e. no returned values until window is full'" +
"%s" % (algo.history_return_price_class,))
self.assertEqual(algo.history_return_price_decorator[:n_none_events],
[None] * n_none_events,
"First two iterations should return None." + "\n" +
"i.e. no returned values until window is full'" +
"%s" % (algo.history_return_price_decorator,))
# After three Nones, the next value should be a data frame
self.assertTrue(isinstance(
algo.history_return_price_class[wl],
pd.DataFrame)
)
# Test whether arbitrary fields can be added to datapanel
field = algo.history_return_arbitrary_fields[-1]
self.assertTrue(
'arbitrary' in field.items,
'datapanel should contain column arbitrary'
)
self.assertTrue(all(
field['arbitrary'].values.flatten() ==
[123] * algo.window_length),
'arbitrary dataframe should contain only "test"'
)
for data in algo.history_return_sid_filter[wl:]:
self.assertIn(0, data.columns)
self.assertNotIn(1, data.columns)
for data in algo.history_return_field_filter[wl:]:
self.assertIn('price', data.items)
self.assertNotIn('ignore', data.items)
for data in algo.history_return_field_no_filter[wl:]:
self.assertIn('price', data.items)
self.assertIn('ignore', data.items)
for data in algo.history_return_ticks[wl:]:
self.assertTrue(isinstance(data, deque))
for data in algo.history_return_not_full:
self.assertIsNot(data, None)
# test overloaded class
for test_history in [algo.history_return_price_class,
algo.history_return_price_decorator]:
# starting at window length, the window should contain
# consecutive (of window length) numbers up till the end.
for i in range(algo.window_length, len(test_history)):
np.testing.assert_array_equal(
range(i - algo.window_length + 2, i + 2),
test_history[i].values.flatten()
)
def test_passing_of_args(self):
algo = BatchTransformAlgorithm(1, kwarg='str',
sim_params=self.sim_params)