mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-02 16:12:08 +08:00
TST: Move tests that use minute data.
Make the delineation between `DailyEquityHistoryTestCase` and `MinuteEquityHistoryTestCase` whether or not minute dts or daily dts are used as the query timestamp, instead of whether the frequency is `1d` vs. `1m`. Preparing for adding a repro case for where using `1d` with minute data fails when there is an adjustment occuring the day before the query minute dt.
This commit is contained in:
Eddie Hebert
+172
-162
@@ -468,6 +468,18 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
start_val=2,
|
||||
)
|
||||
|
||||
asset2 = cls.asset_finder.retrieve_asset(2)
|
||||
data[asset2.sid] = create_minute_df_for_asset(
|
||||
cls.env,
|
||||
asset2.start_date,
|
||||
cls.env.previous_trading_day(asset2.end_date),
|
||||
start_val=2,
|
||||
minute_blacklist=[
|
||||
pd.Timestamp('2015-01-08 14:31', tz='UTC'),
|
||||
pd.Timestamp('2015-01-08 21:00', tz='UTC'),
|
||||
],
|
||||
)
|
||||
|
||||
# Start values are crafted so that the thousands place are equal when
|
||||
# adjustments are applied correctly.
|
||||
# The splits and mergers are defined as 2:1 splits, so the prices
|
||||
@@ -1003,6 +1015,166 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
[self.ASSET1], first_day_minutes[5], 15, '1m', 'price'
|
||||
)[self.ASSET1]
|
||||
|
||||
def test_daily_history_blended(self):
|
||||
# daily history windows that end mid-day use minute values for the
|
||||
# last day
|
||||
|
||||
# January 2015 has both daily and minute data for ASSET2
|
||||
day = pd.Timestamp('2015-01-07', tz='UTC')
|
||||
minutes = self.env.market_minutes_for_day(day)
|
||||
|
||||
# minute data, baseline:
|
||||
# Jan 5: 2 to 391
|
||||
# Jan 6: 392 to 781
|
||||
# Jan 7: 782 to 1172
|
||||
for idx, minute in enumerate(minutes):
|
||||
for field in ALL_FIELDS:
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.ASSET2],
|
||||
minute,
|
||||
3,
|
||||
'1d',
|
||||
field
|
||||
)[self.ASSET2]
|
||||
|
||||
self.assertEqual(len(window), 3)
|
||||
|
||||
if field == 'open':
|
||||
self.assertEqual(window[0], 3)
|
||||
self.assertEqual(window[1], 393)
|
||||
elif field == 'high':
|
||||
self.assertEqual(window[0], 393)
|
||||
self.assertEqual(window[1], 783)
|
||||
elif field == 'low':
|
||||
self.assertEqual(window[0], 1)
|
||||
self.assertEqual(window[1], 391)
|
||||
elif field == 'close':
|
||||
self.assertEqual(window[0], 391)
|
||||
self.assertEqual(window[1], 781)
|
||||
elif field == 'volume':
|
||||
self.assertEqual(window[0], 39100)
|
||||
self.assertEqual(window[1], 78100)
|
||||
|
||||
last_val = -1
|
||||
|
||||
if field == 'open':
|
||||
last_val = 783
|
||||
elif field == 'high':
|
||||
# since we increase monotonically, it's just the last
|
||||
# value
|
||||
last_val = 784 + idx
|
||||
elif field == 'low':
|
||||
# since we increase monotonically, the low is the first
|
||||
# value of the day
|
||||
last_val = 781
|
||||
elif field == 'close' or field == 'price':
|
||||
last_val = 782 + idx
|
||||
elif field == 'volume':
|
||||
# for volume, we sum up all the minutely volumes so far
|
||||
# today
|
||||
|
||||
last_val = sum(np.array(range(782, 782 + idx + 1)) * 100)
|
||||
|
||||
self.assertEqual(window[-1], last_val)
|
||||
|
||||
@parameterized.expand(ALL_FIELDS)
|
||||
def test_daily_history_blended_gaps(self, field):
|
||||
# daily history windows that end mid-day use minute values for the
|
||||
# last day
|
||||
|
||||
# January 2015 has both daily and minute data for ASSET2
|
||||
day = pd.Timestamp('2015-01-08', tz='UTC')
|
||||
minutes = self.env.market_minutes_for_day(day)
|
||||
|
||||
# minute data, baseline:
|
||||
# Jan 5: 2 to 391
|
||||
# Jan 6: 392 to 781
|
||||
# Jan 7: 782 to 1172
|
||||
for idx, minute in enumerate(minutes):
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.ASSET2],
|
||||
minute,
|
||||
3,
|
||||
'1d',
|
||||
field
|
||||
)[self.ASSET2]
|
||||
|
||||
self.assertEqual(len(window), 3)
|
||||
|
||||
if field == 'open':
|
||||
self.assertEqual(window[0], 393)
|
||||
self.assertEqual(window[1], 783)
|
||||
elif field == 'high':
|
||||
self.assertEqual(window[0], 783)
|
||||
self.assertEqual(window[1], 1173)
|
||||
elif field == 'low':
|
||||
self.assertEqual(window[0], 391)
|
||||
self.assertEqual(window[1], 781)
|
||||
elif field == 'close':
|
||||
self.assertEqual(window[0], 781)
|
||||
self.assertEqual(window[1], 1171)
|
||||
elif field == 'price':
|
||||
self.assertEqual(window[0], 781)
|
||||
self.assertEqual(window[1], 1171)
|
||||
elif field == 'volume':
|
||||
self.assertEqual(window[0], 78100)
|
||||
self.assertEqual(window[1], 117100)
|
||||
|
||||
last_val = -1
|
||||
|
||||
if field == 'open':
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
else:
|
||||
last_val = 1174.0
|
||||
elif field == 'high':
|
||||
# since we increase monotonically, it's just the last
|
||||
# value
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
elif idx == 389:
|
||||
last_val = 1562.0
|
||||
else:
|
||||
last_val = 1174.0 + idx
|
||||
elif field == 'low':
|
||||
# since we increase monotonically, the low is the first
|
||||
# value of the day
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
else:
|
||||
last_val = 1172.0
|
||||
elif field == 'close':
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
elif idx == 389:
|
||||
last_val = 1172.0 + 388
|
||||
else:
|
||||
last_val = 1172.0 + idx
|
||||
elif field == 'price':
|
||||
if idx == 0:
|
||||
last_val = 1171.0
|
||||
elif idx == 389:
|
||||
last_val = 1172.0 + 388
|
||||
else:
|
||||
last_val = 1172.0 + idx
|
||||
elif field == 'volume':
|
||||
# for volume, we sum up all the minutely volumes so far
|
||||
# today
|
||||
if idx == 0:
|
||||
last_val = 0
|
||||
elif idx == 389:
|
||||
last_val = sum(
|
||||
np.array(range(1173, 1172 + 388 + 1)) * 100)
|
||||
else:
|
||||
last_val = sum(
|
||||
np.array(range(1173, 1172 + idx + 1)) * 100)
|
||||
|
||||
np.testing.assert_almost_equal(window[-1], last_val,
|
||||
err_msg='field={0} minute={1}'.
|
||||
format(field, minute))
|
||||
|
||||
|
||||
class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
@classmethod
|
||||
@@ -1029,27 +1201,6 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
asset.end_date,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def make_minute_bar_data(cls):
|
||||
asset1 = cls.asset_finder.retrieve_asset(1)
|
||||
asset2 = cls.asset_finder.retrieve_asset(2)
|
||||
yield asset1.sid, create_minute_df_for_asset(
|
||||
cls.env,
|
||||
asset1.start_date,
|
||||
asset1.end_date,
|
||||
start_val=2,
|
||||
)
|
||||
yield asset2.sid, create_minute_df_for_asset(
|
||||
cls.env,
|
||||
asset2.start_date,
|
||||
cls.env.previous_trading_day(asset2.end_date),
|
||||
start_val=2,
|
||||
minute_blacklist=[
|
||||
pd.Timestamp('2015-01-08 14:31', tz='UTC'),
|
||||
pd.Timestamp('2015-01-08 21:00', tz='UTC'),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def create_df_for_asset(cls, start_day, end_day, interval=1,
|
||||
force_zeroes=False):
|
||||
@@ -1353,147 +1504,6 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
|
||||
self.assertNotEqual(0, volume_window[self.ASSET2][-3])
|
||||
|
||||
def test_daily_history_blended(self):
|
||||
# daily history windows that end mid-day use minute values for the
|
||||
# last day
|
||||
|
||||
# January 2015 has both daily and minute data for ASSET2
|
||||
day = pd.Timestamp('2015-01-07', tz='UTC')
|
||||
minutes = self.env.market_minutes_for_day(day)
|
||||
|
||||
# minute data, baseline:
|
||||
# Jan 5: 2 to 391
|
||||
# Jan 6: 392 to 781
|
||||
# Jan 7: 782 to 1172
|
||||
for idx, minute in enumerate(minutes):
|
||||
for field in ALL_FIELDS:
|
||||
adj = MINUTE_FIELD_INFO[field]
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.ASSET2],
|
||||
minute,
|
||||
3,
|
||||
'1d',
|
||||
field
|
||||
)[self.ASSET2]
|
||||
|
||||
self.assertEqual(len(window), 3)
|
||||
|
||||
if field == 'volume':
|
||||
self.assertEqual(window[0], 200)
|
||||
self.assertEqual(window[1], 300)
|
||||
else:
|
||||
self.assertEqual(window[0], 2 + adj)
|
||||
self.assertEqual(window[1], 3 + adj)
|
||||
|
||||
last_val = -1
|
||||
|
||||
if field == 'open':
|
||||
last_val = 783
|
||||
elif field == 'high':
|
||||
# since we increase monotonically, it's just the last
|
||||
# value
|
||||
last_val = 784 + idx
|
||||
elif field == 'low':
|
||||
# since we increase monotonically, the low is the first
|
||||
# value of the day
|
||||
last_val = 781
|
||||
elif field == 'close' or field == 'price':
|
||||
last_val = 782 + idx
|
||||
elif field == 'volume':
|
||||
# for volume, we sum up all the minutely volumes so far
|
||||
# today
|
||||
|
||||
last_val = sum(np.array(range(782, 782 + idx + 1)) * 100)
|
||||
|
||||
self.assertEqual(window[-1], last_val)
|
||||
|
||||
@parameterized.expand(ALL_FIELDS)
|
||||
def test_daily_history_blended_gaps(self, field):
|
||||
# daily history windows that end mid-day use minute values for the
|
||||
# last day
|
||||
|
||||
# January 2015 has both daily and minute data for ASSET2
|
||||
day = pd.Timestamp('2015-01-08', tz='UTC')
|
||||
minutes = self.env.market_minutes_for_day(day)
|
||||
|
||||
# minute data, baseline:
|
||||
# Jan 5: 2 to 391
|
||||
# Jan 6: 392 to 781
|
||||
# Jan 7: 782 to 1172
|
||||
for idx, minute in enumerate(minutes):
|
||||
adj = MINUTE_FIELD_INFO[field]
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.ASSET2],
|
||||
minute,
|
||||
3,
|
||||
'1d',
|
||||
field
|
||||
)[self.ASSET2]
|
||||
|
||||
self.assertEqual(len(window), 3)
|
||||
|
||||
if field == 'volume':
|
||||
self.assertEqual(window[0], 300)
|
||||
self.assertEqual(window[1], 400)
|
||||
else:
|
||||
self.assertEqual(window[0], 3 + adj)
|
||||
self.assertEqual(window[1], 4 + adj)
|
||||
|
||||
last_val = -1
|
||||
|
||||
if field == 'open':
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
else:
|
||||
last_val = 1174.0
|
||||
elif field == 'high':
|
||||
# since we increase monotonically, it's just the last
|
||||
# value
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
elif idx == 389:
|
||||
last_val = 1562.0
|
||||
else:
|
||||
last_val = 1174.0 + idx
|
||||
elif field == 'low':
|
||||
# since we increase monotonically, the low is the first
|
||||
# value of the day
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
else:
|
||||
last_val = 1172.0
|
||||
elif field == 'close':
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
elif idx == 389:
|
||||
last_val = 1172.0 + 388
|
||||
else:
|
||||
last_val = 1172.0 + idx
|
||||
elif field == 'price':
|
||||
if idx == 0:
|
||||
last_val = 4
|
||||
elif idx == 389:
|
||||
last_val = 1172.0 + 388
|
||||
else:
|
||||
last_val = 1172.0 + idx
|
||||
elif field == 'volume':
|
||||
# for volume, we sum up all the minutely volumes so far
|
||||
# today
|
||||
if idx == 0:
|
||||
last_val = 0
|
||||
elif idx == 389:
|
||||
last_val = sum(
|
||||
np.array(range(1173, 1172 + 388 + 1)) * 100)
|
||||
else:
|
||||
last_val = sum(
|
||||
np.array(range(1173, 1172 + idx + 1)) * 100)
|
||||
|
||||
np.testing.assert_almost_equal(window[-1], last_val,
|
||||
err_msg='field={0} minute={1}'.
|
||||
format(field, minute))
|
||||
|
||||
def test_history_window_before_first_trading_day(self):
|
||||
# trading_start is 2/3/2014
|
||||
# get a history window that starts before that, and ends after that
|
||||
|
||||
Reference in New Issue
Block a user