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TST: Adds MinuteEquityHistoryFuturesCalendarTestCase
Added as a subclass of MinuteEquityHistoryTestCase, where the primary
calendar is 'us_futures'.
Notes on modifications to MinuteEquityHistoryTestCase:
- To work on generic calendars, many tests now use set minutes for
window start and end, and check the values on active equity minutes.
- test_minute_regular should test against active equity minutes
- Adapts test_minute_midnight to work with futures calendar
- Use a method of getting the last open minute that works with
calendars that are open at midnight
- Test against Sunday at midnight, since the real intention of this
test is to check that given a non-open minute, we fall back to the
last open minute.
This commit is contained in:
Andrew Daniels
+264
-99
@@ -22,7 +22,7 @@ from six import iteritems
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from zipline import TradingAlgorithm
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from zipline._protocol import handle_non_market_minutes, BarData
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from zipline.assets import Asset
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from zipline.assets import Asset, Equity
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from zipline.errors import (
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HistoryInInitialize,
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HistoryWindowStartsBeforeData,
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@@ -537,19 +537,21 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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@classmethod
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def make_equity_minute_bar_data(cls):
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equities_cal = cls.trading_calendars[Equity]
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data = {}
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sids = {2, 5, cls.SHORT_ASSET_SID, cls.HALF_DAY_TEST_ASSET_SID}
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for sid in sids:
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asset = cls.asset_finder.retrieve_asset(sid)
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data[sid] = create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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asset.start_date,
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asset.end_date,
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start_val=2,
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)
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data[1] = create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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pd.Timestamp('2014-01-03', tz='utc'),
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pd.Timestamp('2016-01-29', tz='utc'),
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start_val=2,
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@@ -557,9 +559,9 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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asset2 = cls.asset_finder.retrieve_asset(2)
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data[asset2.sid] = create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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asset2.start_date,
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cls.trading_calendar.previous_session_label(asset2.end_date),
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equities_cal.previous_session_label(asset2.end_date),
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start_val=2,
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minute_blacklist=[
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pd.Timestamp('2015-01-08 14:31', tz='UTC'),
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@@ -574,29 +576,29 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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# the thousands place.
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data[cls.MERGER_ASSET_SID] = data[cls.SPLIT_ASSET_SID] = pd.concat((
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create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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pd.Timestamp('2015-01-05', tz='UTC'),
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pd.Timestamp('2015-01-05', tz='UTC'),
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start_val=8000),
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create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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pd.Timestamp('2015-01-06', tz='UTC'),
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pd.Timestamp('2015-01-06', tz='UTC'),
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start_val=2000),
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create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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pd.Timestamp('2015-01-07', tz='UTC'),
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pd.Timestamp('2015-01-07', tz='UTC'),
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start_val=1000),
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create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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pd.Timestamp('2015-01-08', tz='UTC'),
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pd.Timestamp('2015-01-08', tz='UTC'),
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start_val=1000)
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))
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asset3 = cls.asset_finder.retrieve_asset(3)
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data[3] = create_minute_df_for_asset(
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cls.trading_calendar,
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equities_cal,
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asset3.start_date,
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asset3.end_date,
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start_val=2,
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@@ -817,7 +819,8 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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# 10 minutes
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asset = self.env.asset_finder.retrieve_asset(sid)
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minutes = self.trading_calendar.minutes_for_session(
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# Check the first hour of equities trading.
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minutes = self.trading_calendars[Equity].minutes_for_session(
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pd.Timestamp('2015-01-05', tz='UTC')
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)[0:60]
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@@ -826,33 +829,37 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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assets=[asset],
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fields=[field])
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def test_minute_midnight(self):
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midnight = pd.Timestamp('2015-01-06', tz='UTC')
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last_minute = self.trading_calendar.open_and_close_for_session(
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self.trading_calendar.minute_to_session_label(
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midnight,
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direction="previous"
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)
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)[1]
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def test_minute_sunday_midnight(self):
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# Most trading calendars aren't open at midnight on Sunday.
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sunday_midnight = pd.Timestamp('2015-01-09', tz='UTC')
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midnight_bar_data = self.create_bardata(
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lambda: midnight,
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)
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yesterday_bar_data = self.create_bardata(
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lambda: last_minute
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)
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# Find the closest prior minute when the trading calendar was
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# open (note that if the calendar is open at `sunday_midnight`,
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# this will be `sunday_midnight`).
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trading_minutes = self.trading_calendar.all_minutes
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last_minute = trading_minutes[trading_minutes <= sunday_midnight][-1]
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with handle_non_market_minutes(midnight_bar_data):
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sunday_midnight_bar_data = self.create_bardata(lambda: sunday_midnight)
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last_minute_bar_data = self.create_bardata(lambda: last_minute)
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# Ensure that we get the same results at midnight on Sunday as
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# the last open minute.
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with handle_non_market_minutes(sunday_midnight_bar_data):
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for field in ALL_FIELDS:
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np.testing.assert_array_equal(
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midnight_bar_data.history(self.ASSET2, field, 30, '1m'),
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yesterday_bar_data.history(self.ASSET2, field, 30, '1m')
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sunday_midnight_bar_data.history(
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self.ASSET2,
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field,
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30,
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'1m',
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),
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last_minute_bar_data.history(self.ASSET2, field, 30, '1m')
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)
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def test_minute_after_asset_stopped(self):
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# SHORT_ASSET's last day was 2015-01-06
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# get some history windows that straddle the end
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minutes = self.trading_calendar.minutes_for_session(
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minutes = self.trading_calendars[Equity].minutes_for_session(
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pd.Timestamp('2015-01-07', tz='UTC')
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)[0:60]
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@@ -867,15 +874,6 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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# Reset data portal because it has advanced past next test date.
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data_portal = self.make_data_portal()
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# choose a window that contains the last minute of the asset
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bar_data = BarData(
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data_portal=data_portal,
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simulation_dt_func=lambda: minutes[15],
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data_frequency='minute',
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restrictions=NoRestrictions(),
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trading_calendar=self.trading_calendar,
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)
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# close high low open price volume
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# 2015-01-06 20:47:00+00:00 768 770 767 769 768 76800
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# 2015-01-06 20:48:00+00:00 769 771 768 770 769 76900
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@@ -908,9 +906,29 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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# 2015-01-07 14:45:00+00:00 NaN NaN NaN NaN NaN 0
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# 2015-01-07 14:46:00+00:00 NaN NaN NaN NaN NaN 0
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window = bar_data.history(self.SHORT_ASSET, ALL_FIELDS, 30, '1m')
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# choose a window that contains the last minute of the asset
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window_start = pd.Timestamp('2015-01-06 20:47', tz='UTC')
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window_end = pd.Timestamp('2015-01-07 14:46', tz='UTC')
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# there should be 14 values and 16 NaNs/0s
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bar_data = BarData(
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data_portal=data_portal,
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simulation_dt_func=lambda: minutes[15],
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data_frequency='minute',
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restrictions=NoRestrictions(),
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trading_calendar=self.trading_calendar,
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)
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bar_count = len(
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self.trading_calendar.minutes_in_range(window_start, window_end)
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)
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window = bar_data.history(
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self.SHORT_ASSET,
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ALL_FIELDS,
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bar_count,
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'1m',
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)
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# Window should start with 14 values and end with 16 NaNs/0s.
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for field in ALL_FIELDS:
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if field == 'volume':
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np.testing.assert_array_equal(
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@@ -953,9 +971,10 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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for asset in [self.SPLIT_ASSET, self.MERGER_ASSET]:
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# before any of the adjustments, last 10 minutes of jan 5
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equity_cal = self.trading_calendars[Equity]
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window1 = self.data_portal.get_history_window(
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[asset],
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self.trading_calendar.open_and_close_for_session(jan5)[1],
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equity_cal.open_and_close_for_session(jan5)[1],
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10,
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'1m',
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'close'
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@@ -964,11 +983,19 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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np.testing.assert_array_equal(
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np.array(range(8380, 8390)), window1)
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# straddling the first event
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# straddling the first event - begins with the last 5 equity
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# minutes on 2015-01-05, ends with the first 5 on
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# 2015-01-06.
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window2_start = pd.Timestamp('2015-01-05 20:56', tz='UTC')
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window2_end = pd.Timestamp('2015-01-06 14:35', tz='UTC')
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window2_count = len(self.trading_calendar.minutes_in_range(
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window2_start,
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window2_end,
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))
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window2 = self.data_portal.get_history_window(
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[asset],
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pd.Timestamp('2015-01-06 14:35', tz='UTC'),
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10,
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window2_count,
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'1m',
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'close'
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)[asset]
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@@ -979,22 +1006,33 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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2096.5,
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2096.75,
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2097,
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2097.25,
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# Split occurs. The value of the thousands place should
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# match.
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2000,
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2097.25],
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window2[:5],
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)
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# Split occurs. The value of the thousands place should
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# match.
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np.testing.assert_array_equal(
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[2000,
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2001,
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2002,
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2003,
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2004],
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window2
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window2[-5:],
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)
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# straddling both events!
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# straddling both events! on the equities calendar this is 5
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# minutes of 1/7, 390 of 1/6, and 5 minutes of 1/5.
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window3_start = pd.Timestamp('2015-01-05 20:56', tz='UTC')
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window3_end = pd.Timestamp('2015-01-07 14:35', tz='UTC')
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window3_minutes = self.trading_calendar.minutes_in_range(
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window3_start,
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window3_end,
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)
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window3_count = len(window3_minutes)
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window3 = self.data_portal.get_history_window(
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[asset],
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pd.Timestamp('2015-01-07 14:35', tz='UTC'),
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400, # 5 minutes of 1/7, 390 of 1/6, and 5 minutes of 1/5
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window3_count,
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'1m',
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'close'
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)[asset]
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@@ -1005,14 +1043,21 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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window3[0:5]
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)
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# next 390 minutes should be 2000-2390, but halved
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# next 390 minutes (the 2015-01-06 session) should be
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# 2000-2390, but halved
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middle_day_open_i = window3_minutes.searchsorted(
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pd.Timestamp('2015-01-06 14:31', tz='UTC')
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)
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middle_day_close_i = window3_minutes.searchsorted(
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pd.Timestamp('2015-01-06 21:00', tz='UTC')
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)
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np.testing.assert_array_equal(
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np.array(range(2000, 2390), dtype='float64') / 2,
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window3[5:395]
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window3[middle_day_open_i:middle_day_close_i + 1]
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)
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# final 5 minutes should be 1000-1004
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np.testing.assert_array_equal(range(1000, 1005), window3[395:])
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np.testing.assert_array_equal(range(1000, 1005), window3[-5:])
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# after last event
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window4 = self.data_portal.get_history_window(
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@@ -1040,11 +1085,16 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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np.testing.assert_array_equal(np.array(range(382, 392)), window1)
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# straddling the first dividend
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# straddling the first dividend (10 active equity minutes)
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window2_start = pd.Timestamp('2015-01-05 20:56', tz='UTC')
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window2_end = pd.Timestamp('2015-01-06 14:35', tz='UTC')
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window2_count = len(
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self.trading_calendar.minutes_in_range(window2_start, window2_end)
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)
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window2 = self.data_portal.get_history_window(
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[self.DIVIDEND_ASSET],
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pd.Timestamp('2015-01-06 14:35', tz='UTC'),
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10,
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window2_end,
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window2_count,
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'1m',
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'close'
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)[self.DIVIDEND_ASSET]
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@@ -1057,13 +1107,21 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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)
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# second half of window is unadjusted
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np.testing.assert_array_equal(range(392, 397), window2[5:])
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np.testing.assert_array_equal(range(392, 397), window2[-5:])
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# straddling both dividends
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# straddling both dividends (on the equities calendar, this is
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# 5 minutes of 1/7, 390 of 1/6, and 5 minutes of 1/5).
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window3_start = pd.Timestamp('2015-01-05 20:56', tz='UTC')
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window3_end = pd.Timestamp('2015-01-07 14:35', tz='UTC')
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window3_minutes = self.trading_calendar.minutes_in_range(
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window3_start,
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window3_end,
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)
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window3_count = len(window3_minutes)
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window3 = self.data_portal.get_history_window(
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[self.DIVIDEND_ASSET],
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pd.Timestamp('2015-01-07 14:35', tz='UTC'),
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400, # 5 minutes of 1/7, 390 of 1/6, and 5 minutes of 1/5
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window3_end,
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window3_count,
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'1m',
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'close'
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)[self.DIVIDEND_ASSET]
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@@ -1074,14 +1132,21 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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window3[0:5]
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)
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# next 390 minutes should be hit by 0.96 (second dividend)
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# next 390 minutes (the 2015-01-06 session) should be hit by 0.96
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# (second dividend)
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middle_day_open_i = window3_minutes.searchsorted(
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pd.Timestamp('2015-01-06 14:31', tz='UTC')
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)
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middle_day_close_i = window3_minutes.searchsorted(
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pd.Timestamp('2015-01-06 21:00', tz='UTC')
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)
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np.testing.assert_array_almost_equal(
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np.array(range(392, 782), dtype='float64') * 0.96,
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window3[5:395]
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window3[middle_day_open_i:middle_day_close_i + 1]
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)
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# last 5 minutes should not be adjusted
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np.testing.assert_array_equal(np.array(range(782, 787)), window3[395:])
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np.testing.assert_array_equal(np.array(range(782, 787)), window3[-5:])
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def test_passing_iterable_to_history_regular_hours(self):
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# regular hours
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@@ -1129,50 +1194,70 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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'price': np.arange(382, 392) / 2.0,
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}
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# Use a window looking back to 3:51pm from 8:45am the following day.
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# This contains the last ten minutes of the equity session for
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# 2015-01-05.
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window_start = pd.Timestamp('2015-01-05 20:51', tz='UTC')
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window_end = pd.Timestamp('2015-01-06 13:44', tz='UTC')
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window_length = len(
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self.trading_calendar.minutes_in_range(window_start, window_end)
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)
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with handle_non_market_minutes(bar_data):
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# Single field, single asset
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for field in ALL_FIELDS:
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values = bar_data.history(self.SPLIT_ASSET, field, 10, '1m')
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np.testing.assert_array_equal(values.values,
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values = bar_data.history(
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self.SPLIT_ASSET,
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field,
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window_length,
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'1m',
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)
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# The first 10 bars the `values` correspond to the last
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# 10 minutes in the 2015-01-05 session.
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np.testing.assert_array_equal(values.values[:10],
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adj_expected[field],
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err_msg=field)
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# Multi field, single asset
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values = bar_data.history(
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self.SPLIT_ASSET, ['open', 'volume'], 10, '1m'
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self.SPLIT_ASSET, ['open', 'volume'], window_length, '1m'
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)
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np.testing.assert_array_equal(values.open.values,
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np.testing.assert_array_equal(values.open.values[:10],
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adj_expected['open'])
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np.testing.assert_array_equal(values.volume.values,
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np.testing.assert_array_equal(values.volume.values[:10],
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adj_expected['volume'])
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# Single field, multi asset
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values = bar_data.history(
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[self.SPLIT_ASSET, self.ASSET2], 'open', 10, '1m'
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[self.SPLIT_ASSET, self.ASSET2], 'open', window_length, '1m'
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)
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np.testing.assert_array_equal(values[self.SPLIT_ASSET].values,
|
||||
np.testing.assert_array_equal(values[self.SPLIT_ASSET].values[:10],
|
||||
adj_expected['open'])
|
||||
np.testing.assert_array_equal(values[self.ASSET2].values,
|
||||
np.testing.assert_array_equal(values[self.ASSET2].values[:10],
|
||||
expected['open'] * 2)
|
||||
|
||||
# Multi field, multi asset
|
||||
values = bar_data.history(
|
||||
[self.SPLIT_ASSET, self.ASSET2], ['open', 'volume'], 10, '1m'
|
||||
[self.SPLIT_ASSET, self.ASSET2],
|
||||
['open', 'volume'],
|
||||
window_length,
|
||||
'1m',
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
values.open[self.SPLIT_ASSET].values,
|
||||
values.open[self.SPLIT_ASSET].values[:10],
|
||||
adj_expected['open']
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
values.volume[self.SPLIT_ASSET].values,
|
||||
values.volume[self.SPLIT_ASSET].values[:10],
|
||||
adj_expected['volume']
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
values.open[self.ASSET2].values,
|
||||
values.open[self.ASSET2].values[:10],
|
||||
expected['open'] * 2
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
values.volume[self.ASSET2].values,
|
||||
values.volume[self.ASSET2].values[:10],
|
||||
expected['volume'] / 2
|
||||
)
|
||||
|
||||
@@ -1182,12 +1267,16 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
#
|
||||
# five minutes into the day after the early close, get 20 1m bars
|
||||
|
||||
dt = pd.Timestamp('2014-07-07 13:35:00', tz='UTC')
|
||||
cal = self.trading_calendar
|
||||
|
||||
window_start = pd.Timestamp('2014-07-03 16:46:00', tz='UTC')
|
||||
window_end = pd.Timestamp('2014-07-07 13:35:00', tz='UTC')
|
||||
bar_count = len(cal.minutes_in_range(window_start, window_end))
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.HALF_DAY_TEST_ASSET],
|
||||
dt,
|
||||
20,
|
||||
window_end,
|
||||
bar_count,
|
||||
'1m',
|
||||
'close'
|
||||
)[self.HALF_DAY_TEST_ASSET]
|
||||
@@ -1196,10 +1285,20 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
# first minute of 7/7 is the 600th trading minute for this asset
|
||||
# this asset's first minute had a close value of 2, so every value is
|
||||
# 2 + (minute index)
|
||||
np.testing.assert_array_equal(range(587, 607), window)
|
||||
expected = range(587, 607)
|
||||
|
||||
# First 15 bars on occur at the end of 2014-07-03.
|
||||
np.testing.assert_array_equal(window[:15], expected[:15])
|
||||
# Interim bars (only on other calendars) should all be nan.
|
||||
np.testing.assert_array_equal(
|
||||
window[15:-5],
|
||||
np.full(len(window) - 20, np.nan),
|
||||
)
|
||||
# Last 5 bars occur at the start of 2014-07-07.
|
||||
np.testing.assert_array_equal(window[-5:], expected[-5:])
|
||||
|
||||
self.assertEqual(
|
||||
window.index[-6],
|
||||
window.index[14],
|
||||
pd.Timestamp('2014-07-03 17:00', tz='UTC')
|
||||
)
|
||||
|
||||
@@ -1209,34 +1308,58 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
)
|
||||
|
||||
def test_minute_different_lifetimes(self):
|
||||
cal = self.trading_calendar
|
||||
equity_cal = self.trading_calendars[Equity]
|
||||
|
||||
# at trading start, only asset1 existed
|
||||
day = self.trading_calendar.next_session_label(self.TRADING_START_DT)
|
||||
|
||||
asset1_minutes = \
|
||||
self.trading_calendar.minutes_for_sessions_in_range(
|
||||
self.ASSET1.start_date,
|
||||
self.ASSET1.end_date
|
||||
)
|
||||
# Range containing 100 equity minutes, possibly more on other
|
||||
# calendars (i.e. futures).
|
||||
window_start = pd.Timestamp('2014-01-03 19:22', tz='UTC')
|
||||
window_end = pd.Timestamp('2014-01-06 14:31', tz='UTC')
|
||||
bar_count = len(cal.minutes_in_range(window_start, window_end))
|
||||
|
||||
asset1_idx = asset1_minutes.searchsorted(
|
||||
self.trading_calendar.open_and_close_for_session(day)[0]
|
||||
equity_cal = self.trading_calendars[Equity]
|
||||
first_equity_open, _ = equity_cal.open_and_close_for_session(day)
|
||||
|
||||
asset1_minutes = equity_cal.minutes_for_sessions_in_range(
|
||||
self.ASSET1.start_date,
|
||||
self.ASSET1.end_date
|
||||
)
|
||||
asset1_idx = asset1_minutes.searchsorted(first_equity_open)
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.ASSET1, self.ASSET2],
|
||||
self.trading_calendar.open_and_close_for_session(day)[0],
|
||||
100,
|
||||
first_equity_open,
|
||||
bar_count,
|
||||
'1m',
|
||||
'close'
|
||||
)
|
||||
|
||||
expected = range(asset1_idx - 97, asset1_idx + 3)
|
||||
|
||||
# First 99 bars occur on the previous day,
|
||||
np.testing.assert_array_equal(
|
||||
range(asset1_idx - 97, asset1_idx + 3),
|
||||
window[self.ASSET1]
|
||||
window[self.ASSET1][:99],
|
||||
expected[:99],
|
||||
)
|
||||
# Any interim bars are not active equity minutes, so should all
|
||||
# be nan.
|
||||
np.testing.assert_array_equal(
|
||||
window[self.ASSET1][99:-1],
|
||||
np.full(len(window) - 100, np.nan),
|
||||
)
|
||||
# Final bar in the window is the first equity bar of `day`.
|
||||
np.testing.assert_array_equal(
|
||||
window[self.ASSET1][-1:],
|
||||
expected[-1:],
|
||||
)
|
||||
|
||||
# All NaNs for ASSET2, since it hasn't started yet.
|
||||
np.testing.assert_array_equal(
|
||||
np.full(100, np.nan), window[self.ASSET2]
|
||||
window[self.ASSET2],
|
||||
np.full(len(window), np.nan),
|
||||
)
|
||||
|
||||
def test_history_window_before_first_trading_day(self):
|
||||
@@ -1264,11 +1387,17 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
day = pd.Timestamp('2015-01-07', tz='UTC')
|
||||
minutes = self.trading_calendar.minutes_for_session(day)
|
||||
|
||||
equity_cal = self.trading_calendars[Equity]
|
||||
equity_minutes = equity_cal.minutes_for_session(day)
|
||||
equity_open, equity_close = equity_minutes[0], equity_minutes[-1]
|
||||
|
||||
# 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 minute in minutes:
|
||||
idx = equity_minutes.searchsorted(min(minute, equity_close))
|
||||
|
||||
for field in ALL_FIELDS:
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
@@ -1299,7 +1428,20 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
|
||||
last_val = -1
|
||||
|
||||
if field == 'open':
|
||||
# XXX
|
||||
if minute == day:
|
||||
continue
|
||||
|
||||
if minute < equity_open:
|
||||
# If before the equity calendar open, we don't yet
|
||||
# have data (but price is forward-filled).
|
||||
if field == 'volume':
|
||||
last_val = 0
|
||||
elif field == 'price':
|
||||
last_val = window[1]
|
||||
else:
|
||||
last_val = nan
|
||||
elif field == 'open':
|
||||
last_val = 783
|
||||
elif field == 'high':
|
||||
# since we increase monotonically, it's just the last
|
||||
@@ -1317,7 +1459,7 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
|
||||
last_val = sum(np.array(range(782, 782 + idx + 1)) * 100)
|
||||
|
||||
self.assertEqual(window[-1], last_val)
|
||||
np.testing.assert_equal(window[-1], last_val)
|
||||
|
||||
@parameterized.expand(ALL_FIELDS)
|
||||
def test_daily_history_blended_gaps(self, field):
|
||||
@@ -1328,11 +1470,16 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
day = pd.Timestamp('2015-01-08', tz='UTC')
|
||||
minutes = self.trading_calendar.minutes_for_session(day)
|
||||
|
||||
equity_cal = self.trading_calendars[Equity]
|
||||
equity_minutes = equity_cal.minutes_for_session(day)
|
||||
equity_open, equity_close = equity_minutes[0], equity_minutes[-1]
|
||||
|
||||
# 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 minute in minutes:
|
||||
idx = equity_minutes.searchsorted(min(minute, equity_close))
|
||||
|
||||
window = self.data_portal.get_history_window(
|
||||
[self.ASSET2],
|
||||
@@ -1363,9 +1510,22 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
|
||||
self.assertEqual(window[0], 22873500)
|
||||
self.assertEqual(window[1], 38083500)
|
||||
|
||||
# XXX
|
||||
if minute == day:
|
||||
continue
|
||||
|
||||
last_val = -1
|
||||
|
||||
if field == 'open':
|
||||
if minute < equity_open:
|
||||
# If before the equity calendar open, we don't yet
|
||||
# have data (but price is forward-filled).
|
||||
if field == 'volume':
|
||||
last_val = 0
|
||||
elif field == 'price':
|
||||
last_val = window[1]
|
||||
else:
|
||||
last_val = nan
|
||||
elif field == 'open':
|
||||
if idx == 0:
|
||||
last_val = np.nan
|
||||
else:
|
||||
@@ -1902,6 +2062,11 @@ class NoPrefetchDailyEquityHistoryTestCase(DailyEquityHistoryTestCase):
|
||||
DATA_PORTAL_DAILY_HISTORY_PREFETCH = 0
|
||||
|
||||
|
||||
class DailyEquityHistoryOnFuturesCalendarTestCase(DailyEquityHistoryTestCase):
|
||||
class MinuteEquityHistoryFuturesCalendarTestCase(MinuteEquityHistoryTestCase):
|
||||
TRADING_CALENDAR_STRS = ('NYSE', 'us_futures')
|
||||
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
|
||||
|
||||
|
||||
class DailyEquityHistoryFuturesCalendarTestCase(DailyEquityHistoryTestCase):
|
||||
TRADING_CALENDAR_STRS = ('NYSE', 'us_futures')
|
||||
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
|
||||
|
||||
Reference in New Issue
Block a user