WIP: Refactors tests to use TradingSchedule

tests/pipeline/base.py

@@ -21,7 +21,7 @@ from zipline.testing import (
 
 from zipline.utils.functional import dzip_exact
 from zipline.utils.pandas_utils import explode
-from zipline.utils.tradingcalendar import trading_day
+from zipline.utils.calendars import default_nyse_schedule
 
 
 def with_defaults(**default_funcs):
@@ -55,7 +55,8 @@ class BasePipelineTestCase(TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.__calendar = date_range('2014', '2015', freq=trading_day)
+        cls.__calendar = date_range('2014', '2015',
+                                    freq=default_nyse_schedule.day)
         cls.__assets = assets = Int64Index(arange(1, 20))
         cls.__tmp_finder_ctx = tmp_asset_finder(
             equities=make_simple_equity_info(

tests/test_algorithm.py

@@ -3211,7 +3211,7 @@ class TestOrderCancelation(WithDataPortal,
             sim_params=SimulationParameters(
                 period_start=self.sim_params.period_start,
                 period_end=self.sim_params.period_end,
-                trading_schedule=self.env,
+                trading_schedule=default_nyse_schedule,
                 data_frequency=data_frequency,
                 emission_rate='minute' if minute_emission else 'daily'
             )
@@ -3401,6 +3401,7 @@ class TestEquityAutoClose(WithTmpDir, ZiplineTestCase):
     @classmethod
     def init_class_fixtures(cls):
         super(TestEquityAutoClose, cls).init_class_fixtures()
+        trading_days = default_nyse_schedule.all_execution_days
         start_date = pd.Timestamp('2015-01-05', tz='UTC')
         start_date_loc = trading_days.get_loc(start_date)
         test_duration = 7
@@ -3416,7 +3417,7 @@ class TestEquityAutoClose(WithTmpDir, ZiplineTestCase):
             num_assets=3,
             start_date=self.test_days[0],
             first_end=self.first_asset_expiration,
-            frequency=trading_day,
+            frequency=default_nyse_schedule.day,
             periods_between_ends=2,
             auto_close_delta=auto_close_delta,
         )
@@ -3578,7 +3579,7 @@ class TestEquityAutoClose(WithTmpDir, ZiplineTestCase):
         Make sure that after an equity gets delisted, our portfolio holds the
         correct number of equities and correct amount of cash.
         """
-        auto_close_delta = trading_day * auto_close_lag
+        auto_close_delta = default_nyse_schedule.day * auto_close_lag
         resources = self.make_data(auto_close_delta, 'daily', capital_base)
 
         assets = resources.assets
@@ -3738,7 +3739,7 @@ class TestEquityAutoClose(WithTmpDir, ZiplineTestCase):
         canceled.  Unless an equity is auto closed, any open orders for that
         equity will persist indefinitely.
         """
-        auto_close_delta = trading_day
+        auto_close_delta = default_nyse_schedule.day
         resources = self.make_data(auto_close_delta, 'daily')
         env = resources.env
         assets = resources.assets
@@ -3810,7 +3811,7 @@ class TestEquityAutoClose(WithTmpDir, ZiplineTestCase):
         )
 
     def test_minutely_delisted_equities(self):
-        resources = self.make_data(trading_day, 'minute')
+        resources = self.make_data(default_nyse_schedule.day, 'minute')
 
         env = resources.env
         assets = resources.assets

tests/test_api_shim.py

@@ -19,6 +19,7 @@ from zipline.testing.fixtures import (
     ZiplineTestCase,
 )
 from zipline.zipline_warnings import ZiplineDeprecationWarning
+from zipline.utils.calendars import default_nyse_schedule
 
 simple_algo = """
 from zipline.api import sid, order
@@ -124,7 +125,7 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
     def make_minute_bar_data(cls):
         for sid in cls.sids:
             yield sid, create_minute_df_for_asset(
-                cls.env,
+                default_nyse_schedule,
                 cls.SIM_PARAMS_START,
                 cls.SIM_PARAMS_END,
             )
@@ -133,7 +134,7 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
     def make_daily_bar_data(cls):
         for sid in cls.sids:
             yield sid, create_daily_df_for_asset(
-                cls.env,
+                default_nyse_schedule,
                 cls.SIM_PARAMS_START,
                 cls.SIM_PARAMS_END,
             )
@@ -179,10 +180,10 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
         similar)  and the new data API(data.current(sid(N), field) and
         similar) hit the same code paths on the DataPortal.
         """
-        test_start_minute = self.env.market_minutes_for_day(
+        test_start_minute = default_nyse_schedule.execution_minutes_for_day(
             self.sim_params.trading_days[0]
         )[1]
-        test_end_minute = self.env.market_minutes_for_day(
+        test_end_minute = default_nyse_schedule.execution_minutes_for_day(
             self.sim_params.trading_days[0]
         )[-1]
         bar_data = BarData(
@@ -260,7 +261,7 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
             period_start=test_start_minute,
             period_end=test_end_minute,
             data_frequency="minute",
-            env=self.env
+            trading_schedule=default_nyse_schedule,
         )
 
         history_algorithm = self.create_algo(
@@ -381,7 +382,7 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
                 capital_base=self.sim_params.capital_base,
                 data_frequency=self.sim_params.data_frequency,
                 emission_rate=self.sim_params.emission_rate,
-                env=self.env,
+                trading_schedule=default_nyse_schedule,
             )
 
             algo = self.create_algo(history_algo,
@@ -424,7 +425,7 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
                 period_start=self.sim_params.trading_days[8],
                 period_end=self.sim_params.trading_days[-1],
                 data_frequency="minute",
-                env=self.env
+                trading_schedule=default_nyse_schedule,
             )
 
             algo = self.create_algo(simple_transforms_algo,

tests/test_bar_data.py

@@ -439,8 +439,8 @@ class TestMinuteBarData(WithBarDataChecks,
     def test_can_trade_at_midnight(self):
         # make sure that if we use `can_trade` at midnight, we don't pretend
         # we're in the previous day's last minute
-        the_day_after = self.env.next_trading_day(
-            self.bcolz_minute_bar_days[-1],
+        the_day_after = self.trading_schedule.next_execution_day(
+            self.bcolz_minute_bar_days[-1]
         )
 
         bar_data = BarData(self.data_portal, lambda: the_day_after, "minute")

tests/test_data_portal.py

@@ -16,6 +16,7 @@ from pandas.tslib import Timedelta
 
 from zipline.data.data_portal import DataPortal
 from zipline.testing.fixtures import WithTradingEnvironment, ZiplineTestCase
+from zipline.utils.calendars import default_nyse_schedule
 import pandas as pd
 
 
@@ -26,7 +27,8 @@ class TestDataPortal(WithTradingEnvironment, ZiplineTestCase):
     def init_instance_fixtures(self):
         super(TestDataPortal, self).init_instance_fixtures()
 
-        self.data_portal = DataPortal(self.env, first_trading_day=None)
+        self.data_portal = DataPortal(self.env, default_nyse_schedule,
+                                      first_trading_day=None)
 
     def test_bar_count_for_simple_transforms(self):
         # July 2015
@@ -40,7 +42,7 @@ class TestDataPortal(WithTradingEnvironment, ZiplineTestCase):
         # half an hour into july 9, getting a 4-"day" window should get us
         # all the minutes of 7/6, 7/7, 7/8, and 31 minutes of 7/9
 
-        july_9_dt = self.env.get_open_and_close(
+        july_9_dt = default_nyse_schedule.start_and_end(
             pd.Timestamp("2015-07-09")
         )[0] + Timedelta("30 minutes")
 
@@ -63,7 +65,7 @@ class TestDataPortal(WithTradingEnvironment, ZiplineTestCase):
         # half an hour into nov 30, getting a 4-"day" window should get us
         # all the minutes of 11/24, 11/25, 11/27 (half day!), and 31 minutes
         # of 11/30
-        nov_30_dt = self.env.get_open_and_close(
+        nov_30_dt = default_nyse_schedule.start_and_end(
             pd.Timestamp("2015-11-30")
         )[0] + Timedelta("30 minutes")
 

tests/test_finance.py

@@ -280,7 +280,8 @@ class FinanceTestCase(WithLogger,
             else:
                 alternator = 1
 
-            tracker = PerformanceTracker(sim_params, self.env)
+            tracker = PerformanceTracker(sim_params, default_nyse_schedule,
+                                         self.env)
 
             # replicate what tradesim does by going through every minute or day
             # of the simulation and processing open orders each time
@@ -558,18 +559,3 @@ class TradingEnvironmentTestCase(WithLogger,
         self.assertTrue(all(today == minutes[:31]))
         self.assertTrue(all(friday == minutes[31:421]))
         self.assertTrue(all(thursday == minutes[421:]))
-
-    def test_min_date(self):
-        min_date = pd.Timestamp('2016-03-04', tz='UTC')
-        env = TradingEnvironment(min_date=min_date)
-
-        self.assertGreaterEqual(env.first_trading_day, min_date)
-        self.assertGreaterEqual(env.treasury_curves.index[0],
-                                min_date)
-
-    def test_max_date(self):
-        max_date = pd.Timestamp('2008-08-01', tz='UTC')
-        env = TradingEnvironment(max_date=max_date)
-
-        self.assertLessEqual(env.treasury_curves.index[-1],
-                             max_date)

tests/test_history.py

@@ -1136,6 +1136,7 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
     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
+        self.data_portal._first_trading_day = self.TRADING_START_DT
         first_day_minutes = default_nyse_schedule.execution_minutes_for_day(
             self.TRADING_START_DT
         )
@@ -1670,8 +1671,8 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
             )[self.ASSET1]
 
         # Use a minute to force minute mode.
-        first_minute = default_nyse_schedule.open_and_closes.market_open[
-            self.TRADING_START_DT]
+        first_minute = \
+            default_nyse_schedule.schedule.market_open[self.TRADING_START_DT]
 
         with self.assertRaisesRegexp(HistoryWindowStartsBeforeData, exp_msg):
             self.data_portal.get_history_window(

tests/utils/test_events.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from collections import namedtuple
 import datetime
 from functools import partial
 from inspect import isabstract
@@ -25,7 +24,6 @@ import pandas as pd
 from six import iteritems
 from six.moves import range, map
 
-from zipline.finance.trading import TradingEnvironment
 from zipline.testing import subtest, parameter_space
 import zipline.utils.events
 from zipline.utils.calendars import get_calendar
@@ -213,7 +211,7 @@ def minutes_for_days(ordered_days=False):
         # optimization in AfterOpen and BeforeClose, we rely on the fact that
         # the clock only ever moves forward in a simulation. For those cases,
         # we guarantee that the list of trading days we test is ordered.
-        ordered_day_list = random.sample(list(env.trading_days), 500)
+        ordered_day_list = random.sample(list(cal.all_trading_days), 500)
         ordered_day_list.sort()
 
         def day_picker(day):

zipline/algorithm.py

@@ -906,7 +906,8 @@ class TradingAlgorithm(object):
             url,
             pre_func,
             post_func,
-            self.trading_environment,
+            self.asset_finder,
+            self.trading_schedule.day,
             self.sim_params.period_start,
             self.sim_params.period_end,
             date_column,
@@ -1945,7 +1946,9 @@ class TradingAlgorithm(object):
             # If we are in before_trading_start, we need to get the window
             # as of the previous market minute
             adjusted_dt = \
-                self.data_portal.env.previous_market_minute(self.datetime)
+                self.data_portal.trading_schedule.previous_execution_minute(
+                    self.datetime
+                )
 
             window = self.data_portal.get_history_window(
                 assets,

zipline/data/data_portal.py

@@ -1018,12 +1018,13 @@ class DataPortal(object):
         tds = self.trading_schedule.all_execution_days
         end_loc = tds.get_loc(end_date)
         start_loc = end_loc - bar_count + 1
-        start_dt = tds[start_loc]
-        if start_dt < self._first_trading_day:
+        if start_loc < self._first_trading_day_loc:
             raise HistoryWindowStartsBeforeData(
                 first_trading_day=self._first_trading_day.date(),
                 bar_count=bar_count,
-                suggested_start_day=tds[bar_count].date(),
+                suggested_start_day=tds[
+                    self._first_trading_day_loc + bar_count
+                ].date(),
             )
         return tds[start_loc:end_loc + 1]
 
@@ -1175,12 +1176,14 @@ class DataPortal(object):
         mm = self.trading_schedule.all_execution_minutes
         end_loc = mm.get_loc(end_dt)
         start_loc = end_loc - bar_count + 1
-        if start_loc < 0:
-            suggested_start_day = \
-                (mm[bar_count] + self.trading_schedule.day).date()
+        if start_loc < self._first_trading_minute_loc:
+            suggested_start_day = (
+                mm[
+                    self._first_trading_minute_loc + bar_count
+                ] + self.trading_schedule.day
+            ).date()
             raise HistoryWindowStartsBeforeData(
-                first_trading_day=\
-                    self.trading_schedule.first_execution_day.date(),
+                first_trading_day=self._first_trading_day.date(),
                 bar_count=bar_count,
                 suggested_start_day=suggested_start_day,
             )
@@ -1704,11 +1707,13 @@ class DataPortal(object):
             previous_day,
         )
 
-        minutes_count = \
-            sum(210 if day in self.env.early_closes else 390 for day in days)
+        minutes_count = sum(
+            210 if day in self.trading_schedule.early_ends
+            else 390 for day in days
+        )
 
         # add the minutes for today
-        today_open = self.env.get_open_and_close(ending_minute)[0]
+        today_open = self.trading_schedule.start_and_end(ending_minute)[0]
         minutes_count += \
             ((ending_minute - today_open).total_seconds() // 60) + 1
 

zipline/finance/performance/position_tracker.py

@@ -384,7 +384,7 @@ class PositionTracker(object):
                 last_sale_price = data_portal.get_adjusted_value(
                     asset,
                     'price',
-                    data_portal.env.previous_market_minute(dt),
+                    data_portal.trading_schedule.previous_execution_minute(dt),
                     dt,
                     self.data_frequency
                 )

zipline/finance/trading.py

@@ -78,8 +78,6 @@ class TradingEnvironment(object):
         load=None,
         bm_symbol='^GSPC',
         exchange_tz="US/Eastern",
-        min_date=None,
-        max_date=None,
         trading_schedule=default_nyse_schedule,
         asset_db_path=':memory:'
     ):
@@ -94,13 +92,13 @@ class TradingEnvironment(object):
             self.bm_symbol,
         )
 
-        if max_date:
-            tr_c = self.treasury_curves
-            # Mask the treasury curves down to the current date.
-            # In the case of live trading, the last date in the treasury
-            # curves would be the day before the date considered to be
-            # 'today'.
-            self.treasury_curves = tr_c[tr_c.index <= max_date]
+        #if max_date:
+        #    tr_c = self.treasury_curves
+        #    # Mask the treasury curves down to the current date.
+        #    # In the case of live trading, the last date in the treasury
+        #    # curves would be the day before the date considered to be
+        #    # 'today'.
+        #    self.treasury_curves = tr_c[tr_c.index <= max_date]
 
         self.exchange_tz = exchange_tz
 

zipline/sources/requests_csv.py

@@ -26,14 +26,14 @@ from zipline.assets import Equity
 logger = Logger('Requests Source Logger')
 
 
-def roll_dts_to_midnight(dts, env):
+def roll_dts_to_midnight(dts, trading_day):
     if len(dts) == 0:
         return dts
 
     return pd.DatetimeIndex(
         (dts.tz_convert('US/Eastern') - pd.Timedelta(hours=16)).date,
         tz='UTC',
-    ) + env.trading_day
+    ) + trading_day
 
 
 class FetcherEvent(Event):
@@ -144,7 +144,8 @@ class PandasCSV(with_metaclass(ABCMeta, object)):
     def __init__(self,
                  pre_func,
                  post_func,
-                 env,
+                 asset_finder,
+                 trading_day,
                  start_date,
                  end_date,
                  date_column,
@@ -175,8 +176,8 @@ class PandasCSV(with_metaclass(ABCMeta, object)):
 
         self.symbol = symbol
 
-        self.env = env
-        self.finder = env.asset_finder
+        self.finder = asset_finder
+        self.trading_day = trading_day
 
         self.pre_func = pre_func
         self.post_func = post_func
@@ -194,7 +195,7 @@ class PandasCSV(with_metaclass(ABCMeta, object)):
 
     @staticmethod
     def parse_date_str_series(format_str, tz, date_str_series, data_frequency,
-                              env):
+                              trading_day):
         """
         Efficient parsing for a 1d Pandas/numpy object containing string
         representations of dates.
@@ -233,7 +234,7 @@ class PandasCSV(with_metaclass(ABCMeta, object)):
             ).tz_localize(tz_str).tz_convert('UTC')
 
         if data_frequency == 'daily':
-            parsed = roll_dts_to_midnight(parsed, env)
+            parsed = roll_dts_to_midnight(parsed, trading_day)
         return parsed
 
     def mask_pandas_args(self, kwargs):
@@ -290,7 +291,7 @@ class PandasCSV(with_metaclass(ABCMeta, object)):
             self.timezone,
             df[self.date_column],
             self.data_frequency,
-            self.env
+            self.trading_day,
         ).values
 
         # ignore rows whose dates we couldn't parse
@@ -456,7 +457,8 @@ class PandasRequestsCSV(PandasCSV):
                  url,
                  pre_func,
                  post_func,
-                 env,
+                 asset_finder,
+                 trading_day,
                  start_date,
                  end_date,
                  date_column,
@@ -488,7 +490,8 @@ class PandasRequestsCSV(PandasCSV):
         super(PandasRequestsCSV, self).__init__(
             pre_func,
             post_func,
-            env,
+            asset_finder,
+            trading_day,
             start_date,
             end_date,
             date_column,

zipline/testing/core.py

@@ -702,9 +702,11 @@ class FakeDataPortal(DataPortal):
     def get_history_window(self, assets, end_dt, bar_count, frequency, field,
                            ffill=True):
         if frequency == "1d":
-            end_idx = self.env.trading_days.searchsorted(end_dt)
-            days = \
-                self.env.trading_days[(end_idx - bar_count + 1):(end_idx + 1)]
+            end_idx = \
+                self.trading_schedule.all_execution_days.searchsorted(end_dt)
+            days = self.trading_schedule.all_execution_days[
+                   (end_idx - bar_count + 1):(end_idx + 1)
+                   ]
 
             df = pd.DataFrame(
                 np.full((bar_count, len(assets)), 100),

zipline/testing/fixtures.py

@@ -398,8 +398,6 @@ class WithTradingEnvironment(WithAssetFinder):
     --------
     :class:`zipline.finance.trading.TradingEnvironment`
     """
-    TRADING_ENV_MIN_DATE = None
-    TRADING_ENV_MAX_DATE = None
     TRADING_ENV_TRADING_SCHEDULE = default_nyse_schedule
 
     @classmethod
@@ -411,8 +409,6 @@ class WithTradingEnvironment(WithAssetFinder):
         return TradingEnvironment(
             load=cls.make_load_function(),
             asset_db_path=cls.asset_finder.engine,
-            min_date=cls.TRADING_ENV_MIN_DATE,
-            max_date=cls.TRADING_ENV_MAX_DATE,
             trading_schedule=cls.TRADING_ENV_TRADING_SCHEDULE,
         )
 

zipline/utils/calendars/trading_schedule.py

@@ -280,6 +280,14 @@ class TradingSchedule(object):
         """
         raise NotImplementedError()
 
+    @abstractproperty
+    def early_ends(self):
+        """
+        Returns a DatetimeIndex containing the session dates on-which there is
+        an early end to trading.
+        """
+        raise NotImplementedError()
+
 
 class ExchangeTradingSchedule(TradingSchedule):
     """
@@ -355,6 +363,10 @@ class ExchangeTradingSchedule(TradingSchedule):
                                                      count=count,
                                                      step=step)
 
+    @property
+    def early_ends(self):
+        return self._exchange_calendar.early_closes
+
 
 class NYSETradingSchedule(ExchangeTradingSchedule):
     """

zipline/utils/events.py

@@ -348,7 +348,7 @@ class AfterOpen(StatelessRule):
     def calculate_dates(self, dt):
         # given a dt, find that day's open and period end (open + offset)
         self._period_start, self._period_close = \
-            _static_nyse_cal.get_open_and_close(dt)
+            _static_nyse_cal.open_and_close(dt)
         self._period_end = \
             self._period_start + self.offset - self._one_minute
 
@@ -392,7 +392,7 @@ class BeforeClose(StatelessRule):
 
     def calculate_dates(self, dt):
         # given a dt, find that day's close and period start (close - offset)
-        self._period_end = _static_nyse_cal.get_open_and_close(dt)[1]
+        self._period_end = _static_nyse_cal.open_and_close(dt)[1]
         self._period_start = \
             self._period_end - self.offset
         self._period_close = self._period_end