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TEST: Modify tests for extra BarData parameter

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Introducing a WithCreateBarData fixture which allows for the
creation of a BarData using only the `simulation_dt_func` and
`restrictions` params. Assumes that each suite uses the same
`data_portal`, `data_frequency` and `trading_calendar`
This commit is contained in:
Andrew Liang
2016-09-14 11:29:52 -04:00
parent e465f64f91
commit 5e276d0e72
7 changed files with 502 additions and 432 deletions
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tests/finance/test_slippage.py
+409 -374
View File
@@ -27,20 +27,25 @@ from pandas.tslib import normalize_date
from zipline.finance.slippage import VolumeShareSlippage
from zipline.protocol import DATASOURCE_TYPE
from zipline.protocol import DATASOURCE_TYPE, BarData
from zipline.finance.blotter import Order
from zipline.finance.restrictions import NoopRestrictions
from zipline.data.data_portal import DataPortal
from zipline.protocol import BarData
from zipline.testing import tmp_bcolz_equity_minute_bar_reader
from zipline.testing.fixtures import (
WithCreateBarData,
WithDataPortal,
WithSimParams,
WithTradingEnvironment,
ZiplineTestCase,
)
from zipline.utils.classproperty import classproperty
class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
class SlippageTestCase(WithCreateBarData,
WithSimParams,
WithDataPortal,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-05 14:31', tz='utc')
END_DATE = pd.Timestamp('2006-01-05 14:36', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 1.0e5
@@ -56,6 +61,10 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
freq='1min'
)
@classproperty
def CREATE_BARDATA_DATA_FREQUENCY(cls):
return cls.sim_params.data_frequency
@classmethod
def make_equity_minute_bar_data(cls):
yield 133, pd.DataFrame(
@@ -74,97 +83,6 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
super(SlippageTestCase, cls).init_class_fixtures()
cls.ASSET133 = cls.env.asset_finder.retrieve_asset(133)
def test_volume_share_slippage(self):
assets = (
(133, pd.DataFrame(
{
'open': [3.00],
'high': [3.15],
'low': [2.85],
'close': [3.00],
'volume': [200],
},
index=[self.minutes[0]],
)),
)
days = pd.date_range(
start=normalize_date(self.minutes[0]),
end=normalize_date(self.minutes[-1])
)
with tmp_bcolz_equity_minute_bar_reader(self.trading_calendar, days, assets) \
as reader:
data_portal = DataPortal(
self.env.asset_finder, self.trading_calendar,
first_trading_day=reader.first_trading_day,
equity_minute_reader=reader,
)
slippage_model = VolumeShareSlippage()
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=100,
filled=0,
sid=self.ASSET133
)
]
bar_data = BarData(data_portal,
lambda: self.minutes[0],
'minute',
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.0001875),
'dt': datetime.datetime(
2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': int(5),
'sid': int(133),
'commission': None,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
# TODO: Make expected_txn an Transaction object and ensure there
# is a __eq__ for that class.
self.assertEquals(expected_txn, txn.__dict__)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=100,
filled=0,
sid=self.ASSET133
)
]
# Set bar_data to be a minute ahead of last trade.
# Volume share slippage should not execute when there is no trade.
bar_data = BarData(data_portal,
lambda: self.minutes[1],
'minute',
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
def test_orders_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
@@ -179,10 +97,9 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
'limit': 3.5})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[3],
self.sim_params.data_frequency,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3],
)
orders_txns = list(slippage_model.simulate(
bar_data,
@@ -202,10 +119,9 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
'limit': 3.5})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[3],
self.sim_params.data_frequency,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3],
)
orders_txns = list(slippage_model.simulate(
bar_data,
@@ -225,10 +141,9 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
'limit': 3.6})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[3],
self.sim_params.data_frequency,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3],
)
orders_txns = list(slippage_model.simulate(
bar_data,
@@ -265,10 +180,9 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
'limit': 3.5})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[0],
self.sim_params.data_frequency,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(slippage_model.simulate(
bar_data,
@@ -288,10 +202,9 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
'limit': 3.5})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[0],
self.sim_params.data_frequency,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(slippage_model.simulate(
bar_data,
@@ -311,10 +224,9 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
'limit': 3.4})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[1],
self.sim_params.data_frequency,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1],
)
orders_txns = list(slippage_model.simulate(
bar_data,
@@ -338,6 +250,376 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
def test_orders_stop_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
# long, does not trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'sid': self.ASSET133,
'stop': 4.0,
'limit': 3.0})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does not trade - impacted price worse than limit price
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'sid': self.ASSET133,
'stop': 4.0,
'limit': 3.5})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'sid': self.ASSET133,
'stop': 4.0,
'limit': 3.6})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.50021875),
'dt': datetime.datetime(
2006, 1, 5, 14, 34, tzinfo=pytz.utc),
'amount': int(50),
'sid': int(133)
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
# short, does not trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'sid': self.ASSET133,
'stop': 3.0,
'limit': 4.0})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does not trade - impacted price worse than limit price
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'sid': self.ASSET133,
'stop': 3.0,
'limit': 3.5})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'sid': self.ASSET133,
'stop': 3.0,
'limit': 3.4})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.49978125),
'dt': datetime.datetime(
2006, 1, 5, 14, 32, tzinfo=pytz.utc),
'amount': int(-50),
'sid': int(133)
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
class VolumeShareSlippageTestCase(WithCreateBarData,
WithSimParams,
WithDataPortal,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-05 14:31', tz='utc')
END_DATE = pd.Timestamp('2006-01-05 14:36', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 1.0e5
SIM_PARAMS_DATA_FREQUENCY = 'minute'
SIM_PARAMS_EMISSION_RATE = 'daily'
ASSET_FINDER_EQUITY_SIDS = (133,)
ASSET_FINDER_EQUITY_START_DATE = pd.Timestamp('2006-01-05', tz='utc')
ASSET_FINDER_EQUITY_END_DATE = pd.Timestamp('2006-01-07', tz='utc')
minutes = pd.DatetimeIndex(
start=START_DATE,
end=END_DATE - pd.Timedelta('1 minute'),
freq='1min'
)
@classproperty
def CREATE_BARDATA_DATA_FREQUENCY(cls):
return cls.sim_params.data_frequency
@classmethod
def make_equity_minute_bar_data(cls):
yield 133, pd.DataFrame(
{
'open': [3.00],
'high': [3.15],
'low': [2.85],
'close': [3.00],
'volume': [200],
},
index=[cls.minutes[0]],
)
@classmethod
def init_class_fixtures(cls):
super(VolumeShareSlippageTestCase, cls).init_class_fixtures()
cls.ASSET133 = cls.env.asset_finder.retrieve_asset(133)
def test_volume_share_slippage(self):
slippage_model = VolumeShareSlippage()
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=100,
filled=0,
sid=self.ASSET133
)
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.0001875),
'dt': datetime.datetime(
2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': int(5),
'sid': int(133),
'commission': None,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
# TODO: Make expected_txn an Transaction object and ensure there
# is a __eq__ for that class.
self.assertEquals(expected_txn, txn.__dict__)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=100,
filled=0,
sid=self.ASSET133
)
]
# Set bar_data to be a minute ahead of last trade.
# Volume share slippage should not execute when there is no trade.
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1],
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
class OrdersStopTestCase(WithSimParams,
WithTradingEnvironment,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-05 14:31', tz='utc')
END_DATE = pd.Timestamp('2006-01-05 14:36', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 1.0e5
SIM_PARAMS_DATA_FREQUENCY = 'minute'
SIM_PARAMS_EMISSION_RATE = 'daily'
ASSET_FINDER_EQUITY_SIDS = (133,)
minutes = pd.DatetimeIndex(
start=START_DATE,
end=END_DATE - pd.Timedelta('1 minute'),
freq='1min'
)
@classmethod
def init_class_fixtures(cls):
super(OrdersStopTestCase, cls).init_class_fixtures()
cls.ASSET133 = cls.env.asset_finder.retrieve_asset(133)
STOP_ORDER_CASES = {
# Stop orders can be long/short and have their price greater or
# less than the stop.
@@ -501,10 +783,14 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
try:
dt = pd.Timestamp('2006-01-05 14:31', tz='UTC')
bar_data = BarData(data_portal,
lambda: dt,
'minute',
self.trading_calendar)
bar_data = BarData(
data_portal,
lambda: dt,
self.sim_params.data_frequency,
self.trading_calendar,
NoopRestrictions(),
)
_, txn = next(slippage_model.simulate(
bar_data,
self.ASSET133,
@@ -520,254 +806,3 @@ class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
for key, value in expected['transaction'].items():
self.assertEquals(value, txn[key])
def test_orders_stop_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
# long, does not trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'sid': self.ASSET133,
'stop': 4.0,
'limit': 3.0})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[2],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = BarData(self.data_portal,
lambda: self.minutes[3],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does not trade - impacted price worse than limit price
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'sid': self.ASSET133,
'stop': 4.0,
'limit': 3.5})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[2],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = BarData(self.data_portal,
lambda: self.minutes[3],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'sid': self.ASSET133,
'stop': 4.0,
'limit': 3.6})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[2],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = BarData(self.data_portal,
lambda: self.minutes[3],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.50021875),
'dt': datetime.datetime(
2006, 1, 5, 14, 34, tzinfo=pytz.utc),
'amount': int(50),
'sid': int(133)
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
# short, does not trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'sid': self.ASSET133,
'stop': 3.0,
'limit': 4.0})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[0],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = BarData(self.data_portal,
lambda: self.minutes[1],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does not trade - impacted price worse than limit price
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'sid': self.ASSET133,
'stop': 3.0,
'limit': 3.5})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[0],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = BarData(self.data_portal,
lambda: self.minutes[1],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'sid': self.ASSET133,
'stop': 3.0,
'limit': 3.4})
]
bar_data = BarData(self.data_portal,
lambda: self.minutes[0],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = BarData(self.data_portal,
lambda: self.minutes[1],
self.sim_params.data_frequency,
self.trading_calendar)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.49978125),
'dt': datetime.datetime(
2006, 1, 5, 14, 32, tzinfo=pytz.utc),
'amount': int(-50),
'sid': int(133)
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
tests/test_api_shim.py
+7 -6
View File
@@ -7,7 +7,6 @@ from pandas.core.common import PerformanceWarning
from zipline import TradingAlgorithm
from zipline.finance.trading import SimulationParameters
from zipline.protocol import BarData
from zipline.testing import (
MockDailyBarReader,
create_daily_df_for_asset,
@@ -15,6 +14,7 @@ from zipline.testing import (
str_to_seconds,
)
from zipline.testing.fixtures import (
WithCreateBarData,
WithDataPortal,
WithSimParams,
ZiplineTestCase,
@@ -114,7 +114,11 @@ def handle_data(context, data):
"""
class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
class TestAPIShim(WithCreateBarData,
WithDataPortal,
WithSimParams,
ZiplineTestCase,
):
START_DATE = pd.Timestamp("2016-01-05", tz='UTC')
END_DATE = pd.Timestamp("2016-01-28", tz='UTC')
SIM_PARAMS_DATA_FREQUENCY = 'minute'
@@ -186,11 +190,8 @@ class TestAPIShim(WithDataPortal, WithSimParams, ZiplineTestCase):
test_end_minute = self.trading_calendar.minutes_for_session(
self.sim_params.sessions[0]
)[-1]
bar_data = BarData(
self.data_portal,
bar_data = self.create_bardata(
lambda: test_end_minute,
"minute",
self.trading_calendar
)
ohlcvp_fields = [
"open",
tests/test_blotter.py
+12 -12
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@@ -31,8 +31,9 @@ from zipline.finance.slippage import (
DEFAULT_VOLUME_SLIPPAGE_BAR_LIMIT,
FixedSlippage,
)
from zipline.protocol import BarData
from zipline.utils.classproperty import classproperty
from zipline.testing.fixtures import (
WithCreateBarData,
WithDataPortal,
WithLogger,
WithSimParams,
@@ -40,7 +41,8 @@ from zipline.testing.fixtures import (
)
class BlotterTestCase(WithLogger,
class BlotterTestCase(WithCreateBarData,
WithLogger,
WithDataPortal,
WithSimParams,
ZiplineTestCase):
@@ -71,6 +73,10 @@ class BlotterTestCase(WithLogger,
index=cls.sim_params.sessions,
)
@classproperty
def CREATE_BARDATA_DATA_FREQUENCY(cls):
return cls.sim_params.data_frequency
@parameterized.expand([(MarketOrder(), None, None),
(LimitOrder(10), 10, None),
(StopOrder(10), None, 10),
@@ -219,11 +225,8 @@ class BlotterTestCase(WithLogger,
filled_id = blotter.order(asset_24, 100, MarketOrder())
filled_order = None
blotter.current_dt = self.sim_params.sessions[-1]
bar_data = BarData(
self.data_portal,
lambda: self.sim_params.sessions[-1],
self.sim_params.data_frequency,
self.trading_calendar
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.sim_params.sessions[-1],
)
txns, _, closed_orders = blotter.get_transactions(bar_data)
for txn in txns:
@@ -295,11 +298,8 @@ class BlotterTestCase(WithLogger,
filled_order = None
blotter.current_dt = dt
bar_data = BarData(
self.data_portal,
lambda: dt,
self.sim_params.data_frequency,
self.trading_calendar
bar_data = self.create_bardata(
simulation_dt_func=lambda: dt,
)
txns, _, _ = blotter.get_transactions(bar_data)
for txn in txns:
tests/test_finance.py
+6 -4
View File
@@ -37,6 +37,7 @@ from zipline.data.minute_bars import BcolzMinuteBarReader
from zipline.data.data_portal import DataPortal
from zipline.data.us_equity_pricing import BcolzDailyBarWriter
from zipline.finance.slippage import FixedSlippage
from zipline.finance.restrictions import NoopRestrictions
from zipline.protocol import BarData
from zipline.testing import (
tmp_trading_env,
@@ -317,10 +318,11 @@ class FinanceTestCase(WithLogger,
order_date = order_date.replace(hour=14, minute=30)
else:
bar_data = BarData(
data_portal,
lambda: tick,
sim_params.data_frequency,
self.trading_calendar
data_portal=data_portal,
simulation_dt_func=lambda: tick,
data_frequency=sim_params.data_frequency,
trading_calendar=self.trading_calendar,
restrictions=NoopRestrictions(),
)
txns, _, closed_orders = blotter.get_transactions(bar_data)
for txn in txns:
tests/test_history.py
+50 -36
View File
@@ -21,20 +21,21 @@ import pandas as pd
from six import iteritems
from zipline import TradingAlgorithm
from zipline._protocol import handle_non_market_minutes
from zipline._protocol import handle_non_market_minutes, BarData
from zipline.assets import Asset
from zipline.errors import (
HistoryInInitialize,
HistoryWindowStartsBeforeData,
)
from zipline.finance.trading import SimulationParameters
from zipline.protocol import BarData
from zipline.finance.restrictions import NoopRestrictions
from zipline.testing import (
create_minute_df_for_asset,
str_to_seconds,
MockDailyBarReader,
)
from zipline.testing.fixtures import (
WithCreateBarData,
WithDataPortal,
ZiplineTestCase,
alias,
@@ -46,7 +47,7 @@ OHLCP = OHLC + ['price']
ALL_FIELDS = OHLCP + ['volume']
class WithHistory(WithDataPortal):
class WithHistory(WithCreateBarData, WithDataPortal):
TRADING_START_DT = TRADING_ENV_MIN_DATE = START_DATE = pd.Timestamp(
'2014-01-03',
tz='UTC',
@@ -251,8 +252,9 @@ class WithHistory(WithDataPortal):
fields = fields if fields is not None else ALL_FIELDS
assets = assets if assets is not None else [self.ASSET2, self.ASSET3]
bar_data = BarData(self.data_portal, lambda: dt, mode,
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: dt,
)
check_internal_consistency(
bar_data, assets, fields, 10, freq
)
@@ -704,8 +706,9 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
)[0:60]
for idx, minute in enumerate(minutes):
bar_data = BarData(self.data_portal, lambda: minute, 'minute',
self.trading_calendar)
bar_data = self.create_bardata(
lambda: minute,
)
check_internal_consistency(
bar_data, [self.ASSET2, self.ASSET3], ALL_FIELDS, 10, '1m'
)
@@ -766,13 +769,12 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
)
)[1]
midnight_bar_data = \
BarData(self.data_portal, lambda: midnight, 'minute',
self.trading_calendar)
yesterday_bar_data = \
BarData(self.data_portal, lambda: last_minute, 'minute',
self.trading_calendar)
midnight_bar_data = self.create_bardata(
lambda: midnight,
)
yesterday_bar_data = self.create_bardata(
lambda: last_minute
)
with handle_non_market_minutes(midnight_bar_data):
for field in ALL_FIELDS:
@@ -789,8 +791,9 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
)[0:60]
for idx, minute in enumerate(minutes):
bar_data = BarData(self.data_portal, lambda: minute, 'minute',
self.trading_calendar)
bar_data = self.create_bardata(
lambda: minute
)
check_internal_consistency(
bar_data, self.SHORT_ASSET, ALL_FIELDS, 30, '1m'
)
@@ -799,8 +802,13 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
data_portal = self.make_data_portal()
# choose a window that contains the last minute of the asset
bar_data = BarData(data_portal, lambda: minutes[15], 'minute',
self.trading_calendar)
bar_data = BarData(
data_portal=data_portal,
simulation_dt_func=lambda: minutes[15],
data_frequency='minute',
restrictions=NoopRestrictions(),
trading_calendar=self.trading_calendar,
)
# close high low open price volume
# 2015-01-06 20:47:00+00:00 768 770 767 769 768 76800
@@ -1012,8 +1020,9 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
def test_passing_iterable_to_history_regular_hours(self):
# regular hours
current_dt = pd.Timestamp("2015-01-06 9:45", tz='US/Eastern')
bar_data = BarData(self.data_portal, lambda: current_dt, "minute",
self.trading_calendar)
bar_data = self.create_bardata(
lambda: current_dt,
)
bar_data.history(pd.Index([self.ASSET1, self.ASSET2]),
"high", 5, "1m")
@@ -1021,8 +1030,9 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
def test_passing_iterable_to_history_bts(self):
# before market hours
current_dt = pd.Timestamp("2015-01-07 8:45", tz='US/Eastern')
bar_data = BarData(self.data_portal, lambda: current_dt, "minute",
self.trading_calendar)
bar_data = self.create_bardata(
lambda: current_dt,
)
with handle_non_market_minutes(bar_data):
bar_data.history(pd.Index([self.ASSET1, self.ASSET2]),
@@ -1031,8 +1041,9 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
def test_overnight_adjustments(self):
# Should incorporate adjustments on midnight 01/06
current_dt = pd.Timestamp('2015-01-06 8:45', tz='US/Eastern')
bar_data = BarData(self.data_portal, lambda: current_dt, 'minute',
self.trading_calendar)
bar_data = self.create_bardata(
lambda: current_dt,
)
adj_expected = {
'open': np.arange(8381, 8391) / 4.0,
@@ -1341,6 +1352,8 @@ class MinuteEquityHistoryTestCase(WithHistory, ZiplineTestCase):
class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
CREATE_BARDATA_DATA_FREQUENCY = 'daily'
@classmethod
def make_equity_daily_bar_data(cls):
yield 1, cls.create_df_for_asset(
@@ -1403,8 +1416,9 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
)
for idx, day in enumerate(days):
bar_data = BarData(self.data_portal, lambda: day, 'daily',
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: day,
)
check_internal_consistency(
bar_data, [self.ASSET2, self.ASSET3], ALL_FIELDS, 10, '1d'
)
@@ -1445,10 +1459,9 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
# asset1 ends on 2016-01-30
# asset2 ends on 2015-12-13
bar_data = BarData(self.data_portal,
lambda: pd.Timestamp('2016-01-06', tz='UTC'),
'daily',
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: pd.Timestamp('2016-01-06', tz='UTC'),
)
for field in OHLCP:
window = bar_data.history(
@@ -1486,8 +1499,9 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
# days has 1/7, 1/8
for idx, day in enumerate(days):
bar_data = BarData(self.data_portal, lambda: day, 'daily',
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda: day,
)
check_internal_consistency(
bar_data, self.SHORT_ASSET, ALL_FIELDS, 2, '1d'
)
@@ -1639,10 +1653,10 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
# asset1 ends on 2016-01-30
# asset2 ends on 2016-01-04
bar_data = BarData(self.data_portal,
lambda: pd.Timestamp('2016-01-06 16:00', tz='UTC'),
'daily',
self.trading_calendar)
bar_data = self.create_bardata(
simulation_dt_func=lambda:
pd.Timestamp('2016-01-06 16:00', tz='UTC'),
)
for field in OHLCP:
window = bar_data.history(
tests/test_tradesimulation.py
+2
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@@ -24,6 +24,7 @@ from zipline import TradingAlgorithm
from zipline.gens.sim_engine import BEFORE_TRADING_START_BAR
from zipline.finance.performance import PerformanceTracker
from zipline.finance.restrictions import NoopRestrictions
from zipline.gens.tradesimulation import AlgorithmSimulator
from zipline.sources.benchmark_source import BenchmarkSource
from zipline.test_algorithms import NoopAlgorithm
@@ -135,6 +136,7 @@ def initialize(context):
self.data_portal,
BeforeTradingStartsOnlyClock(dt),
algo._create_benchmark_source(),
NoopRestrictions(),
None
)
zipline/testing/fixtures.py
+16
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@@ -36,8 +36,10 @@ from ..utils.classproperty import classproperty
from ..utils.final import FinalMeta, final
from .core import tmp_asset_finder, make_simple_equity_info
from zipline.assets import Equity, Future
from zipline.finance.restrictions import NoopRestrictions
from zipline.pipeline import SimplePipelineEngine
from zipline.pipeline.loaders.testing import make_seeded_random_loader
from zipline.protocol import BarData
from zipline.utils.calendars import (
get_calendar,
register_calendar)
@@ -1319,3 +1321,17 @@ class WithResponses(object):
self.responses = self.enter_instance_context(
responses.RequestsMock(),
)
class WithCreateBarData(WithDataPortal):
CREATE_BARDATA_DATA_FREQUENCY = 'minute'
def create_bardata(self, simulation_dt_func, restrictions=None):
return BarData(
self.data_portal,
simulation_dt_func,
self.CREATE_BARDATA_DATA_FREQUENCY,
self.trading_calendar,
restrictions or NoopRestrictions()
)