def test_orders_stop(self, name, order_data, event_data, expected):
data = order_data
data['asset'] = self.ASSET133
order = Order(**data)
if expected['transaction']:
expected['transaction']['asset'] = self.ASSET133
event_data['asset'] = self.ASSET133
assets = (
(133, pd.DataFrame(
{
'open': [event_data['open']],
'high': [event_data['high']],
'low': [event_data['low']],
'close': [event_data['close']],
'volume': [event_data['volume']],
},
index=[pd.Timestamp('2006-01-05 14:31', tz='UTC')],
)),
)
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()
try:
dt = pd.Timestamp('2006-01-05 14:31', tz='UTC')
bar_data = BarData(
data_portal,
lambda: dt,
self.sim_params.data_frequency,
self.trading_calendar,
NoRestrictions(),
)
_, txn = next(slippage_model.simulate(
bar_data,
self.ASSET133,
[order],
))
except StopIteration:
txn = None
if expected['transaction'] is None:
self.assertIsNone(txn)
else:
self.assertIsNotNone(txn)
for key, value in expected['transaction'].items():
self.assertEquals(value, txn[key])
def test_orders_stop(self, name, order_data, event_data, expected):
data = order_data
data['asset'] = self.ASSET133
order = Order(**data)
if expected['transaction']:
expected['transaction']['asset'] = self.ASSET133
event_data['asset'] = self.ASSET133
assets = (
(133, pd.DataFrame(
{
'open': [event_data['open']],
'high': [event_data['high']],
'low': [event_data['low']],
'close': [event_data['close']],
'volume': [event_data['volume']],
},
index=[pd.Timestamp('2006-01-05 14:31', tz='UTC')],
)),
)
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()
try:
dt = pd.Timestamp('2006-01-05 14:31', tz='UTC')
bar_data = BarData(
data_portal,
lambda: dt,
self.sim_params.data_frequency,
self.trading_calendar,
NoRestrictions(),
)
_, txn = next(slippage_model.simulate(
bar_data,
self.ASSET133,
[order],
))
except StopIteration:
txn = None
if expected['transaction'] is None:
self.assertIsNone(txn)
else:
self.assertIsNotNone(txn)
for key, value in expected['transaction'].items():
self.assertEquals(value, txn[key])
def test_asset_IPOed_same_day(self):
# gotta get some minute data up in here.
# add sid 4 for a couple of days
minutes = self.trading_calendar.minutes_for_sessions_in_range(
self.sim_params.sessions[0],
self.sim_params.sessions[5]
)
tmp_reader = tmp_bcolz_equity_minute_bar_reader(
self.trading_calendar,
self.trading_calendar.all_sessions,
create_minute_bar_data(minutes, [2]),
)
with tmp_reader as reader:
data_portal = DataPortal(
self.env.asset_finder, self.trading_calendar,
first_trading_day=reader.first_trading_day,
equity_minute_reader=reader,
equity_daily_reader=self.bcolz_equity_daily_bar_reader,
adjustment_reader=self.adjustment_reader,
)
source = BenchmarkSource(
2,
self.env,
self.trading_calendar,
self.sim_params.sessions,
data_portal
)
days_to_use = self.sim_params.sessions
# first value should be 0.0, coming from daily data
self.assertAlmostEquals(0.0, source.get_value(days_to_use[0]))
manually_calculated = data_portal.get_history_window(
[2], days_to_use[-1],
len(days_to_use),
"1d",
"close",
"daily",
)[2].pct_change()
for idx, day in enumerate(days_to_use[1:]):
self.assertEqual(
source.get_value(day),
manually_calculated[idx + 1]
)
def test_asset_IPOed_same_day(self):
# gotta get some minute data up in here.
# add sid 4 for a couple of days
minutes = self.trading_calendar.minutes_for_sessions_in_range(
self.sim_params.sessions[0],
self.sim_params.sessions[5]
)
tmp_reader = tmp_bcolz_equity_minute_bar_reader(
self.trading_calendar,
self.trading_calendar.all_sessions,
create_minute_bar_data(minutes, [2]),
)
with tmp_reader as reader:
data_portal = DataPortal(
self.env.asset_finder, self.trading_calendar,
first_trading_day=reader.first_trading_day,
equity_minute_reader=reader,
equity_daily_reader=self.bcolz_equity_daily_bar_reader,
adjustment_reader=self.adjustment_reader,
)
source = BenchmarkSource(
2,
self.env,
self.trading_calendar,
self.sim_params.sessions,
data_portal
)
days_to_use = self.sim_params.sessions
# first value should be 0.0, coming from daily data
self.assertAlmostEquals(0.0, source.get_value(days_to_use[0]))
manually_calculated = data_portal.get_history_window(
[2], days_to_use[-1],
len(days_to_use),
"1d",
"close",
)[2].pct_change()
for idx, day in enumerate(days_to_use[1:]):
self.assertEqual(
source.get_value(day),
manually_calculated[idx + 1]
)
def test_orders_stop(self, name, order_data, event_data, expected):
data = order_data
data["asset"] = self.ASSET133
order = Order(**data)
if expected["transaction"]:
expected["transaction"]["asset"] = self.ASSET133
event_data["asset"] = self.ASSET133
assets = ((
133,
pd.DataFrame(
{
"open": [event_data["open"]],
"high": [event_data["high"]],
"low": [event_data["low"]],
"close": [event_data["close"]],
"volume": [event_data["volume"]],
},
index=[pd.Timestamp("2006-01-05 14:31", tz="UTC")],
),
), )
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.asset_finder,
self.trading_calendar,
first_trading_day=reader.first_trading_day,
equity_minute_reader=reader,
)
slippage_model = VolumeShareSlippage()
try:
dt = pd.Timestamp("2006-01-05 14:31", tz="UTC")
bar_data = BarData(
data_portal,
lambda: dt,
self.sim_params.data_frequency,
self.trading_calendar,
NoRestrictions(),
)
_, txn = next(
slippage_model.simulate(
bar_data,
self.ASSET133,
[order],
))
except StopIteration:
txn = None
if expected["transaction"] is None:
assert txn is None
else:
assert txn is not None
for key, value in expected["transaction"].items():
assert value == txn[key]
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_schedule, days, assets) \
as reader:
data_portal = DataPortal(
self.env.asset_finder, self.trading_schedule,
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')
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')
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
