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,
asset=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),
'asset': self.ASSET133,
'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,
asset=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)
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_volume_share_slippage_with_future(self):
slippage_model = VolumeShareSlippage(volume_limit=1, price_impact=0.3)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=10,
filled=0,
asset=self.ASSET1000,
),
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(
slippage_model.simulate(bar_data, self.ASSET1000, open_orders)
)
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
# We expect to fill the order for all 10 contracts. The volume for the
# futures contract in this bar is 100, so our volume share is:
# 10.0 / 100 = 0.1
# The current price is 5.0 and the price impact is 0.3, so the expected
# impacted price is:
# 5.0 + (5.0 * (0.1 ** 2) * 0.3) = 5.015
expected_txn = {
'price': 5.015,
'dt': datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': 10,
'asset': self.ASSET1000,
'commission': None,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id,
}
self.assertIsNotNone(txn)
self.assertEquals(expected_txn, txn.__dict__)
def test_volume_share_slippage_with_future(self):
slippage_model = VolumeShareSlippage(volume_limit=1, price_impact=0.3)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=10,
filled=0,
asset=self.ASSET1000,
),
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(bar_data, self.ASSET1000, open_orders))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
# We expect to fill the order for all 10 contracts. The volume for the
# futures contract in this bar is 100, so our volume share is:
# 10.0 / 100 = 0.1
# The current price is 5.0 and the price impact is 0.3, so the expected
# impacted price is:
# 5.0 + (5.0 * (0.1 ** 2) * 0.3) = 5.015
expected_txn = {
'price': 5.015,
'dt': datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': 10,
'asset': self.ASSET1000,
'commission': None,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id,
}
self.assertIsNotNone(txn)
self.assertEquals(expected_txn, txn.__dict__)
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,
asset=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),
'asset': self.ASSET133,
'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,
asset=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)
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,
'asset': 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,
'asset': 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,
'asset': 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),
'asset': self.ASSET133
}
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,
'asset': 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,
'asset': 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,
'asset': 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),
'asset': self.ASSET133,
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
def test_orders_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,
'asset': self.ASSET133,
'limit': 3.5})
]
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,
'asset': self.ASSET133,
'limit': 3.5})
]
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,
'asset': self.ASSET133,
'limit': 3.6})
]
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][1]
expected_txn = {
'price': float(3.50021875),
'dt': datetime.datetime(
2006, 1, 5, 14, 34, tzinfo=pytz.utc),
# we ordered 100 shares, but default volume slippage only allows
# for 2.5% of the volume. 2.5% * 2000 = 50 shares
'amount': int(50),
'asset': self.ASSET133,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
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,
'asset': self.ASSET133,
'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)
# 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,
'asset': self.ASSET133,
'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)
# short, does trade
open_orders = [
Order(**{
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.4})
]
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),
'asset': self.ASSET133,
}
self.assertIsNotNone(txn)
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,
'asset': 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,
'asset': 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,
'asset': 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),
'asset': self.ASSET133
}
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,
'asset': 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,
'asset': 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,
'asset': 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),
'asset': self.ASSET133,
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
def test_orders_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,
'asset': self.ASSET133,
'limit': 3.5
})
]
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,
'asset': self.ASSET133,
'limit': 3.5
})
]
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,
'asset': self.ASSET133,
'limit': 3.6
})
]
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][1]
expected_txn = {
'price': float(3.50021875),
'dt': datetime.datetime(2006, 1, 5, 14, 34, tzinfo=pytz.utc),
# we ordered 100 shares, but default volume slippage only allows
# for 2.5% of the volume. 2.5% * 2000 = 50 shares
'amount': int(50),
'asset': self.ASSET133,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
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,
'asset': self.ASSET133,
'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)
# 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,
'asset': self.ASSET133,
'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)
# short, does trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.4
})
]
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),
'asset': self.ASSET133,
}
self.assertIsNotNone(txn)
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])