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zipline/tests/finance/test_slippage.py
Richard Frank abd4c61c64 TST: Generate test names and order deterministically 
so they can be parallelized across boxes reliably
2019-09-13 10:26:23 -04:00

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#
# Copyright 2017 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# 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.
'''
Unit tests for finance.slippage
'''
from collections import namedtuple
import datetime
from math import sqrt
from nose_parameterized import parameterized
import numpy as np
import pandas as pd
import pytz
from zipline.assets import Equity, Future
from zipline.data.data_portal import DataPortal
from zipline.finance.asset_restrictions import NoRestrictions
from zipline.finance.order import Order
from zipline.finance.slippage import (
EquitySlippageModel,
fill_price_worse_than_limit_price,
FutureSlippageModel,
SlippageModel,
VolatilityVolumeShare,
VolumeShareSlippage,
FixedBasisPointsSlippage,
)
from zipline.protocol import DATASOURCE_TYPE, BarData
from zipline.testing import (
create_minute_bar_data,
tmp_bcolz_equity_minute_bar_reader,
)
from zipline.testing.fixtures import (
WithAssetFinder,
WithCreateBarData,
WithDataPortal,
WithSimParams,
WithTradingCalendars,
ZiplineTestCase,
)
from zipline.utils.classproperty import classproperty
from zipline.utils.pandas_utils import normalize_date
TestOrder = namedtuple('TestOrder', 'limit direction')
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
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': [3.0, 3.0, 3.5, 4.0, 3.5],
'high': [3.15, 3.15, 3.15, 3.15, 3.15],
'low': [2.85, 2.85, 2.85, 2.85, 2.85],
'close': [3.0, 3.5, 4.0, 3.5, 3.0],
'volume': [2000, 2000, 2000, 2000, 2000],
},
index=cls.minutes,
)
@classmethod
def init_class_fixtures(cls):
super(SlippageTestCase, cls).init_class_fixtures()
cls.ASSET133 = cls.asset_finder.retrieve_asset(133)
def test_allowed_asset_types(self):
# Custom equities model.
class MyEquitiesModel(EquitySlippageModel):
def process_order(self, data, order):
return 0, 0
self.assertEqual(MyEquitiesModel.allowed_asset_types, (Equity,))
# Custom futures model.
class MyFuturesModel(FutureSlippageModel):
def process_order(self, data, order):
return 0, 0
self.assertEqual(MyFuturesModel.allowed_asset_types, (Future,))
# Custom model for both equities and futures.
class MyMixedModel(EquitySlippageModel, FutureSlippageModel):
def process_order(self, data, order):
return 0, 0
self.assertEqual(MyMixedModel.allowed_asset_types, (Equity, Future))
# Equivalent custom model for both equities and futures.
class MyMixedModel(SlippageModel):
def process_order(self, data, order):
return 0, 0
self.assertEqual(MyMixedModel.allowed_asset_types, (Equity, Future))
SomeType = type('SomeType', (object,), {})
# A custom model that defines its own allowed types should take
# precedence over the parent class definitions.
class MyCustomModel(EquitySlippageModel, FutureSlippageModel):
allowed_asset_types = (SomeType,)
def process_order(self, data, order):
return 0, 0
self.assertEqual(MyCustomModel.allowed_asset_types, (SomeType,))
def test_fill_price_worse_than_limit_price(self):
non_limit_order = TestOrder(limit=None, direction=1)
limit_buy = TestOrder(limit=1.5, direction=1)
limit_sell = TestOrder(limit=1.5, direction=-1)
for price in [1, 1.5, 2]:
self.assertFalse(
fill_price_worse_than_limit_price(price, non_limit_order)
)
self.assertFalse(fill_price_worse_than_limit_price(1, limit_buy))
self.assertFalse(fill_price_worse_than_limit_price(1.5, limit_buy))
self.assertTrue(fill_price_worse_than_limit_price(2, limit_buy))
self.assertTrue(fill_price_worse_than_limit_price(1, limit_sell))
self.assertFalse(fill_price_worse_than_limit_price(1.5, limit_sell))
self.assertFalse(fill_price_worse_than_limit_price(2, limit_sell))
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])
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 make_futures_info(cls):
return pd.DataFrame({
'sid': [1000],
'root_symbol': ['CL'],
'symbol': ['CLF06'],
'start_date': [cls.ASSET_FINDER_EQUITY_START_DATE],
'end_date': [cls.ASSET_FINDER_EQUITY_END_DATE],
'multiplier': [500],
'exchange': ['CMES'],
})
@classmethod
def make_future_minute_bar_data(cls):
yield 1000, pd.DataFrame(
{
'open': [5.00],
'high': [5.15],
'low': [4.85],
'close': [5.00],
'volume': [100],
},
index=[cls.minutes[0]],
)
@classmethod
def init_class_fixtures(cls):
super(VolumeShareSlippageTestCase, cls).init_class_fixtures()
cls.ASSET133 = cls.asset_finder.retrieve_asset(133)
cls.ASSET1000 = cls.asset_finder.retrieve_asset(1000)
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,
'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_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,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id,
}
self.assertIsNotNone(txn)
self.assertEquals(expected_txn, txn.__dict__)
class VolatilityVolumeShareTestCase(WithCreateBarData,
WithSimParams,
WithDataPortal,
ZiplineTestCase):
ASSET_START_DATE = pd.Timestamp('2006-02-10')
TRADING_CALENDAR_STRS = ('NYSE', 'us_futures')
TRADING_CALENDAR_PRIMARY_CAL = 'us_futures'
@classmethod
def init_class_fixtures(cls):
super(VolatilityVolumeShareTestCase, cls).init_class_fixtures()
cls.ASSET = cls.asset_finder.retrieve_asset(1000)
@classmethod
def make_futures_info(cls):
return pd.DataFrame({
'sid': [1000, 1001],
'root_symbol': ['CL', 'FV'],
'symbol': ['CLF07', 'FVF07'],
'start_date': [cls.ASSET_START_DATE, cls.START_DATE],
'end_date': [cls.END_DATE, cls.END_DATE],
'multiplier': [500, 500],
'exchange': ['CMES', 'CMES'],
})
@classmethod
def make_future_minute_bar_data(cls):
data = list(
super(
VolatilityVolumeShareTestCase, cls,
).make_future_minute_bar_data()
)
# Make the first month's worth of data NaN to simulate cases where a
# futures contract does not exist yet.
data[0][1].loc[:cls.ASSET_START_DATE] = np.NaN
return data
def test_calculate_impact_buy(self):
answer_key = [
# We ordered 10 contracts, but are capped at 100 * 0.05 = 5
(91485.500085168125, 5),
(91486.500085169057, 5),
(None, None),
]
order = Order(
dt=pd.Timestamp.now(tz='utc').round('min'),
asset=self.ASSET,
amount=10,
)
self._calculate_impact(order, answer_key)
def test_calculate_impact_sell(self):
answer_key = [
# We ordered -10 contracts, but are capped at -(100 * 0.05) = -5
(91485.499914831875, -5),
(91486.499914830943, -5),
(None, None),
]
order = Order(
dt=pd.Timestamp.now(tz='utc').round('min'),
asset=self.ASSET,
amount=-10,
)
self._calculate_impact(order, answer_key)
def _calculate_impact(self, test_order, answer_key):
model = VolatilityVolumeShare(volume_limit=0.05)
first_minute = pd.Timestamp('2006-03-31 11:35AM', tz='UTC')
next_3_minutes = self.trading_calendar.minutes_window(first_minute, 3)
remaining_shares = test_order.open_amount
for i, minute in enumerate(next_3_minutes):
data = self.create_bardata(simulation_dt_func=lambda: minute)
new_order = Order(
dt=data.current_dt, asset=self.ASSET, amount=remaining_shares,
)
price, amount = model.process_order(data, new_order)
self.assertEqual(price, answer_key[i][0])
self.assertEqual(amount, answer_key[i][1])
amount = amount or 0
if remaining_shares < 0:
remaining_shares = min(0, remaining_shares - amount)
else:
remaining_shares = max(0, remaining_shares - amount)
def test_calculate_impact_without_history(self):
model = VolatilityVolumeShare(volume_limit=1)
late_start_asset = self.asset_finder.retrieve_asset(1000)
early_start_asset = self.asset_finder.retrieve_asset(1001)
cases = [
# History will look for data before the start date.
(pd.Timestamp('2006-01-05 11:35AM', tz='UTC'), early_start_asset),
# Start day of the futures contract; no history yet.
(pd.Timestamp('2006-02-10 11:35AM', tz='UTC'), late_start_asset),
# Only a week's worth of history data.
(pd.Timestamp('2006-02-17 11:35AM', tz='UTC'), late_start_asset),
]
for minute, asset in cases:
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=asset, amount=10)
price, amount = model.process_order(data, order)
avg_price = (
data.current(asset, 'high') + data.current(asset, 'low')
) / 2
expected_price = \
avg_price * (1 + model.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT)
self.assertAlmostEqual(price, expected_price, delta=0.001)
self.assertEqual(amount, 10)
def test_impacted_price_worse_than_limit(self):
model = VolatilityVolumeShare(volume_limit=0.05)
# Use all the same numbers from the 'calculate_impact' tests. Since the
# impacted price is 59805.5, which is worse than the limit price of
# 59800, the model should return None.
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(
dt=data.current_dt, asset=self.ASSET, amount=10, limit=59800,
)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def test_low_transaction_volume(self):
# With a volume limit of 0.001, and a bar volume of 100, we should
# compute a transaction volume of 100 * 0.001 = 0.1, which gets rounded
# down to zero. In this case we expect no amount to be transacted.
model = VolatilityVolumeShare(volume_limit=0.001)
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=self.ASSET, amount=10)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
class MarketImpactTestCase(WithCreateBarData, ZiplineTestCase):
ASSET_FINDER_EQUITY_SIDS = (1,)
@classmethod
def make_equity_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Equity]
return create_minute_bar_data(
trading_calendar.minutes_for_sessions_in_range(
cls.equity_minute_bar_days[0],
cls.equity_minute_bar_days[-1],
),
cls.asset_finder.equities_sids,
)
def test_window_data(self):
session = pd.Timestamp('2006-03-01')
minute = self.trading_calendar.minutes_for_session(session)[1]
data = self.create_bardata(simulation_dt_func=lambda: minute)
asset = self.asset_finder.retrieve_asset(1)
mean_volume, volatility = VolatilityVolumeShare(0.0)._get_window_data(
data, asset, window_length=20,
)
# close volume
# 2006-01-31 00:00:00+00:00 29.0 119.0
# 2006-02-01 00:00:00+00:00 30.0 120.0
# 2006-02-02 00:00:00+00:00 31.0 121.0
# 2006-02-03 00:00:00+00:00 32.0 122.0
# 2006-02-06 00:00:00+00:00 33.0 123.0
# 2006-02-07 00:00:00+00:00 34.0 124.0
# 2006-02-08 00:00:00+00:00 35.0 125.0
# 2006-02-09 00:00:00+00:00 36.0 126.0
# 2006-02-10 00:00:00+00:00 37.0 127.0
# 2006-02-13 00:00:00+00:00 38.0 128.0
# 2006-02-14 00:00:00+00:00 39.0 129.0
# 2006-02-15 00:00:00+00:00 40.0 130.0
# 2006-02-16 00:00:00+00:00 41.0 131.0
# 2006-02-17 00:00:00+00:00 42.0 132.0
# 2006-02-21 00:00:00+00:00 43.0 133.0
# 2006-02-22 00:00:00+00:00 44.0 134.0
# 2006-02-23 00:00:00+00:00 45.0 135.0
# 2006-02-24 00:00:00+00:00 46.0 136.0
# 2006-02-27 00:00:00+00:00 47.0 137.0
# 2006-02-28 00:00:00+00:00 48.0 138.0
# Mean volume is (119 + 138) / 2 = 128.5
self.assertEqual(mean_volume, 128.5)
# Volatility is closes.pct_change().std() * sqrt(252)
reference_vol = pd.Series(range(29, 49)).pct_change().std() * sqrt(252)
self.assertEqual(volatility, reference_vol)
class OrdersStopTestCase(WithSimParams,
WithAssetFinder,
WithTradingCalendars,
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.asset_finder.retrieve_asset(133)
STOP_ORDER_CASES = {
# Stop orders can be long/short and have their price greater or
# less than the stop.
#
# A stop being reached is conditional on the order direction.
# Long orders reach the stop when the price is greater than the stop.
# Short orders reach the stop when the price is less than the stop.
#
# Which leads to the following 4 cases:
#
# | long | short |
# | price > stop | | |
# | price < stop | | |
#
# Currently the slippage module acts according to the following table,
# where 'X' represents triggering a transaction
# | long | short |
# | price > stop | | X |
# | price < stop | X | |
#
# However, the following behavior *should* be followed.
#
# | long | short |
# | price > stop | X | |
# | price < stop | | X |
'long | price gt stop': {
'order': {
'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
'amount': 100,
'filled': 0,
'stop': 3.5
},
'event': {
'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
'volume': 2000,
'price': 4.0,
'high': 3.15,
'low': 2.85,
'close': 4.0,
'open': 3.5
},
'expected': {
'transaction': {
'price': 4.00025,
'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
'amount': 50,
}
}
},
'long | price lt stop': {
'order': {
'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
'amount': 100,
'filled': 0,
'stop': 3.6
},
'event': {
'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
'volume': 2000,
'price': 3.5,
'high': 3.15,
'low': 2.85,
'close': 3.5,
'open': 4.0
},
'expected': {
'transaction': None
}
},
'short | price gt stop': {
'order': {
'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
'amount': -100,
'filled': 0,
'stop': 3.4
},
'event': {
'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
'volume': 2000,
'price': 3.5,
'high': 3.15,
'low': 2.85,
'close': 3.5,
'open': 3.0
},
'expected': {
'transaction': None
}
},
'short | price lt stop': {
'order': {
'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
'amount': -100,
'filled': 0,
'stop': 3.5
},
'event': {
'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
'volume': 2000,
'price': 3.0,
'high': 3.15,
'low': 2.85,
'close': 3.0,
'open': 3.0
},
'expected': {
'transaction': {
'price': 2.9998125,
'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
'amount': -50,
}
}
},
}
@parameterized.expand(sorted(
(name, case['order'], case['event'], case['expected'])
for name, case in STOP_ORDER_CASES.items()
))
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:
self.assertIsNone(txn)
else:
self.assertIsNotNone(txn)
for key, value in expected['transaction'].items():
self.assertEquals(value, txn[key])
class FixedBasisPointsSlippageTestCase(WithCreateBarData,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-05', tz='utc')
END_DATE = pd.Timestamp('2006-01-05', tz='utc')
ASSET_FINDER_EQUITY_SIDS = (133,)
first_minute = (
pd.Timestamp('2006-01-05 9:31', tz='US/Eastern').tz_convert('UTC')
)
@classmethod
def make_equity_minute_bar_data(cls):
yield 133, pd.DataFrame(
{
'open': [2.9],
'high': [3.15],
'low': [2.85],
'close': [3.00],
'volume': [200],
},
index=[cls.first_minute],
)
@classmethod
def init_class_fixtures(cls):
super(FixedBasisPointsSlippageTestCase, cls).init_class_fixtures()
cls.ASSET133 = cls.asset_finder.retrieve_asset(133)
@parameterized.expand([
# Volume limit of 10% on an order of 100 shares. Since the bar volume
# is 200, we should hit the limit and only fill 20 shares.
('5bps_over_vol_limit', 5, 0.1, 100, 3.0015, 20),
# Same as previous, but on the short side.
('5bps_negative_over_vol_limit', 5, 0.1, -100, 2.9985, -20),
# Volume limit of 10% on an order of 10 shares. We should fill the full
# amount.
('5bps_under_vol_limit', 5, 0.1, 10, 3.0015, 10),
# Same as previous, but on the short side.
('5bps_negative_under_vol_limit', 5, 0.1, -10, 2.9985, -10),
# Change the basis points value.
('10bps', 10, 0.1, 100, 3.003, 20),
# Change the volume limit points value.
('20pct_volume_limit', 5, 0.2, 100, 3.0015, 40),
])
def test_fixed_bps_slippage(self,
name,
basis_points,
volume_limit,
order_amount,
expected_price,
expected_amount):
slippage_model = FixedBasisPointsSlippage(basis_points=basis_points,
volume_limit=volume_limit)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=order_amount,
filled=0,
asset=self.ASSET133
)
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_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': expected_price,
'dt': datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': expected_amount,
'asset': self.ASSET133,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
self.assertEquals(expected_txn, txn.__dict__)
@parameterized.expand([
# Volume limit for the bar is 20. We've ordered 10 total shares.
# We should fill both orders completely.
('order_under_limit', 9, 1, 9, 1),
# Volume limit for the bar is 20. We've ordered 21 total shares.
# The second order should have one share remaining after fill.
('order_over_limit', -3, 18, -3, 17),
])
def test_volume_limit(self, name,
first_order_amount,
second_order_amount,
first_order_fill_amount,
second_order_fill_amount):
slippage_model = FixedBasisPointsSlippage(basis_points=5,
volume_limit=0.1)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=order_amount,
filled=0,
asset=self.ASSET133
)
for order_amount in [first_order_amount, second_order_amount]
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_minute,
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 2)
_, first_txn = orders_txns[0]
_, second_txn = orders_txns[1]
self.assertEquals(first_txn['amount'], first_order_fill_amount)
self.assertEquals(second_txn['amount'], second_order_fill_amount)
def test_broken_constructions(self):
with self.assertRaises(ValueError) as e:
FixedBasisPointsSlippage(basis_points=-1)
self.assertEqual(
str(e.exception),
"FixedBasisPointsSlippage() expected a value greater than "
"or equal to 0 for argument 'basis_points', but got -1 instead."
)
with self.assertRaises(ValueError) as e:
FixedBasisPointsSlippage(volume_limit=0)
self.assertEqual(
str(e.exception),
"FixedBasisPointsSlippage() expected a value strictly "
"greater than 0 for argument 'volume_limit', but got 0 instead."
)
def test_fill_zero_shares(self):
slippage_model = FixedBasisPointsSlippage(basis_points=5,
volume_limit=0.1)
# since the volume limit for the bar is 20, the first order will be
# filled and there will be a transaction for it, and the second order
# will order zero shares so there should not be a transaction for it.
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=20,
filled=0,
asset=self.ASSET133
)
] * 2
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_minute
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEqual(1, len(orders_txns))
# ordering zero shares should result in zero transactions
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=0,
filled=0,
asset=self.ASSET133
)
]
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEqual(0, len(orders_txns))
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