mirror of
https://github.com/wassname/catalyst.git
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Jean Bredeche
46c0c064fe
BarData now takes the trading calendar as a parameter. can_trade now checks if the asset’s exchange is open at the current or next market minute (defined by the given trading calendar).
774 lines
24 KiB
Python
774 lines
24 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an 'AS IS' BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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'''
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Unit tests for finance.slippage
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'''
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import datetime
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import pytz
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from nose_parameterized import parameterized
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import pandas as pd
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from pandas.tslib import normalize_date
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from zipline.finance.slippage import VolumeShareSlippage
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from zipline.protocol import DATASOURCE_TYPE
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from zipline.finance.blotter import Order
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from zipline.data.data_portal import DataPortal
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from zipline.protocol import BarData
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from zipline.testing import tmp_bcolz_equity_minute_bar_reader
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from zipline.testing.fixtures import (
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WithDataPortal,
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WithSimParams,
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ZiplineTestCase,
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)
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class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase):
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START_DATE = pd.Timestamp('2006-01-05 14:31', tz='utc')
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END_DATE = pd.Timestamp('2006-01-05 14:36', tz='utc')
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SIM_PARAMS_CAPITAL_BASE = 1.0e5
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SIM_PARAMS_DATA_FREQUENCY = 'minute'
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SIM_PARAMS_EMISSION_RATE = 'daily'
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ASSET_FINDER_EQUITY_SIDS = (133,)
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ASSET_FINDER_EQUITY_START_DATE = pd.Timestamp('2006-01-05', tz='utc')
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ASSET_FINDER_EQUITY_END_DATE = pd.Timestamp('2006-01-07', tz='utc')
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minutes = pd.DatetimeIndex(
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start=START_DATE,
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end=END_DATE - pd.Timedelta('1 minute'),
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freq='1min'
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)
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@classmethod
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def make_equity_minute_bar_data(cls):
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yield 133, pd.DataFrame(
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{
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'open': [3.0, 3.0, 3.5, 4.0, 3.5],
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'high': [3.15, 3.15, 3.15, 3.15, 3.15],
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'low': [2.85, 2.85, 2.85, 2.85, 2.85],
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'close': [3.0, 3.5, 4.0, 3.5, 3.0],
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'volume': [2000, 2000, 2000, 2000, 2000],
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},
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index=cls.minutes,
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)
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@classmethod
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def init_class_fixtures(cls):
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super(SlippageTestCase, cls).init_class_fixtures()
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cls.ASSET133 = cls.env.asset_finder.retrieve_asset(133)
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def test_volume_share_slippage(self):
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assets = (
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(133, pd.DataFrame(
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{
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'open': [3.00],
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'high': [3.15],
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'low': [2.85],
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'close': [3.00],
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'volume': [200],
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},
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index=[self.minutes[0]],
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)),
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)
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days = pd.date_range(
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start=normalize_date(self.minutes[0]),
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end=normalize_date(self.minutes[-1])
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)
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with tmp_bcolz_equity_minute_bar_reader(self.trading_calendar, days, assets) \
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as reader:
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data_portal = DataPortal(
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self.env.asset_finder, self.trading_calendar,
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first_trading_day=reader.first_trading_day,
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equity_minute_reader=reader,
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)
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slippage_model = VolumeShareSlippage()
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open_orders = [
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Order(
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dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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amount=100,
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filled=0,
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sid=self.ASSET133
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)
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]
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bar_data = BarData(data_portal,
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lambda: self.minutes[0],
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'minute',
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 1)
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_, txn = orders_txns[0]
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expected_txn = {
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'price': float(3.0001875),
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'dt': datetime.datetime(
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2006, 1, 5, 14, 31, tzinfo=pytz.utc),
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'amount': int(5),
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'sid': int(133),
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'commission': None,
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'type': DATASOURCE_TYPE.TRANSACTION,
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'order_id': open_orders[0].id
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}
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self.assertIsNotNone(txn)
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# TODO: Make expected_txn an Transaction object and ensure there
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# is a __eq__ for that class.
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self.assertEquals(expected_txn, txn.__dict__)
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open_orders = [
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Order(
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dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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amount=100,
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filled=0,
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sid=self.ASSET133
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)
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]
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# Set bar_data to be a minute ahead of last trade.
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# Volume share slippage should not execute when there is no trade.
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bar_data = BarData(data_portal,
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lambda: self.minutes[1],
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'minute',
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 0)
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def test_orders_limit(self):
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slippage_model = VolumeShareSlippage()
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slippage_model.data_portal = self.data_portal
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# long, does not trade
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': 100,
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'filled': 0,
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'sid': self.ASSET133,
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'limit': 3.5})
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]
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bar_data = BarData(self.data_portal,
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lambda: self.minutes[3],
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self.sim_params.data_frequency,
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 0)
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# long, does not trade - impacted price worse than limit price
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': 100,
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'filled': 0,
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'sid': self.ASSET133,
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'limit': 3.5})
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]
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bar_data = BarData(self.data_portal,
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lambda: self.minutes[3],
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self.sim_params.data_frequency,
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 0)
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# long, does trade
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': 100,
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'filled': 0,
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'sid': self.ASSET133,
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'limit': 3.6})
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]
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bar_data = BarData(self.data_portal,
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lambda: self.minutes[3],
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self.sim_params.data_frequency,
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 1)
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txn = orders_txns[0][1]
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expected_txn = {
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'price': float(3.50021875),
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'dt': datetime.datetime(
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2006, 1, 5, 14, 34, tzinfo=pytz.utc),
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# we ordered 100 shares, but default volume slippage only allows
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# for 2.5% of the volume. 2.5% * 2000 = 50 shares
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'amount': int(50),
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'sid': int(133),
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'order_id': open_orders[0].id
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}
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self.assertIsNotNone(txn)
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for key, value in expected_txn.items():
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self.assertEquals(value, txn[key])
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# short, does not trade
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': -100,
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'filled': 0,
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'sid': self.ASSET133,
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'limit': 3.5})
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]
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bar_data = BarData(self.data_portal,
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lambda: self.minutes[0],
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self.sim_params.data_frequency,
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 0)
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# short, does not trade - impacted price worse than limit price
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': -100,
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'filled': 0,
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'sid': self.ASSET133,
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'limit': 3.5})
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]
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bar_data = BarData(self.data_portal,
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lambda: self.minutes[0],
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self.sim_params.data_frequency,
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 0)
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# short, does trade
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': -100,
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'filled': 0,
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'sid': self.ASSET133,
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'limit': 3.4})
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]
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bar_data = BarData(self.data_portal,
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lambda: self.minutes[1],
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self.sim_params.data_frequency,
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self.trading_calendar)
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orders_txns = list(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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open_orders,
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))
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self.assertEquals(len(orders_txns), 1)
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_, txn = orders_txns[0]
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expected_txn = {
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'price': float(3.49978125),
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'dt': datetime.datetime(
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2006, 1, 5, 14, 32, tzinfo=pytz.utc),
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'amount': int(-50),
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'sid': int(133)
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}
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self.assertIsNotNone(txn)
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for key, value in expected_txn.items():
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self.assertEquals(value, txn[key])
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STOP_ORDER_CASES = {
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# Stop orders can be long/short and have their price greater or
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# less than the stop.
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#
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# A stop being reached is conditional on the order direction.
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# Long orders reach the stop when the price is greater than the stop.
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# Short orders reach the stop when the price is less than the stop.
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#
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# Which leads to the following 4 cases:
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#
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# | long | short |
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# | price > stop | | |
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# | price < stop | | |
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#
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# Currently the slippage module acts according to the following table,
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# where 'X' represents triggering a transaction
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# | long | short |
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# | price > stop | | X |
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# | price < stop | X | |
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#
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# However, the following behavior *should* be followed.
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#
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# | long | short |
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# | price > stop | X | |
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# | price < stop | | X |
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'long | price gt stop': {
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'order': {
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'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
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'amount': 100,
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'filled': 0,
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'sid': 133,
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'stop': 3.5
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},
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'event': {
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'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
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'volume': 2000,
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'price': 4.0,
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'high': 3.15,
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'low': 2.85,
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'sid': 133,
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'close': 4.0,
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'open': 3.5
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},
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'expected': {
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'transaction': {
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'price': 4.00025,
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'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
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'amount': 50,
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'sid': 133,
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}
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}
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},
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'long | price lt stop': {
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'order': {
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'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
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'amount': 100,
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'filled': 0,
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'sid': 133,
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'stop': 3.6
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},
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'event': {
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'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
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'volume': 2000,
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'price': 3.5,
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'high': 3.15,
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'low': 2.85,
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'sid': 133,
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'close': 3.5,
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'open': 4.0
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},
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'expected': {
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'transaction': None
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}
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},
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'short | price gt stop': {
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'order': {
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'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
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'amount': -100,
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'filled': 0,
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'sid': 133,
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'stop': 3.4
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},
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'event': {
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'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
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'volume': 2000,
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'price': 3.5,
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'high': 3.15,
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'low': 2.85,
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'sid': 133,
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'close': 3.5,
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'open': 3.0
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},
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'expected': {
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'transaction': None
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}
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},
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'short | price lt stop': {
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'order': {
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'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'),
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'amount': -100,
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'filled': 0,
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'sid': 133,
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'stop': 3.5
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},
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'event': {
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'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
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'volume': 2000,
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'price': 3.0,
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'high': 3.15,
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'low': 2.85,
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'sid': 133,
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'close': 3.0,
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'open': 3.0
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},
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'expected': {
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'transaction': {
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'price': 2.9998125,
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'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'),
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'amount': -50,
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'sid': 133,
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}
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}
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},
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}
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@parameterized.expand([
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(name, case['order'], case['event'], case['expected'])
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for name, case in STOP_ORDER_CASES.items()
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])
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def test_orders_stop(self, name, order_data, event_data, expected):
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data = order_data
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data['sid'] = self.ASSET133
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order = Order(**data)
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assets = (
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(133, pd.DataFrame(
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{
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'open': [event_data['open']],
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'high': [event_data['high']],
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'low': [event_data['low']],
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'close': [event_data['close']],
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'volume': [event_data['volume']],
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},
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index=[pd.Timestamp('2006-01-05 14:31', tz='UTC')],
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)),
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)
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days = pd.date_range(
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start=normalize_date(self.minutes[0]),
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end=normalize_date(self.minutes[-1])
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)
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with tmp_bcolz_equity_minute_bar_reader(self.trading_calendar, days, assets) \
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as reader:
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data_portal = DataPortal(
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self.env.asset_finder, self.trading_calendar,
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first_trading_day=reader.first_trading_day,
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equity_minute_reader=reader,
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)
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slippage_model = VolumeShareSlippage()
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try:
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dt = pd.Timestamp('2006-01-05 14:31', tz='UTC')
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bar_data = BarData(data_portal,
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lambda: dt,
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'minute',
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self.trading_calendar)
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_, txn = next(slippage_model.simulate(
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bar_data,
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self.ASSET133,
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[order],
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))
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except StopIteration:
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txn = None
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if expected['transaction'] is None:
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self.assertIsNone(txn)
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else:
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self.assertIsNotNone(txn)
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for key, value in expected['transaction'].items():
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self.assertEquals(value, txn[key])
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def test_orders_stop_limit(self):
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slippage_model = VolumeShareSlippage()
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slippage_model.data_portal = self.data_portal
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# long, does not trade
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open_orders = [
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Order(**{
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'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
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'amount': 100,
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'filled': 0,
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'sid': self.ASSET133,
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'stop': 4.0,
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'limit': 3.0})
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]
|
|
|
|
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])
|