mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-03 12:17:50 +08:00
Joe Jevnik
bc0b117dc9
Changes BcolzDailyBarWriter to not be an abc, data is passed as an iterator of (sid, dataframe) pairs to the write method. Changes the AssetsDBWriter to be a single class which accepts an engine at construction time and has a `write` method for writing dataframes for the various tables. We no longer support writing the various other data types, callers should coerce their data into a dataframe themselves. See zipline.assets.synthetic for some helpers to do this. Adds many new fixtures and updates some existing fixtures to use the new ones: WithDefaultDateBounds A fixture that provides the suite a START_DATE and END_DATE. This is meant to make it easy for other fixtures to synchronize their date ranges without depending on eachother in strange ways. For example, WithBcolzMinuteBarReader and WithBcolzDailyBarReader by default should both have data for the same dates, so they may use depend on WithDefaultDates without forcing a dependency between them. WithTmpDir, WithInstanceTmpDir Provides the suite or individual test case a temporary directory. WithBcolzDailyBarReader Provides the suite a BcolzDailyBarReader which reads from bcolz data written to a temporary directory. The data will be read from dataframes and then converted to bcolz files with BcolzDailyBarWriter.write WithBcolzDailyBarReaderFromCSVs Provides the suite a BcolzDailyBarReader which reads from bcolz data written to a temporary directory. The data will be read from a collection of CSV files and then converted into the bcolz data through BcolzDailyBarWriter.write_csvs WithBcolzMinuteBarReader Provides the suite a BcolzMinuteBarReader which reads from bcolz data written to a temporary directory. The data will be read from dataframes and then converted to bcolz files with BcolzMinuteBarWriter.write WithAdjustmentReader Provides the suite a SQLiteAdjustmentReader which reads from an in memory sqlite database. The data will be read from dataframes and then converted into sqlite with SQLiteAdjustmentWriter.write WithDataPortal Provides each test case a DataPortal object with data from temporary resources.
751 lines
23 KiB
Python
751 lines
23 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_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_minute_bar_data(cls):
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return {
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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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}
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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_minute_bar_reader(self.env, days, assets) as reader:
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data_portal = DataPortal(
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self.env,
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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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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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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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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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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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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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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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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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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_minute_bar_reader(self.env, days, assets) as reader:
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data_portal = DataPortal(
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self.env,
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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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_, 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):
|
|
slippage_model = VolumeShareSlippage()
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|
slippage_model.data_portal = self.data_portal
|
|
|
|
# long, does not trade
|
|
open_orders = [
|
|
Order(**{
|
|
'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
|
|
'amount': 100,
|
|
'filled': 0,
|
|
'sid': self.ASSET133,
|
|
'stop': 4.0,
|
|
'limit': 3.0})
|
|
]
|
|
|
|
bar_data = BarData(self.data_portal,
|
|
lambda: self.minutes[2],
|
|
self.sim_params.data_frequency)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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])
|