mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 17:03:07 +08:00
Joe Jevnik
7fd8c29880
Changes the overlap behavior so that it is an error to write data which would have two companies holding the same ticker. Other than one test around which company would win in that case, all the other tests are passing. That single test has been changed to check the write-time error.
208 lines
7.2 KiB
Python
208 lines
7.2 KiB
Python
#
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# Copyright 2015 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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import numpy as np
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import pandas as pd
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from zipline.data.data_portal import DataPortal
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from zipline.errors import (
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BenchmarkAssetNotAvailableTooEarly,
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BenchmarkAssetNotAvailableTooLate,
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InvalidBenchmarkAsset)
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from zipline.sources.benchmark_source import BenchmarkSource
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from zipline.testing import (
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MockDailyBarReader,
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create_minute_bar_data,
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tmp_bcolz_equity_minute_bar_reader,
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)
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from zipline.testing.fixtures import (
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WithDataPortal,
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WithSimParams,
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WithTradingCalendar,
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ZiplineTestCase,
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)
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class TestBenchmark(WithDataPortal, WithSimParams, WithTradingCalendar,
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ZiplineTestCase):
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START_DATE = pd.Timestamp('2006-01-03', tz='utc')
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END_DATE = pd.Timestamp('2006-12-29', tz='utc')
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@classmethod
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def make_equity_info(cls):
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return pd.DataFrame.from_dict(
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{
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1: {
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'symbol': 'A',
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'start_date': cls.START_DATE,
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'end_date': cls.END_DATE + pd.Timedelta(days=1)
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},
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2: {
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'symbol': 'B',
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'start_date': cls.START_DATE,
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'end_date': cls.END_DATE + pd.Timedelta(days=1)
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},
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3: {
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'symbol': 'C',
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'start_date': pd.Timestamp('2006-05-26', tz='utc'),
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'end_date': pd.Timestamp('2006-08-09', tz='utc')
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},
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4: {
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'symbol': 'D',
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'start_date': cls.START_DATE,
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'end_date': cls.END_DATE + pd.Timedelta(days=1)
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},
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},
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orient='index',
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)
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@classmethod
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def make_adjustment_writer_equity_daily_bar_reader(cls):
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return MockDailyBarReader()
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@classmethod
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def make_stock_dividends_data(cls):
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declared_date = cls.sim_params.sessions[45]
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ex_date = cls.sim_params.sessions[50]
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record_date = pay_date = cls.sim_params.sessions[55]
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return pd.DataFrame({
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'sid': np.array([4], dtype=np.uint32),
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'payment_sid': np.array([5], dtype=np.uint32),
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'ratio': np.array([2], dtype=np.float64),
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'declared_date': np.array([declared_date], dtype='datetime64[ns]'),
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'ex_date': np.array([ex_date], dtype='datetime64[ns]'),
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'record_date': np.array([record_date], dtype='datetime64[ns]'),
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'pay_date': np.array([pay_date], dtype='datetime64[ns]'),
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})
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def test_normal(self):
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days_to_use = self.sim_params.sessions[1:]
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source = BenchmarkSource(
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1, self.env, self.trading_calendar, days_to_use, self.data_portal
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)
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# should be the equivalent of getting the price history, then doing
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# a pct_change on it
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manually_calculated = self.data_portal.get_history_window(
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[1], days_to_use[-1], len(days_to_use), "1d", "close"
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)[1].pct_change()
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# compare all the fields except the first one, for which we don't have
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# data in manually_calculated
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for idx, day in enumerate(days_to_use[1:]):
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self.assertEqual(
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source.get_value(day),
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manually_calculated[idx + 1]
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)
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def test_asset_not_trading(self):
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benchmark = self.env.asset_finder.retrieve_asset(3)
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benchmark_start = benchmark.start_date
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benchmark_end = benchmark.end_date
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with self.assertRaises(BenchmarkAssetNotAvailableTooEarly) as exc:
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BenchmarkSource(
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3,
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self.env,
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self.trading_calendar,
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self.sim_params.sessions[1:],
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self.data_portal
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)
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self.assertEqual(
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'3 does not exist on %s. It started trading on %s.' %
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(self.sim_params.sessions[1], benchmark_start),
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exc.exception.message
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)
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with self.assertRaises(BenchmarkAssetNotAvailableTooLate) as exc2:
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BenchmarkSource(
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3,
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self.env,
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self.trading_calendar,
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self.sim_params.sessions[120:],
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self.data_portal
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)
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self.assertEqual(
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'3 does not exist on %s. It stopped trading on %s.' %
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(self.sim_params.sessions[-1], benchmark_end),
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exc2.exception.message
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)
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def test_asset_IPOed_same_day(self):
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# gotta get some minute data up in here.
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# add sid 4 for a couple of days
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minutes = self.trading_calendar.minutes_for_sessions_in_range(
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self.sim_params.sessions[0],
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self.sim_params.sessions[5]
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)
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tmp_reader = tmp_bcolz_equity_minute_bar_reader(
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self.trading_calendar,
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self.trading_calendar.all_sessions,
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create_minute_bar_data(minutes, [2]),
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)
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with tmp_reader 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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equity_daily_reader=self.bcolz_equity_daily_bar_reader,
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adjustment_reader=self.adjustment_reader,
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)
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source = BenchmarkSource(
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2,
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self.env,
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self.trading_calendar,
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self.sim_params.sessions,
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data_portal
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)
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days_to_use = self.sim_params.sessions
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# first value should be 0.0, coming from daily data
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self.assertAlmostEquals(0.0, source.get_value(days_to_use[0]))
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manually_calculated = data_portal.get_history_window(
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[2], days_to_use[-1],
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len(days_to_use),
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"1d",
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"close",
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)[2].pct_change()
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for idx, day in enumerate(days_to_use[1:]):
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self.assertEqual(
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source.get_value(day),
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manually_calculated[idx + 1]
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)
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def test_no_stock_dividends_allowed(self):
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# try to use sid(4) as benchmark, should blow up due to the presence
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# of a stock dividend
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with self.assertRaises(InvalidBenchmarkAsset) as exc:
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BenchmarkSource(
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4, self.env, self.trading_calendar,
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self.sim_params.sessions, self.data_portal
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)
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self.assertEqual("4 cannot be used as the benchmark because it has a "
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"stock dividend on 2006-03-16 00:00:00. Choose "
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"another asset to use as the benchmark.",
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exc.exception.message)
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