tests.TestBenchmark.setUpClass() - Code Metrics - Inspection of "Q 2.0" - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#858)

by Eddie

created 2016-01-06 18:40 UTC

tests.TestBenchmark.setUpClass() A

↳ Parent: tests.TestBenchmark

Complexity

Conditions

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Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	1
dl	0
loc	71
rs	9.137

How to fix Long Method

#
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from unittest import TestCase
from datetime import timedelta
import numpy as np
import pandas as pd
from testfixtures import TempDirectory
from zipline.data.us_equity_pricing import SQLiteAdjustmentWriter, \
    SQLiteAdjustmentReader
from zipline.errors import (
    BenchmarkAssetNotAvailableTooEarly,
    BenchmarkAssetNotAvailableTooLate,
    InvalidBenchmarkAsset)

from zipline.finance.trading import TradingEnvironment
from zipline.sources.benchmark_source import BenchmarkSource
from zipline.utils import factory
from zipline.utils.test_utils import create_data_portal, write_minute_data
from .test_perf_tracking import MockDailyBarSpotReader


class TestBenchmark(TestCase):
    @classmethod
    def setUpClass(cls):
        cls.env = TradingEnvironment()
        cls.tempdir = TempDirectory()

        cls.sim_params = factory.create_simulation_parameters()

        cls.env.write_data(equities_data={
            1: {
                "start_date": cls.sim_params.trading_days[0],
                "end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
            },
            2: {
                "start_date": cls.sim_params.trading_days[0],
                "end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
            },
            3: {
                "start_date": cls.sim_params.trading_days[100],
                "end_date": cls.sim_params.trading_days[-100]
            },
            4: {
                "start_date": cls.sim_params.trading_days[0],
                "end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
            }

        })

        dbpath = os.path.join(cls.tempdir.path, "adjustments.db")

        writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
                                        MockDailyBarSpotReader())
        splits = mergers = pd.DataFrame(
            {
                # Hackery to make the dtypes correct on an empty frame.
                'effective_date': np.array([], dtype=int),
                'ratio': np.array([], dtype=float),
                'sid': np.array([], dtype=int),
            },
            index=pd.DatetimeIndex([], tz='UTC'),
            columns=['effective_date', 'ratio', 'sid'],
        )
        dividends = pd.DataFrame({
            'sid': np.array([], dtype=np.uint32),
            'amount': np.array([], dtype=np.float64),
            'declared_date': np.array([], dtype='datetime64[ns]'),
            'ex_date': np.array([], dtype='datetime64[ns]'),
            'pay_date': np.array([], dtype='datetime64[ns]'),
            'record_date': np.array([], dtype='datetime64[ns]'),
        })
        declared_date = cls.sim_params.trading_days[45]
        ex_date = cls.sim_params.trading_days[50]
        record_date = pay_date = cls.sim_params.trading_days[55]

        stock_dividends = pd.DataFrame({
            'sid': np.array([4], dtype=np.uint32),
            'payment_sid': np.array([5], dtype=np.uint32),
            'ratio': np.array([2], dtype=np.float64),
            'declared_date': np.array([declared_date], dtype='datetime64[ns]'),
            'ex_date': np.array([ex_date], dtype='datetime64[ns]'),
            'record_date': np.array([record_date], dtype='datetime64[ns]'),
            'pay_date': np.array([pay_date], dtype='datetime64[ns]'),
        })
        writer.write(splits, mergers, dividends,
                     stock_dividends=stock_dividends)

        cls.data_portal = create_data_portal(
            cls.env,
            cls.tempdir,
            cls.sim_params,
            [1, 2, 3, 4],
            adjustment_reader=SQLiteAdjustmentReader(dbpath)
        )

    @classmethod
    def tearDownClass(cls):
        del cls.env
        cls.tempdir.cleanup()

    def test_normal(self):
        days_to_use = self.sim_params.trading_days[1:]

        source = BenchmarkSource(
            1, self.env, days_to_use, self.data_portal
        )

        # should be the equivalent of getting the price history, then doing
        # a pct_change on it
        manually_calculated = self.data_portal.get_history_window(
            [1], days_to_use[-1], len(days_to_use), "1d", "close_price"
        )[1].pct_change()

        # compare all the fields except the first one, for which we don't have
        # data in manually_calculated
        for idx, day in enumerate(days_to_use[1:]):
            self.assertEqual(
                source.get_value(day),
                manually_calculated[idx + 1]
            )

    def test_asset_not_trading(self):
        with self.assertRaises(BenchmarkAssetNotAvailableTooEarly) as exc:
            BenchmarkSource(
                3,
                self.env,
                self.sim_params.trading_days[1:],
                self.data_portal
            )

        self.assertEqual(
            '3 does not exist on 2006-01-04 00:00:00+00:00. '
            'It started trading on 2006-05-26 00:00:00+00:00.',
            exc.exception.message
        )

        with self.assertRaises(BenchmarkAssetNotAvailableTooLate) as exc2:
            BenchmarkSource(
                3,
                self.env,
                self.sim_params.trading_days[120:],
                self.data_portal
            )

        self.assertEqual(
            '3 does not exist on 2006-06-26 00:00:00+00:00. '
            'It stopped trading on 2006-08-09 00:00:00+00:00.',
            exc2.exception.message
        )

    def test_asset_IPOed_same_day(self):
        # gotta get some minute data up in here.
        # add sid 4 for a couple of days
        minutes = self.env.minutes_for_days_in_range(
            self.sim_params.trading_days[0],
            self.sim_params.trading_days[5]
        )

        path = write_minute_data(
            self.tempdir,
            minutes,
            [2]
        )

        self.data_portal._minutes_equities_path = path

        source = BenchmarkSource(
            2,
            self.env,
            self.sim_params.trading_days,
            self.data_portal
        )

        days_to_use = self.sim_params.trading_days

        # first value should be 0.0, coming from daily data
        self.assertAlmostEquals(0.0, source.get_value(days_to_use[0]))

        manually_calculated = self.data_portal.get_history_window(
            [2], days_to_use[-1], len(days_to_use), "1d", "close_price"
        )[2].pct_change()

        for idx, day in enumerate(days_to_use[1:]):
            self.assertEqual(
                source.get_value(day),
                manually_calculated[idx + 1]
            )

    def test_no_stock_dividends_allowed(self):
        # try to use sid(4) as benchmark, should blow up due to the presence
        # of a stock dividend

        with self.assertRaises(InvalidBenchmarkAsset) as exc:
            BenchmarkSource(
                4, self.env, self.sim_params.trading_days, self.data_portal
            )

        self.assertEqual("4 cannot be used as the benchmark because it has a "
                         "stock dividend on 2006-03-16 00:00:00.  Choose "
                         "another asset to use as the benchmark.",
                         exc.exception.message)


1			#
2			# Copyright 2015 Quantopian, Inc.
3			#
4			# Licensed under the Apache License, Version 2.0 (the "License");
5			# you may not use this file except in compliance with the License.
6			# You may obtain a copy of the License at
7			#
8			# http://www.apache.org/licenses/LICENSE-2.0
9			#
10			# Unless required by applicable law or agreed to in writing, software
11			# distributed under the License is distributed on an "AS IS" BASIS,
12			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13			# See the License for the specific language governing permissions and
14			# limitations under the License.
15			import os
16			from unittest import TestCase
17			from datetime import timedelta
18			import numpy as np
19			import pandas as pd
20			from testfixtures import TempDirectory
21			from zipline.data.us_equity_pricing import SQLiteAdjustmentWriter, \
22			SQLiteAdjustmentReader
23			from zipline.errors import (
24			BenchmarkAssetNotAvailableTooEarly,
25			BenchmarkAssetNotAvailableTooLate,
26			InvalidBenchmarkAsset)
27
28			from zipline.finance.trading import TradingEnvironment
29			from zipline.sources.benchmark_source import BenchmarkSource
30			from zipline.utils import factory
31			from zipline.utils.test_utils import create_data_portal, write_minute_data
32			from .test_perf_tracking import MockDailyBarSpotReader
33
34
35			class TestBenchmark(TestCase):
36			@classmethod
37			def setUpClass(cls):
38			cls.env = TradingEnvironment()
39			cls.tempdir = TempDirectory()
40
41			cls.sim_params = factory.create_simulation_parameters()
42
43			cls.env.write_data(equities_data={
44			1: {
45			"start_date": cls.sim_params.trading_days[0],
46			"end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
47			},
48			2: {
49			"start_date": cls.sim_params.trading_days[0],
50			"end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
51			},
52			3: {
53			"start_date": cls.sim_params.trading_days[100],
54			"end_date": cls.sim_params.trading_days[-100]
55			},
56			4: {
57			"start_date": cls.sim_params.trading_days[0],
58			"end_date": cls.sim_params.trading_days[-1] + timedelta(days=1)
59			}
60
61			})
62
63			dbpath = os.path.join(cls.tempdir.path, "adjustments.db")
64
65			writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
66			MockDailyBarSpotReader())
67			splits = mergers = pd.DataFrame(
68			{
69			# Hackery to make the dtypes correct on an empty frame.
70			'effective_date': np.array([], dtype=int),
71			'ratio': np.array([], dtype=float),
72			'sid': np.array([], dtype=int),
73			},
74			index=pd.DatetimeIndex([], tz='UTC'),
75			columns=['effective_date', 'ratio', 'sid'],
76			)
77			dividends = pd.DataFrame({
78			'sid': np.array([], dtype=np.uint32),
79			'amount': np.array([], dtype=np.float64),
80			'declared_date': np.array([], dtype='datetime64[ns]'),
81			'ex_date': np.array([], dtype='datetime64[ns]'),
82			'pay_date': np.array([], dtype='datetime64[ns]'),
83			'record_date': np.array([], dtype='datetime64[ns]'),
84			})
85			declared_date = cls.sim_params.trading_days[45]
86			ex_date = cls.sim_params.trading_days[50]
87			record_date = pay_date = cls.sim_params.trading_days[55]
88
89			stock_dividends = pd.DataFrame({
90			'sid': np.array([4], dtype=np.uint32),
91			'payment_sid': np.array([5], dtype=np.uint32),
92			'ratio': np.array([2], dtype=np.float64),
93			'declared_date': np.array([declared_date], dtype='datetime64[ns]'),
94			'ex_date': np.array([ex_date], dtype='datetime64[ns]'),
95			'record_date': np.array([record_date], dtype='datetime64[ns]'),
96			'pay_date': np.array([pay_date], dtype='datetime64[ns]'),
97			})
98			writer.write(splits, mergers, dividends,
99			stock_dividends=stock_dividends)
100
101			cls.data_portal = create_data_portal(
102			cls.env,
103			cls.tempdir,
104			cls.sim_params,
105			[1, 2, 3, 4],
106			adjustment_reader=SQLiteAdjustmentReader(dbpath)
107			)
108
109			@classmethod
110			def tearDownClass(cls):
111			del cls.env
112			cls.tempdir.cleanup()
113
114			def test_normal(self):
115			days_to_use = self.sim_params.trading_days[1:]
116
117			source = BenchmarkSource(
118			1, self.env, days_to_use, self.data_portal
119			)
120
121			# should be the equivalent of getting the price history, then doing
122			# a pct_change on it
123			manually_calculated = self.data_portal.get_history_window(
124			[1], days_to_use[-1], len(days_to_use), "1d", "close_price"
125			)[1].pct_change()
126
127			# compare all the fields except the first one, for which we don't have
128			# data in manually_calculated
129			for idx, day in enumerate(days_to_use[1:]):
130			self.assertEqual(
131			source.get_value(day),
132			manually_calculated[idx + 1]
133			)
134
135			def test_asset_not_trading(self):
136			with self.assertRaises(BenchmarkAssetNotAvailableTooEarly) as exc:
137			BenchmarkSource(
138			3,
139			self.env,
140			self.sim_params.trading_days[1:],
141			self.data_portal
142			)
143
144			self.assertEqual(
145			'3 does not exist on 2006-01-04 00:00:00+00:00. '
146			'It started trading on 2006-05-26 00:00:00+00:00.',
147			exc.exception.message
148			)
149
150			with self.assertRaises(BenchmarkAssetNotAvailableTooLate) as exc2:
151			BenchmarkSource(
152			3,
153			self.env,
154			self.sim_params.trading_days[120:],
155			self.data_portal
156			)
157
158			self.assertEqual(
159			'3 does not exist on 2006-06-26 00:00:00+00:00. '
160			'It stopped trading on 2006-08-09 00:00:00+00:00.',
161			exc2.exception.message
162			)
163
164			def test_asset_IPOed_same_day(self):
165			# gotta get some minute data up in here.
166			# add sid 4 for a couple of days
167			minutes = self.env.minutes_for_days_in_range(
168			self.sim_params.trading_days[0],
169			self.sim_params.trading_days[5]
170			)
171
172			path = write_minute_data(
173			self.tempdir,
174			minutes,
175			[2]
176			)
177
178			self.data_portal._minutes_equities_path = path
179
180			source = BenchmarkSource(
181			2,
182			self.env,
183			self.sim_params.trading_days,
184			self.data_portal
185			)
186
187			days_to_use = self.sim_params.trading_days
188
189			# first value should be 0.0, coming from daily data
190			self.assertAlmostEquals(0.0, source.get_value(days_to_use[0]))
191
192			manually_calculated = self.data_portal.get_history_window(
193			[2], days_to_use[-1], len(days_to_use), "1d", "close_price"
194			)[2].pct_change()
195
196			for idx, day in enumerate(days_to_use[1:]):
197			self.assertEqual(
198			source.get_value(day),
199			manually_calculated[idx + 1]
200			)
201
202			def test_no_stock_dividends_allowed(self):
203			# try to use sid(4) as benchmark, should blow up due to the presence
204			# of a stock dividend
205
206			with self.assertRaises(InvalidBenchmarkAsset) as exc:
207			BenchmarkSource(
208			4, self.env, self.sim_params.trading_days, self.data_portal
209			)
210
211			self.assertEqual("4 cannot be used as the benchmark because it has a "
212			"stock dividend on 2006-03-16 00:00:00. Choose "
213			"another asset to use as the benchmark.",
214			exc.exception.message)
215

quantopian / zipline

Pull Request — master (#858)

tests.TestBenchmark.setUpClass() A

Complexity

Size

Duplication

How to fix Long Method

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