tests.pipeline.ClosesOnly.before_trading_start() - 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.pipeline.ClosesOnly.before_trading_start() A

↳ Parent: tests.pipeline.ClosesOnly.test_pipeline_output_after_initialize()

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cc	1
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"""
Tests for Algorithms using the Pipeline API.
"""
import os
from unittest import TestCase
from os.path import (
    dirname,
    join,
    realpath,
)

from nose_parameterized import parameterized
from numpy import (
    array,
    arange,
    full_like,
    float64,
    nan,
    uint32,
)
from numpy.testing import assert_almost_equal
from pandas import (
    concat,
    DataFrame,
    date_range,
    DatetimeIndex,
    read_csv,
    Series,
    Timestamp,
)
from six import iteritems, itervalues
from testfixtures import TempDirectory

from zipline.algorithm import TradingAlgorithm
from zipline.api import (
    attach_pipeline,
    pipeline_output,
    get_datetime,
)
from zipline.data.data_portal import DataPortal
from zipline.errors import (
    AttachPipelineAfterInitialize,
    PipelineOutputDuringInitialize,
    NoSuchPipeline,
)
from zipline.data.us_equity_pricing import (
    BcolzDailyBarReader,
    DailyBarWriterFromCSVs,
    SQLiteAdjustmentWriter,
    SQLiteAdjustmentReader,
)
from zipline.finance import trading
from zipline.lib.adjustment import MULTIPLY
from zipline.pipeline import Pipeline
from zipline.pipeline.factors import VWAP
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.loaders.frame import DataFrameLoader
from zipline.pipeline.loaders.equity_pricing_loader import (
    USEquityPricingLoader,
)
from zipline.utils.test_utils import (
    make_simple_equity_info,
    str_to_seconds,
    DailyBarWriterFromDataFrames, FakeDataPortal)
from zipline.utils.tradingcalendar import (
    trading_day,
    trading_days,
)


TEST_RESOURCE_PATH = join(
    dirname(dirname(realpath(__file__))),  # zipline_repo/tests
    'resources',
    'pipeline_inputs',
)


def rolling_vwap(df, length):
    "Simple rolling vwap implementation for testing"
    closes = df['close'].values
    volumes = df['volume'].values
    product = closes * volumes
    out = full_like(closes, nan)
    for upper_bound in range(length, len(closes) + 1):
        bounds = slice(upper_bound - length, upper_bound)
        out[upper_bound - 1] = product[bounds].sum() / volumes[bounds].sum()

    return Series(out, index=df.index)


class ClosesOnly(TestCase):

    @classmethod
    def setUpClass(cls):
        cls.tempdir = TempDirectory()

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

    def setUp(self):
        self.env = env = trading.TradingEnvironment()
        self.dates = date_range(
            '2014-01-01', '2014-02-01', freq=trading_day, tz='UTC'
        )
        asset_info = DataFrame.from_records([
            {
                'sid': 1,
                'symbol': 'A',
                'start_date': self.dates[10],
                'end_date': self.dates[13],
                'exchange': 'TEST',
            },
            {
                'sid': 2,
                'symbol': 'B',
                'start_date': self.dates[11],
                'end_date': self.dates[14],
                'exchange': 'TEST',
            },
            {
                'sid': 3,
                'symbol': 'C',
                'start_date': self.dates[12],
                'end_date': self.dates[15],
                'exchange': 'TEST',
            },
        ])
        self.first_asset_start = min(asset_info.start_date)
        self.last_asset_end = max(asset_info.end_date)
        env.write_data(equities_df=asset_info)
        self.asset_finder = finder = env.asset_finder

        sids = (1, 2, 3)
        self.assets = finder.retrieve_all(sids)

        # View of the baseline data.
        self.closes = DataFrame(
            {sid: arange(1, len(self.dates) + 1) * sid for sid in sids},
            index=self.dates,
            dtype=float,
        )

        # Create a data portal holding the data in self.closes
        data = {}
        for sid in sids:
            data[sid] = DataFrame({
                "open": self.closes[sid].values,
                "high": self.closes[sid].values,
                "low": self.closes[sid].values,
                "close": self.closes[sid].values,
                "volume": self.closes[sid].values,
                "day": [day.value for day in self.dates]
            })

        path = os.path.join(self.tempdir.path, "testdaily.bcolz")

        DailyBarWriterFromDataFrames(data).write(
            path,
            self.dates,
            data
        )

        daily_bar_reader = BcolzDailyBarReader(path)

        self.data_portal = DataPortal(
            self.env,
            equity_daily_reader=daily_bar_reader,
        )

        # Add a split for 'A' on its second date.
        self.split_asset = self.assets[0]
        self.split_date = self.split_asset.start_date + trading_day
        self.split_ratio = 0.5
        self.adjustments = DataFrame.from_records([
            {
                'sid': self.split_asset.sid,
                'value': self.split_ratio,
                'kind': MULTIPLY,
                'start_date': Timestamp('NaT'),
                'end_date': self.split_date,
                'apply_date': self.split_date,
            }
        ])

        # View of the data on/after the split.
        self.adj_closes = adj_closes = self.closes.copy()
        adj_closes.ix[:self.split_date, self.split_asset] *= self.split_ratio

        self.pipeline_loader = DataFrameLoader(
            column=USEquityPricing.close,
            baseline=self.closes,
            adjustments=self.adjustments,
        )

    def expected_close(self, date, asset):
        if date < self.split_date:
            lookup = self.closes
        else:
            lookup = self.adj_closes
        return lookup.loc[date, asset]

    def exists(self, date, asset):
        return asset.start_date <= date <= asset.end_date

    def test_attach_pipeline_after_initialize(self):
        """
        Assert that calling attach_pipeline after initialize raises correctly.
        """
        def initialize(context):
            pass

        def late_attach(context, data):
            attach_pipeline(Pipeline(), 'test')
            raise AssertionError("Shouldn't make it past attach_pipeline!")

        algo = TradingAlgorithm(
            initialize=initialize,
            handle_data=late_attach,
            data_frequency='daily',
            get_pipeline_loader=lambda column: self.pipeline_loader,
            start=self.first_asset_start - trading_day,
            end=self.last_asset_end + trading_day,
            env=self.env,
        )

        with self.assertRaises(AttachPipelineAfterInitialize):
            algo.run(data_portal=self.data_portal)

        def barf(context, data):
            raise AssertionError("Shouldn't make it past before_trading_start")

        algo = TradingAlgorithm(
            initialize=initialize,
            before_trading_start=late_attach,
            handle_data=barf,
            data_frequency='daily',
            get_pipeline_loader=lambda column: self.pipeline_loader,
            start=self.first_asset_start - trading_day,
            end=self.last_asset_end + trading_day,
            env=self.env,
        )

        with self.assertRaises(AttachPipelineAfterInitialize):
            algo.run(data_portal=self.data_portal)

    def test_pipeline_output_after_initialize(self):
        """
        Assert that calling pipeline_output after initialize raises correctly.
        """
        def initialize(context):
            attach_pipeline(Pipeline(), 'test')
            pipeline_output('test')
            raise AssertionError("Shouldn't make it past pipeline_output()")

        def handle_data(context, data):
            raise AssertionError("Shouldn't make it past initialize!")

        def before_trading_start(context, data):
            raise AssertionError("Shouldn't make it past initialize!")

        algo = TradingAlgorithm(
            initialize=initialize,
            handle_data=handle_data,
            before_trading_start=before_trading_start,
            data_frequency='daily',
            get_pipeline_loader=lambda column: self.pipeline_loader,
            start=self.first_asset_start - trading_day,
            end=self.last_asset_end + trading_day,
            env=self.env,
        )

        with self.assertRaises(PipelineOutputDuringInitialize):
            algo.run(data_portal=self.data_portal)

    def test_get_output_nonexistent_pipeline(self):
        """
        Assert that calling add_pipeline after initialize raises appropriately.
        """
        def initialize(context):
            attach_pipeline(Pipeline(), 'test')

        def handle_data(context, data):
            raise AssertionError("Shouldn't make it past before_trading_start")

        def before_trading_start(context, data):
            pipeline_output('not_test')
            raise AssertionError("Shouldn't make it past pipeline_output!")

        algo = TradingAlgorithm(
            initialize=initialize,
            handle_data=handle_data,
            before_trading_start=before_trading_start,
            data_frequency='daily',
            get_pipeline_loader=lambda column: self.pipeline_loader,
            start=self.first_asset_start - trading_day,
            end=self.last_asset_end + trading_day,
            env=self.env,
        )

        with self.assertRaises(NoSuchPipeline):
            algo.run(data_portal=self.data_portal)

    @parameterized.expand([('default', None),
                           ('day', 1),
                           ('week', 5),
                           ('year', 252),
                           ('all_but_one_day', 'all_but_one_day')])
    def test_assets_appear_on_correct_days(self, test_name, chunksize):
        """
        Assert that assets appear at correct times during a backtest, with
        correctly-adjusted close price values.
        """

        if chunksize == 'all_but_one_day':
            chunksize = (
                self.dates.get_loc(self.last_asset_end) -
                self.dates.get_loc(self.first_asset_start)
            ) - 1

        def initialize(context):
            p = attach_pipeline(Pipeline(), 'test', chunksize=chunksize)
            p.add(USEquityPricing.close.latest, 'close')

        def handle_data(context, data):
            results = pipeline_output('test')
            date = get_datetime().normalize()
            for asset in self.assets:
                # Assets should appear iff they exist today and yesterday.
                exists_today = self.exists(date, asset)
                existed_yesterday = self.exists(date - trading_day, asset)
                if exists_today and existed_yesterday:
                    latest = results.loc[asset, 'close']
                    self.assertEqual(latest, self.expected_close(date, asset))
                else:
                    self.assertNotIn(asset, results.index)

        before_trading_start = handle_data

        algo = TradingAlgorithm(
            initialize=initialize,
            handle_data=handle_data,
            before_trading_start=before_trading_start,
            data_frequency='daily',
            get_pipeline_loader=lambda column: self.pipeline_loader,
            start=self.first_asset_start,
            end=self.last_asset_end,
            env=self.env,
        )

        # Run for a week in the middle of our data.
        algo.run(data_portal=self.data_portal)


class MockDailyBarSpotReader(object):
    """
    A BcolzDailyBarReader which returns a constant value for spot price.
    """
    def spot_price(self, sid, day, column):
        return 100.0


class PipelineAlgorithmTestCase(TestCase):

    @classmethod
    def setUpClass(cls):
        cls.AAPL = 1
        cls.MSFT = 2
        cls.BRK_A = 3
        cls.assets = [cls.AAPL, cls.MSFT, cls.BRK_A]
        asset_info = make_simple_equity_info(
            cls.assets,
            Timestamp('2014'),
            Timestamp('2015'),
            ['AAPL', 'MSFT', 'BRK_A'],
        )
        cls.env = trading.TradingEnvironment()
        cls.env.write_data(equities_df=asset_info)
        cls.tempdir = tempdir = TempDirectory()
        tempdir.create()
        try:
            cls.raw_data, cls.bar_reader = cls.create_bar_reader(tempdir)
            cls.adj_reader = cls.create_adjustment_reader(tempdir)
            cls.pipeline_loader = USEquityPricingLoader(
                cls.bar_reader, cls.adj_reader
            )
        except:
            cls.tempdir.cleanup()
            raise

        cls.dates = cls.raw_data[cls.AAPL].index.tz_localize('UTC')
        cls.AAPL_split_date = Timestamp("2014-06-09", tz='UTC')

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

    @classmethod
    def create_bar_reader(cls, tempdir):
        resources = {
            cls.AAPL: join(TEST_RESOURCE_PATH, 'AAPL.csv'),
            cls.MSFT: join(TEST_RESOURCE_PATH, 'MSFT.csv'),
            cls.BRK_A: join(TEST_RESOURCE_PATH, 'BRK-A.csv'),
        }
        raw_data = {
            asset: read_csv(path, parse_dates=['day']).set_index('day')
            for asset, path in iteritems(resources)
        }
        # Add 'price' column as an alias because all kinds of stuff in zipline
        # depends on it being present. :/
        for frame in raw_data.values():
            frame['price'] = frame['close']

        writer = DailyBarWriterFromCSVs(resources)
        data_path = tempdir.getpath('testdata.bcolz')
        table = writer.write(data_path, trading_days, cls.assets)
        return raw_data, BcolzDailyBarReader(table)

    @classmethod
    def create_adjustment_reader(cls, tempdir):
        dbpath = tempdir.getpath('adjustments.sqlite')
        writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
                                        MockDailyBarSpotReader())
        splits = DataFrame.from_records([
            {
                'effective_date': str_to_seconds('2014-06-09'),
                'ratio': (1 / 7.0),
                'sid': cls.AAPL,
            }
        ])
        mergers = DataFrame(
            {
                # Hackery to make the dtypes correct on an empty frame.
                'effective_date': array([], dtype=int),
                'ratio': array([], dtype=float),
                'sid': array([], dtype=int),
            },
            index=DatetimeIndex([], tz='UTC'),
            columns=['effective_date', 'ratio', 'sid'],
        )
        dividends = DataFrame({
            'sid': array([], dtype=uint32),
            'amount': array([], dtype=float64),
            'record_date': array([], dtype='datetime64[ns]'),
            'ex_date': array([], dtype='datetime64[ns]'),
            'declared_date': array([], dtype='datetime64[ns]'),
            'pay_date': array([], dtype='datetime64[ns]'),
        })
        writer.write(splits, mergers, dividends)
        return SQLiteAdjustmentReader(dbpath)

    def compute_expected_vwaps(self, window_lengths):
        AAPL, MSFT, BRK_A = self.AAPL, self.MSFT, self.BRK_A

        # Our view of the data before AAPL's split on June 9, 2014.
        raw = {k: v.copy() for k, v in iteritems(self.raw_data)}

        split_date = self.AAPL_split_date
        split_loc = self.dates.get_loc(split_date)
        split_ratio = 7.0

        # Our view of the data after AAPL's split.  All prices from before June
        # 9 get divided by the split ratio, and volumes get multiplied by the
        # split ratio.
        adj = {k: v.copy() for k, v in iteritems(self.raw_data)}
        for column in 'open', 'high', 'low', 'close':
            adj[AAPL].ix[:split_loc, column] /= split_ratio
        adj[AAPL].ix[:split_loc, 'volume'] *= split_ratio

        # length -> asset -> expected vwap
        vwaps = {length: {} for length in window_lengths}
        for length in window_lengths:
            for asset in AAPL, MSFT, BRK_A:
                raw_vwap = rolling_vwap(raw[asset], length)
                adj_vwap = rolling_vwap(adj[asset], length)
                # Shift computed results one day forward so that they're
                # labelled by the date on which they'll be seen in the
                # algorithm. (We can't show the close price for day N until day
                # N + 1.)
                vwaps[length][asset] = concat(
                    [
                        raw_vwap[:split_loc - 1],
                        adj_vwap[split_loc - 1:]
                    ]
                ).shift(1, trading_day)

        # Make sure all the expected vwaps have the same dates.
        vwap_dates = vwaps[1][self.AAPL].index
        for dict_ in itervalues(vwaps):
            # Each value is a dict mapping sid -> expected series.
            for series in itervalues(dict_):
                self.assertTrue((vwap_dates == series.index).all())

        # Spot check expectations near the AAPL split.
        # length 1 vwap for the morning before the split should be the close
        # price of the previous day.
        before_split = vwaps[1][AAPL].loc[split_date - trading_day]
        assert_almost_equal(before_split, 647.3499, decimal=2)
        assert_almost_equal(
            before_split,
            raw[AAPL].loc[split_date - (2 * trading_day), 'close'],
            decimal=2,
        )

        # length 1 vwap for the morning of the split should be the close price
        # of the previous day, **ADJUSTED FOR THE SPLIT**.
        on_split = vwaps[1][AAPL].loc[split_date]
        assert_almost_equal(on_split, 645.5700 / split_ratio, decimal=2)
        assert_almost_equal(
            on_split,
            raw[AAPL].loc[split_date - trading_day, 'close'] / split_ratio,
            decimal=2,
        )

        # length 1 vwap on the day after the split should be the as-traded
        # close on the split day.
        after_split = vwaps[1][AAPL].loc[split_date + trading_day]
        assert_almost_equal(after_split, 93.69999, decimal=2)
        assert_almost_equal(
            after_split,
            raw[AAPL].loc[split_date, 'close'],
            decimal=2,
        )

        return vwaps

    @parameterized.expand([
        (True,),
        (False,),
    ])
    def test_handle_adjustment(self, set_screen):
        AAPL, MSFT, BRK_A = assets = self.AAPL, self.MSFT, self.BRK_A

        window_lengths = [1, 2, 5, 10]
        vwaps = self.compute_expected_vwaps(window_lengths)

        def vwap_key(length):
            return "vwap_%d" % length

        def initialize(context):
            pipeline = Pipeline()
            context.vwaps = []
            for length in vwaps:
                name = vwap_key(length)
                factor = VWAP(window_length=length)
                context.vwaps.append(factor)
                pipeline.add(factor, name=name)

            filter_ = (USEquityPricing.close.latest > 300)
            pipeline.add(filter_, 'filter')
            if set_screen:
                pipeline.set_screen(filter_)

            attach_pipeline(pipeline, 'test')

        def handle_data(context, data):
            today = get_datetime()
            results = pipeline_output('test')
            expect_over_300 = {
                AAPL: today < self.AAPL_split_date,
                MSFT: False,
                BRK_A: True,
            }
            for asset in assets:
                should_pass_filter = expect_over_300[asset]
                if set_screen and not should_pass_filter:
                    self.assertNotIn(asset, results.index)
                    continue

                asset_results = results.loc[asset]
                self.assertEqual(asset_results['filter'], should_pass_filter)
                for length in vwaps:
                    computed = results.loc[asset, vwap_key(length)]
                    expected = vwaps[length][asset].loc[today]
                    # Only having two places of precision here is a bit
                    # unfortunate.
                    assert_almost_equal(computed, expected, decimal=2)

        # Do the same checks in before_trading_start
        before_trading_start = handle_data

        algo = TradingAlgorithm(
            initialize=initialize,
            handle_data=handle_data,
            before_trading_start=before_trading_start,
            data_frequency='daily',
            get_pipeline_loader=lambda column: self.pipeline_loader,
            start=self.dates[max(window_lengths)],
            end=self.dates[-1],
            env=self.env,
        )

        algo.run(data_portal=FakeDataPortal())


1			"""
2			Tests for Algorithms using the Pipeline API.
3			"""
4			import os
5			from unittest import TestCase
6			from os.path import (
7			dirname,
8			join,
9			realpath,
10			)
11
12			from nose_parameterized import parameterized
13			from numpy import (
14			array,
15			arange,
16			full_like,
17			float64,
18			nan,
19			uint32,
20			)
21			from numpy.testing import assert_almost_equal
22			from pandas import (
23			concat,
24			DataFrame,
25			date_range,
26			DatetimeIndex,
27			read_csv,
28			Series,
29			Timestamp,
30			)
31			from six import iteritems, itervalues
32			from testfixtures import TempDirectory
33
34			from zipline.algorithm import TradingAlgorithm
35			from zipline.api import (
36			attach_pipeline,
37			pipeline_output,
38			get_datetime,
39			)
40			from zipline.data.data_portal import DataPortal
41			from zipline.errors import (
42			AttachPipelineAfterInitialize,
43			PipelineOutputDuringInitialize,
44			NoSuchPipeline,
45			)
46			from zipline.data.us_equity_pricing import (
47			BcolzDailyBarReader,
48			DailyBarWriterFromCSVs,
49			SQLiteAdjustmentWriter,
50			SQLiteAdjustmentReader,
51			)
52			from zipline.finance import trading
53			from zipline.lib.adjustment import MULTIPLY
54			from zipline.pipeline import Pipeline
55			from zipline.pipeline.factors import VWAP
56			from zipline.pipeline.data import USEquityPricing
57			from zipline.pipeline.loaders.frame import DataFrameLoader
58			from zipline.pipeline.loaders.equity_pricing_loader import (
59			USEquityPricingLoader,
60			)
61			from zipline.utils.test_utils import (
62			make_simple_equity_info,
63			str_to_seconds,
64			DailyBarWriterFromDataFrames, FakeDataPortal)
65			from zipline.utils.tradingcalendar import (
66			trading_day,
67			trading_days,
68			)
69
70
71			TEST_RESOURCE_PATH = join(
72			dirname(dirname(realpath(__file__))), # zipline_repo/tests
73			'resources',
74			'pipeline_inputs',
75			)
76
77
78			def rolling_vwap(df, length):
79			"Simple rolling vwap implementation for testing"
80			closes = df['close'].values
81			volumes = df['volume'].values
82			product = closes * volumes
83			out = full_like(closes, nan)
84			for upper_bound in range(length, len(closes) + 1):
85			bounds = slice(upper_bound - length, upper_bound)
86			out[upper_bound - 1] = product[bounds].sum() / volumes[bounds].sum()
87
88			return Series(out, index=df.index)
89
90
91			class ClosesOnly(TestCase):
92
93			@classmethod
94			def setUpClass(cls):
95			cls.tempdir = TempDirectory()
96
97			@classmethod
98			def tearDownClass(cls):
99			cls.tempdir.cleanup()
100
101			def setUp(self):
102			self.env = env = trading.TradingEnvironment()
103			self.dates = date_range(
104			'2014-01-01', '2014-02-01', freq=trading_day, tz='UTC'
105			)
106			asset_info = DataFrame.from_records([
107			{
108			'sid': 1,
109			'symbol': 'A',
110			'start_date': self.dates[10],
111			'end_date': self.dates[13],
112			'exchange': 'TEST',
113			},
114			{
115			'sid': 2,
116			'symbol': 'B',
117			'start_date': self.dates[11],
118			'end_date': self.dates[14],
119			'exchange': 'TEST',
120			},
121			{
122			'sid': 3,
123			'symbol': 'C',
124			'start_date': self.dates[12],
125			'end_date': self.dates[15],
126			'exchange': 'TEST',
127			},
128			])
129			self.first_asset_start = min(asset_info.start_date)
130			self.last_asset_end = max(asset_info.end_date)
131			env.write_data(equities_df=asset_info)
132			self.asset_finder = finder = env.asset_finder
133
134			sids = (1, 2, 3)
135			self.assets = finder.retrieve_all(sids)
136
137			# View of the baseline data.
138			self.closes = DataFrame(
139			{sid: arange(1, len(self.dates) + 1) * sid for sid in sids},
140			index=self.dates,
141			dtype=float,
142			)
143
144			# Create a data portal holding the data in self.closes
145			data = {}
146			for sid in sids:
147			data[sid] = DataFrame({
148			"open": self.closes[sid].values,
149			"high": self.closes[sid].values,
150			"low": self.closes[sid].values,
151			"close": self.closes[sid].values,
152			"volume": self.closes[sid].values,
153			"day": [day.value for day in self.dates]
154			})
155
156			path = os.path.join(self.tempdir.path, "testdaily.bcolz")
157
158			DailyBarWriterFromDataFrames(data).write(
159			path,
160			self.dates,
161			data
162			)
163
164			daily_bar_reader = BcolzDailyBarReader(path)
165
166			self.data_portal = DataPortal(
167			self.env,
168			equity_daily_reader=daily_bar_reader,
169			)
170
171			# Add a split for 'A' on its second date.
172			self.split_asset = self.assets[0]
173			self.split_date = self.split_asset.start_date + trading_day
174			self.split_ratio = 0.5
175			self.adjustments = DataFrame.from_records([
176			{
177			'sid': self.split_asset.sid,
178			'value': self.split_ratio,
179			'kind': MULTIPLY,
180			'start_date': Timestamp('NaT'),
181			'end_date': self.split_date,
182			'apply_date': self.split_date,
183			}
184			])
185
186			# View of the data on/after the split.
187			self.adj_closes = adj_closes = self.closes.copy()
188			adj_closes.ix[:self.split_date, self.split_asset] *= self.split_ratio
189
190			self.pipeline_loader = DataFrameLoader(
191			column=USEquityPricing.close,
192			baseline=self.closes,
193			adjustments=self.adjustments,
194			)
195
196			def expected_close(self, date, asset):
197			if date < self.split_date:
198			lookup = self.closes
199			else:
200			lookup = self.adj_closes
201			return lookup.loc[date, asset]
202
203			def exists(self, date, asset):
204			return asset.start_date <= date <= asset.end_date
205
206			def test_attach_pipeline_after_initialize(self):
207			"""
208			Assert that calling attach_pipeline after initialize raises correctly.
209			"""
210			def initialize(context):
211			pass
212
213			def late_attach(context, data):
214			attach_pipeline(Pipeline(), 'test')
215			raise AssertionError("Shouldn't make it past attach_pipeline!")
216
217			algo = TradingAlgorithm(
218			initialize=initialize,
219			handle_data=late_attach,
220			data_frequency='daily',
221			get_pipeline_loader=lambda column: self.pipeline_loader,
222			start=self.first_asset_start - trading_day,
223			end=self.last_asset_end + trading_day,
224			env=self.env,
225			)
226
227			with self.assertRaises(AttachPipelineAfterInitialize):
228			algo.run(data_portal=self.data_portal)
229
230			def barf(context, data):
231			raise AssertionError("Shouldn't make it past before_trading_start")
232
233			algo = TradingAlgorithm(
234			initialize=initialize,
235			before_trading_start=late_attach,
236			handle_data=barf,
237			data_frequency='daily',
238			get_pipeline_loader=lambda column: self.pipeline_loader,
239			start=self.first_asset_start - trading_day,
240			end=self.last_asset_end + trading_day,
241			env=self.env,
242			)
243
244			with self.assertRaises(AttachPipelineAfterInitialize):
245			algo.run(data_portal=self.data_portal)
246
247			def test_pipeline_output_after_initialize(self):
248			"""
249			Assert that calling pipeline_output after initialize raises correctly.
250			"""
251			def initialize(context):
252			attach_pipeline(Pipeline(), 'test')
253			pipeline_output('test')
254			raise AssertionError("Shouldn't make it past pipeline_output()")
255
256			def handle_data(context, data):
257			raise AssertionError("Shouldn't make it past initialize!")
258
259			def before_trading_start(context, data):
260			raise AssertionError("Shouldn't make it past initialize!")
261
262			algo = TradingAlgorithm(
263			initialize=initialize,
264			handle_data=handle_data,
265			before_trading_start=before_trading_start,
266			data_frequency='daily',
267			get_pipeline_loader=lambda column: self.pipeline_loader,
268			start=self.first_asset_start - trading_day,
269			end=self.last_asset_end + trading_day,
270			env=self.env,
271			)
272
273			with self.assertRaises(PipelineOutputDuringInitialize):
274			algo.run(data_portal=self.data_portal)
275
276			def test_get_output_nonexistent_pipeline(self):
277			"""
278			Assert that calling add_pipeline after initialize raises appropriately.
279			"""
280			def initialize(context):
281			attach_pipeline(Pipeline(), 'test')
282
283			def handle_data(context, data):
284			raise AssertionError("Shouldn't make it past before_trading_start")
285
286			def before_trading_start(context, data):
287			pipeline_output('not_test')
288			raise AssertionError("Shouldn't make it past pipeline_output!")
289
290			algo = TradingAlgorithm(
291			initialize=initialize,
292			handle_data=handle_data,
293			before_trading_start=before_trading_start,
294			data_frequency='daily',
295			get_pipeline_loader=lambda column: self.pipeline_loader,
296			start=self.first_asset_start - trading_day,
297			end=self.last_asset_end + trading_day,
298			env=self.env,
299			)
300
301			with self.assertRaises(NoSuchPipeline):
302			algo.run(data_portal=self.data_portal)
303
304			@parameterized.expand([('default', None),
305			('day', 1),
306			('week', 5),
307			('year', 252),
308			('all_but_one_day', 'all_but_one_day')])
309			def test_assets_appear_on_correct_days(self, test_name, chunksize):
310			"""
311			Assert that assets appear at correct times during a backtest, with
312			correctly-adjusted close price values.
313			"""
314
315			if chunksize == 'all_but_one_day':
316			chunksize = (
317			self.dates.get_loc(self.last_asset_end) -
318			self.dates.get_loc(self.first_asset_start)
319			) - 1
320
321			def initialize(context):
322			p = attach_pipeline(Pipeline(), 'test', chunksize=chunksize)
323			p.add(USEquityPricing.close.latest, 'close')
324
325			def handle_data(context, data):
326			results = pipeline_output('test')
327			date = get_datetime().normalize()
328			for asset in self.assets:
329			# Assets should appear iff they exist today and yesterday.
330			exists_today = self.exists(date, asset)
331			existed_yesterday = self.exists(date - trading_day, asset)
332			if exists_today and existed_yesterday:
333			latest = results.loc[asset, 'close']
334			self.assertEqual(latest, self.expected_close(date, asset))
335			else:
336			self.assertNotIn(asset, results.index)
337
338			before_trading_start = handle_data
339
340			algo = TradingAlgorithm(
341			initialize=initialize,
342			handle_data=handle_data,
343			before_trading_start=before_trading_start,
344			data_frequency='daily',
345			get_pipeline_loader=lambda column: self.pipeline_loader,
346			start=self.first_asset_start,
347			end=self.last_asset_end,
348			env=self.env,
349			)
350
351			# Run for a week in the middle of our data.
352			algo.run(data_portal=self.data_portal)
353
354
355			class MockDailyBarSpotReader(object):
356			"""
357			A BcolzDailyBarReader which returns a constant value for spot price.
358			"""
359			def spot_price(self, sid, day, column):
360			return 100.0
361
362
363			class PipelineAlgorithmTestCase(TestCase):
364
365			@classmethod
366			def setUpClass(cls):
367			cls.AAPL = 1
368			cls.MSFT = 2
369			cls.BRK_A = 3
370			cls.assets = [cls.AAPL, cls.MSFT, cls.BRK_A]
371			asset_info = make_simple_equity_info(
372			cls.assets,
373			Timestamp('2014'),
374			Timestamp('2015'),
375			['AAPL', 'MSFT', 'BRK_A'],
376			)
377			cls.env = trading.TradingEnvironment()
378			cls.env.write_data(equities_df=asset_info)
379			cls.tempdir = tempdir = TempDirectory()
380			tempdir.create()
381			try:
382			cls.raw_data, cls.bar_reader = cls.create_bar_reader(tempdir)
383			cls.adj_reader = cls.create_adjustment_reader(tempdir)
384			cls.pipeline_loader = USEquityPricingLoader(
385			cls.bar_reader, cls.adj_reader
386			)
387			except:
388			cls.tempdir.cleanup()
389			raise
390
391			cls.dates = cls.raw_data[cls.AAPL].index.tz_localize('UTC')
392			cls.AAPL_split_date = Timestamp("2014-06-09", tz='UTC')
393
394			@classmethod
395			def tearDownClass(cls):
396			del cls.env
397			cls.tempdir.cleanup()
398
399			@classmethod
400			def create_bar_reader(cls, tempdir):
401			resources = {
402			cls.AAPL: join(TEST_RESOURCE_PATH, 'AAPL.csv'),
403			cls.MSFT: join(TEST_RESOURCE_PATH, 'MSFT.csv'),
404			cls.BRK_A: join(TEST_RESOURCE_PATH, 'BRK-A.csv'),
405			}
406			raw_data = {
407			asset: read_csv(path, parse_dates=['day']).set_index('day')
408			for asset, path in iteritems(resources)
409			}
410			# Add 'price' column as an alias because all kinds of stuff in zipline
411			# depends on it being present. :/
412			for frame in raw_data.values():
413			frame['price'] = frame['close']
414
415			writer = DailyBarWriterFromCSVs(resources)
416			data_path = tempdir.getpath('testdata.bcolz')
417			table = writer.write(data_path, trading_days, cls.assets)
418			return raw_data, BcolzDailyBarReader(table)
419
420			@classmethod
421			def create_adjustment_reader(cls, tempdir):
422			dbpath = tempdir.getpath('adjustments.sqlite')
423			writer = SQLiteAdjustmentWriter(dbpath, cls.env.trading_days,
424			MockDailyBarSpotReader())
425			splits = DataFrame.from_records([
426			{
427			'effective_date': str_to_seconds('2014-06-09'),
428			'ratio': (1 / 7.0),
429			'sid': cls.AAPL,
430			}
431			])
432			mergers = DataFrame(
433			{
434			# Hackery to make the dtypes correct on an empty frame.
435			'effective_date': array([], dtype=int),
436			'ratio': array([], dtype=float),
437			'sid': array([], dtype=int),
438			},
439			index=DatetimeIndex([], tz='UTC'),
440			columns=['effective_date', 'ratio', 'sid'],
441			)
442			dividends = DataFrame({
443			'sid': array([], dtype=uint32),
444			'amount': array([], dtype=float64),
445			'record_date': array([], dtype='datetime64[ns]'),
446			'ex_date': array([], dtype='datetime64[ns]'),
447			'declared_date': array([], dtype='datetime64[ns]'),
448			'pay_date': array([], dtype='datetime64[ns]'),
449			})
450			writer.write(splits, mergers, dividends)
451			return SQLiteAdjustmentReader(dbpath)
452
453			def compute_expected_vwaps(self, window_lengths):
454			AAPL, MSFT, BRK_A = self.AAPL, self.MSFT, self.BRK_A
455
456			# Our view of the data before AAPL's split on June 9, 2014.
457			raw = {k: v.copy() for k, v in iteritems(self.raw_data)}
458
459			split_date = self.AAPL_split_date
460			split_loc = self.dates.get_loc(split_date)
461			split_ratio = 7.0
462
463			# Our view of the data after AAPL's split. All prices from before June
464			# 9 get divided by the split ratio, and volumes get multiplied by the
465			# split ratio.
466			adj = {k: v.copy() for k, v in iteritems(self.raw_data)}
467			for column in 'open', 'high', 'low', 'close':
468			adj[AAPL].ix[:split_loc, column] /= split_ratio
469			adj[AAPL].ix[:split_loc, 'volume'] *= split_ratio
470
471			# length -> asset -> expected vwap
472			vwaps = {length: {} for length in window_lengths}
473			for length in window_lengths:
474			for asset in AAPL, MSFT, BRK_A:
475			raw_vwap = rolling_vwap(raw[asset], length)
476			adj_vwap = rolling_vwap(adj[asset], length)
477			# Shift computed results one day forward so that they're
478			# labelled by the date on which they'll be seen in the
479			# algorithm. (We can't show the close price for day N until day
480			# N + 1.)
481			vwaps[length][asset] = concat(
482			[
483			raw_vwap[:split_loc - 1],
484			adj_vwap[split_loc - 1:]
485			]
486			).shift(1, trading_day)
487
488			# Make sure all the expected vwaps have the same dates.
489			vwap_dates = vwaps[1][self.AAPL].index
490			for dict_ in itervalues(vwaps):
491			# Each value is a dict mapping sid -> expected series.
492			for series in itervalues(dict_):
493			self.assertTrue((vwap_dates == series.index).all())
494
495			# Spot check expectations near the AAPL split.
496			# length 1 vwap for the morning before the split should be the close
497			# price of the previous day.
498			before_split = vwaps[1][AAPL].loc[split_date - trading_day]
499			assert_almost_equal(before_split, 647.3499, decimal=2)
500			assert_almost_equal(
501			before_split,
502			raw[AAPL].loc[split_date - (2 * trading_day), 'close'],
503			decimal=2,
504			)
505
506			# length 1 vwap for the morning of the split should be the close price
507			# of the previous day, ADJUSTED FOR THE SPLIT.
508			on_split = vwaps[1][AAPL].loc[split_date]
509			assert_almost_equal(on_split, 645.5700 / split_ratio, decimal=2)
510			assert_almost_equal(
511			on_split,
512			raw[AAPL].loc[split_date - trading_day, 'close'] / split_ratio,
513			decimal=2,
514			)
515
516			# length 1 vwap on the day after the split should be the as-traded
517			# close on the split day.
518			after_split = vwaps[1][AAPL].loc[split_date + trading_day]
519			assert_almost_equal(after_split, 93.69999, decimal=2)
520			assert_almost_equal(
521			after_split,
522			raw[AAPL].loc[split_date, 'close'],
523			decimal=2,
524			)
525
526			return vwaps
527
528			@parameterized.expand([
529			(True,),
530			(False,),
531			])
532			def test_handle_adjustment(self, set_screen):
533			AAPL, MSFT, BRK_A = assets = self.AAPL, self.MSFT, self.BRK_A
534
535			window_lengths = [1, 2, 5, 10]
536			vwaps = self.compute_expected_vwaps(window_lengths)
537
538			def vwap_key(length):
539			return "vwap_%d" % length
540
541			def initialize(context):
542			pipeline = Pipeline()
543			context.vwaps = []
544			for length in vwaps:
545			name = vwap_key(length)
546			factor = VWAP(window_length=length)
547			context.vwaps.append(factor)
548			pipeline.add(factor, name=name)
549
550			filter_ = (USEquityPricing.close.latest > 300)
551			pipeline.add(filter_, 'filter')
552			if set_screen:
553			pipeline.set_screen(filter_)
554
555			attach_pipeline(pipeline, 'test')
556
557			def handle_data(context, data):
558			today = get_datetime()
559			results = pipeline_output('test')
560			expect_over_300 = {
561			AAPL: today < self.AAPL_split_date,
562			MSFT: False,
563			BRK_A: True,
564			}
565			for asset in assets:
566			should_pass_filter = expect_over_300[asset]
567			if set_screen and not should_pass_filter:
568			self.assertNotIn(asset, results.index)
569			continue
570
571			asset_results = results.loc[asset]
572			self.assertEqual(asset_results['filter'], should_pass_filter)
573			for length in vwaps:
574			computed = results.loc[asset, vwap_key(length)]
575			expected = vwaps[length][asset].loc[today]
576			# Only having two places of precision here is a bit
577			# unfortunate.
578			assert_almost_equal(computed, expected, decimal=2)
579
580			# Do the same checks in before_trading_start
581			before_trading_start = handle_data
582
583			algo = TradingAlgorithm(
584			initialize=initialize,
585			handle_data=handle_data,
586			before_trading_start=before_trading_start,
587			data_frequency='daily',
588			get_pipeline_loader=lambda column: self.pipeline_loader,
589			start=self.dates[max(window_lengths)],
590			end=self.dates[-1],
591			env=self.env,
592			)
593
594			algo.run(data_portal=FakeDataPortal())
595

quantopian / zipline

Pull Request — master (#858)

tests.pipeline.ClosesOnly.before_trading_start() A

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