def test_read_no_adjustments(self):
adjustment_reader = NullAdjustmentReader()
columns = [USEquityPricing.close, USEquityPricing.volume]
query_days = self.calendar_days_between(TEST_QUERY_START, TEST_QUERY_STOP)
# Our expected results for each day are based on values from the
# previous day.
shifted_query_days = self.calendar_days_between(TEST_QUERY_START, TEST_QUERY_STOP, shift=-1)
adjustments = adjustment_reader.load_adjustments(columns, query_days, self.assets)
self.assertEqual(adjustments, [{}, {}])
baseline_reader = BcolzDailyBarReader(self.bcolz_path)
pricing_loader = USEquityPricingLoader(baseline_reader, adjustment_reader)
results = pricing_loader.load_adjusted_array(
columns, dates=query_days, assets=self.assets, mask=ones((len(query_days), len(self.assets)), dtype=bool)
)
closes, volumes = map(getitem(results), columns)
expected_baseline_closes = self.bcolz_writer.expected_values_2d(shifted_query_days, self.assets, "close")
expected_baseline_volumes = self.bcolz_writer.expected_values_2d(shifted_query_days, self.assets, "volume")
# AdjustedArrays should yield the same data as the expected baseline.
for windowlen in range(1, len(query_days) + 1):
for offset, window in enumerate(closes.traverse(windowlen)):
assert_array_equal(expected_baseline_closes[offset : offset + windowlen], window)
for offset, window in enumerate(volumes.traverse(windowlen)):
assert_array_equal(expected_baseline_volumes[offset : offset + windowlen], window)
# Verify that we checked up to the longest possible window.
with self.assertRaises(WindowLengthTooLong):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
def init_class_fixtures(cls):
(super(WithInternationalPricingPipelineEngine,
cls).init_class_fixtures())
adjustments = NullAdjustmentReader()
cls.loaders = {
GB_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers["XLON"],
adjustments,
cls.in_memory_fx_rate_reader,
),
US_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers["XNYS"],
adjustments,
cls.in_memory_fx_rate_reader,
),
CA_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers["XTSE"],
adjustments,
cls.in_memory_fx_rate_reader,
),
}
cls.engine = SimplePipelineEngine(
get_loader=cls.get_loader,
asset_finder=cls.asset_finder,
)
def init_class_fixtures(cls):
(super().init_class_fixtures())
adjustments = NullAdjustmentReader()
cls.loaders = {
GB_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers['XLON'],
adjustments,
cls.in_memory_fx_rate_reader,
),
US_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers['XNYS'],
adjustments,
cls.in_memory_fx_rate_reader,
),
CA_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers['XTSE'],
adjustments,
cls.in_memory_fx_rate_reader,
)
}
cls.engine = SimplePipelineEngine(
get_loader=cls.get_loader,
asset_finder=cls.asset_finder,
)
def init_class_fixtures(cls):
(super(WithInternationalPricingPipelineEngine,
cls).init_class_fixtures())
adjustments = NullAdjustmentReader()
cls.loaders = {
GB_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers['LSE'],
adjustments,
),
US_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers['NYSE'],
adjustments,
),
CA_EQUITIES:
EquityPricingLoader(
cls.daily_bar_readers['TSX'],
adjustments,
)
}
cls.engine = SimplePipelineEngine(
get_loader=cls.get_loader,
asset_finder=cls.asset_finder,
)
def setUpClass(cls):
cls.first_asset_start = Timestamp('2015-04-01', tz='UTC')
cls.env = TradingEnvironment()
cls.trading_day = day = cls.env.trading_day
cls.calendar = date_range('2015', '2015-08', tz='UTC', freq=day)
cls.asset_info = make_rotating_asset_info(
num_assets=6,
first_start=cls.first_asset_start,
frequency=day,
periods_between_starts=4,
asset_lifetime=8,
)
cls.last_asset_end = cls.asset_info['end_date'].max()
cls.all_assets = cls.asset_info.index
cls.env.write_data(equities_df=cls.asset_info)
cls.finder = cls.env.asset_finder
cls.temp_dir = TempDirectory()
cls.temp_dir.create()
try:
cls.writer = SyntheticDailyBarWriter(
asset_info=cls.asset_info[['start_date', 'end_date']],
calendar=cls.calendar,
)
table = cls.writer.write(
cls.temp_dir.getpath('testdata.bcolz'),
cls.calendar,
cls.all_assets,
)
cls.pipeline_loader = USEquityPricingLoader(
BcolzDailyBarReader(table),
NullAdjustmentReader(),
)
except:
cls.temp_dir.cleanup()
raise
def test_read_no_adjustments(self):
adjustment_reader = NullAdjustmentReader()
columns = [USEquityPricing.close, USEquityPricing.volume]
query_days = self.calendar_days_between(TEST_QUERY_START,
TEST_QUERY_STOP)
# Our expected results for each day are based on values from the
# previous day.
shifted_query_days = self.calendar_days_between(
TEST_QUERY_START,
TEST_QUERY_STOP,
shift=-1,
)
adjustments = adjustment_reader.load_adjustments(
[c.name for c in columns],
query_days,
self.assets,
)
self.assertEqual(adjustments, [{}, {}])
pricing_loader = USEquityPricingLoader(
self.bcolz_equity_daily_bar_reader,
adjustment_reader,
)
results = pricing_loader.load_adjusted_array(
columns,
dates=query_days,
assets=self.assets,
mask=ones((len(query_days), len(self.assets)), dtype=bool),
)
closes, volumes = map(getitem(results), columns)
expected_baseline_closes = expected_bar_values_2d(
shifted_query_days,
self.asset_info,
'close',
)
expected_baseline_volumes = expected_bar_values_2d(
shifted_query_days,
self.asset_info,
'volume',
)
# AdjustedArrays should yield the same data as the expected baseline.
for windowlen in range(1, len(query_days) + 1):
for offset, window in enumerate(closes.traverse(windowlen)):
assert_array_equal(
expected_baseline_closes[offset:offset + windowlen],
window,
)
for offset, window in enumerate(volumes.traverse(windowlen)):
assert_array_equal(
expected_baseline_volumes[offset:offset + windowlen],
window,
)
# Verify that we checked up to the longest possible window.
with self.assertRaises(WindowLengthTooLong):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
def test_read_no_adjustments(self):
adjustment_reader = NullAdjustmentReader()
columns = [USEquityPricing.close, USEquityPricing.volume]
query_days = self.calendar_days_between(
TEST_QUERY_START,
TEST_QUERY_STOP
)
# Our expected results for each day are based on values from the
# previous day.
shifted_query_days = self.calendar_days_between(
TEST_QUERY_START,
TEST_QUERY_STOP,
shift=-1,
)
adjustments = adjustment_reader.load_pricing_adjustments(
[c.name for c in columns],
query_days,
self.sids,
)
self.assertEqual(adjustments, [{}, {}])
pricing_loader = USEquityPricingLoader(
self.bcolz_equity_daily_bar_reader,
adjustment_reader,
)
results = pricing_loader.load_adjusted_array(
domain=US_EQUITIES,
columns=columns,
dates=query_days,
sids=self.sids,
mask=ones((len(query_days), len(self.sids)), dtype=bool),
)
closes, volumes = map(getitem(results), columns)
expected_baseline_closes = expected_bar_values_2d(
shifted_query_days,
self.sids,
self.asset_info,
'close',
)
expected_baseline_volumes = expected_bar_values_2d(
shifted_query_days,
self.sids,
self.asset_info,
'volume',
)
# AdjustedArrays should yield the same data as the expected baseline.
for windowlen in range(1, len(query_days) + 1):
for offset, window in enumerate(closes.traverse(windowlen)):
assert_array_equal(
expected_baseline_closes[offset:offset + windowlen],
window,
)
for offset, window in enumerate(volumes.traverse(windowlen)):
assert_array_equal(
expected_baseline_volumes[offset:offset + windowlen],
window,
)
# Verify that we checked up to the longest possible window.
with self.assertRaises(WindowLengthTooLong):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
