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zipline/tests/pipeline/test_engine.py
samatix 86ddd6072f DEP: Replacing the assertRaisesRegexp which is deprecated by assertRaisesRegex 
This deprecation will enable the upgrade to add support for python 3.7 and higher
2020-01-19 10:46:31 +01:00

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"""
Tests for SimplePipelineEngine
"""
from __future__ import division
from collections import OrderedDict
from itertools import product
from operator import add, sub
from unittest import skipIf
from nose_parameterized import parameterized
import numpy as np
from numpy import (
arange,
array,
concatenate,
float32,
float64,
full,
full_like,
log,
nan,
tile,
where,
zeros,
)
from numpy.testing import assert_almost_equal
from pandas import (
Categorical,
DataFrame,
date_range,
Int64Index,
MultiIndex,
Series,
Timestamp,
)
from pandas.compat.chainmap import ChainMap
from pandas.util.testing import assert_frame_equal
from six import iteritems, itervalues
from toolz import merge
from zipline.assets.synthetic import make_rotating_equity_info
from zipline.errors import NoFurtherDataError
from zipline.lib.adjustment import MULTIPLY
from zipline.lib.labelarray import LabelArray
from zipline.pipeline import CustomFactor, Pipeline
from zipline.pipeline.data import (
Column, DataSet, EquityPricing, USEquityPricing,
)
from zipline.pipeline.data.testing import TestingDataSet
from zipline.pipeline.domain import (
EquitySessionDomain,
GENERIC,
JP_EQUITIES,
US_EQUITIES,
)
from zipline.pipeline.engine import SimplePipelineEngine
from zipline.pipeline.factors import (
AverageDollarVolume,
EWMA,
EWMSTD,
ExponentialWeightedMovingAverage,
ExponentialWeightedMovingStdDev,
MaxDrawdown,
SimpleMovingAverage,
)
from zipline.pipeline.filters import CustomFilter
from zipline.pipeline.loaders.equity_pricing_loader import (
EquityPricingLoader,
)
from zipline.pipeline.loaders.frame import DataFrameLoader
from zipline.pipeline.loaders.synthetic import (
PrecomputedLoader,
make_bar_data,
expected_bar_values_2d,
)
from zipline.pipeline.sentinels import NotSpecified
from zipline.pipeline.term import InputDates
from zipline.testing import (
AssetID,
AssetIDPlusDay,
check_arrays,
make_alternating_boolean_array,
make_cascading_boolean_array,
OpenPrice,
parameter_space,
product_upper_triangle,
)
import zipline.testing.fixtures as zf
from zipline.utils.exploding_object import NamedExplodingObject
from zipline.testing.core import create_simple_domain
from zipline.testing.predicates import assert_equal
from zipline.utils.memoize import lazyval
from zipline.utils.numpy_utils import bool_dtype, datetime64ns_dtype
from zipline.utils.pandas_utils import new_pandas, skip_pipeline_new_pandas
class RollingSumDifference(CustomFactor):
window_length = 3
inputs = [EquityPricing.open, EquityPricing.close]
def compute(self, today, assets, out, open, close):
out[:] = (open - close).sum(axis=0)
class MultipleOutputs(CustomFactor):
window_length = 1
inputs = [EquityPricing.open, EquityPricing.close]
outputs = ['open', 'close']
def compute(self, today, assets, out, open, close):
out.open[:] = open
out.close[:] = close
class OpenCloseSumAndDiff(CustomFactor):
"""
Used for testing a CustomFactor with multiple outputs operating over a non-
trivial window length.
"""
inputs = [EquityPricing.open, EquityPricing.close]
def compute(self, today, assets, out, open, close):
out.sum_[:] = open.sum(axis=0) + close.sum(axis=0)
out.diff[:] = open.sum(axis=0) - close.sum(axis=0)
def assert_multi_index_is_product(testcase, index, *levels):
"""Assert that a MultiIndex contains the product of `*levels`."""
testcase.assertIsInstance(
index, MultiIndex, "%s is not a MultiIndex" % index
)
testcase.assertEqual(set(index), set(product(*levels)))
class ColumnArgs(tuple):
"""A tuple of Columns that defines equivalence based on the order of the
columns' DataSets, instead of the columns themselves. This is used when
comparing the columns passed to a loader's load_adjusted_array method,
since we want to assert that they are ordered by DataSet.
"""
def __new__(cls, *cols):
return super(ColumnArgs, cls).__new__(cls, cols)
@classmethod
def sorted_by_ds(cls, *cols):
return cls(*sorted(cols, key=lambda col: col.dataset))
def by_ds(self):
return tuple(col.dataset for col in self)
def __eq__(self, other):
return set(self) == set(other) and self.by_ds() == other.by_ds()
def __hash__(self):
return hash(frozenset(self))
class RecordingPrecomputedLoader(PrecomputedLoader):
def __init__(self, *args, **kwargs):
super(RecordingPrecomputedLoader, self).__init__(*args, **kwargs)
self.load_calls = []
def load_adjusted_array(self, domain, columns, dates, sids, mask):
self.load_calls.append(ColumnArgs(*columns))
return super(RecordingPrecomputedLoader, self).load_adjusted_array(
domain, columns, dates, sids, mask,
)
class RollingSumSum(CustomFactor):
def compute(self, today, assets, out, *inputs):
assert len(self.inputs) == len(inputs)
out[:] = sum(inputs).sum(axis=0)
class WithConstantInputs(zf.WithAssetFinder):
asset_ids = ASSET_FINDER_EQUITY_SIDS = 1, 2, 3, 4
START_DATE = Timestamp('2014-01-01', tz='utc')
END_DATE = Timestamp('2014-03-01', tz='utc')
ASSET_FINDER_COUNTRY_CODE = 'US'
@classmethod
def init_class_fixtures(cls):
super(WithConstantInputs, cls).init_class_fixtures()
cls.domain = create_simple_domain(
start=cls.START_DATE,
end=cls.END_DATE,
country_code=cls.ASSET_FINDER_COUNTRY_CODE,
)
cls.constants = {
# Every day, assume every stock starts at 2, goes down to 1,
# goes up to 4, and finishes at 3.
EquityPricing.low: 1,
EquityPricing.open: 2,
EquityPricing.close: 3,
EquityPricing.high: 4,
}
cls.dates = date_range(
cls.START_DATE,
cls.END_DATE,
freq='D',
tz='UTC',
)
cls.loader = PrecomputedLoader(
constants=cls.constants,
dates=cls.dates,
sids=cls.asset_ids,
)
cls.assets = cls.asset_finder.retrieve_all(cls.asset_ids)
cls.engine = SimplePipelineEngine(
lambda c: cls.loader,
cls.asset_finder,
default_domain=cls.domain
)
class ConstantInputTestCase(WithConstantInputs,
zf.WithAssetFinder,
zf.WithTradingCalendars,
zf.ZiplineTestCase):
def test_bad_dates(self):
p = Pipeline()
msg = "start_date must be before or equal to end_date .*"
with self.assertRaisesRegex(ValueError, msg):
self.engine.run_pipeline(p, self.dates[2], self.dates[1])
def test_fail_usefully_on_insufficient_data(self):
class SomeFactor(CustomFactor):
inputs = [EquityPricing.close]
window_length = 10
def compute(self, today, assets, out, closes):
pass
p = Pipeline(columns={'t': SomeFactor()})
# self.dates[9] is the earliest date we should be able to compute.
self.engine.run_pipeline(p, self.dates[9], self.dates[9])
# We shouldn't be able to compute dates[8], since we only know about 8
# prior dates, and we need a window length of 10.
with self.assertRaises(NoFurtherDataError):
self.engine.run_pipeline(p, self.dates[8], self.dates[8])
def test_input_dates_provided_by_default(self):
class TestFactor(CustomFactor):
inputs = [InputDates(), EquityPricing.close]
window_length = 10
dtype = datetime64ns_dtype
def compute(self, today, assets, out, dates, closes):
first, last = dates[[0, -1], 0]
assert last == today.asm8
assert len(dates) == len(closes) == self.window_length
out[:] = first
p = Pipeline(columns={'t': TestFactor()})
results = self.engine.run_pipeline(p, self.dates[9], self.dates[10])
# All results are the same, so just grab one column.
column = results.unstack().iloc[:, 0].values
check_arrays(column, self.dates[:2].values)
def test_same_day_pipeline(self):
factor = AssetID()
asset = self.asset_ids[0]
p = Pipeline(columns={'f': factor}, screen=factor <= asset)
# The crux of this is that when we run the pipeline for a single day
# (i.e. start and end dates are the same) we should accurately get
# data for the day prior.
result = self.engine.run_pipeline(p, self.dates[1], self.dates[1])
self.assertEqual(result['f'][0], 1.0)
def test_screen(self):
asset_ids = array(self.asset_ids)
num_dates = 5
dates = self.dates[10:10 + num_dates]
factor = AssetID()
for asset_id in asset_ids:
p = Pipeline(columns={'f': factor}, screen=factor <= asset_id)
result = self.engine.run_pipeline(p, dates[0], dates[-1])
expected_sids = asset_ids[asset_ids <= asset_id]
expected_assets = self.asset_finder.retrieve_all(expected_sids)
expected_result = DataFrame(
index=MultiIndex.from_product([dates, expected_assets]),
data=tile(expected_sids.astype(float), [len(dates)]),
columns=['f'],
)
assert_frame_equal(result, expected_result)
def test_single_factor(self):
assets = self.assets
result_shape = (num_dates, num_assets) = (5, len(assets))
dates = self.dates[10:10 + num_dates]
factor = RollingSumDifference()
expected_result = -factor.window_length
# Since every asset will pass the screen, these should be equivalent.
pipelines = [
Pipeline(columns={'f': factor}),
Pipeline(
columns={'f': factor},
screen=factor.eq(expected_result),
),
]
for p in pipelines:
result = self.engine.run_pipeline(p, dates[0], dates[-1])
self.assertEqual(set(result.columns), {'f'})
assert_multi_index_is_product(
self, result.index, dates, assets
)
check_arrays(
result['f'].unstack().values,
full(result_shape, expected_result, dtype=float),
)
def test_multiple_rolling_factors(self):
assets = self.assets
shape = num_dates, num_assets = (5, len(assets))
dates = self.dates[10:10 + num_dates]
short_factor = RollingSumDifference(window_length=3)
long_factor = RollingSumDifference(window_length=5)
high_factor = RollingSumDifference(
window_length=3,
inputs=[EquityPricing.open, EquityPricing.high],
)
pipeline = Pipeline(
columns={
'short': short_factor,
'long': long_factor,
'high': high_factor,
}
)
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
self.assertEqual(set(results.columns), {'short', 'high', 'long'})
assert_multi_index_is_product(
self, results.index, dates, assets
)
# row-wise sum over an array whose values are all (1 - 2)
check_arrays(
results['short'].unstack().values,
full(shape, -short_factor.window_length, dtype=float),
)
check_arrays(
results['long'].unstack().values,
full(shape, -long_factor.window_length, dtype=float),
)
# row-wise sum over an array whose values are all (1 - 3)
check_arrays(
results['high'].unstack().values,
full(shape, -2 * high_factor.window_length, dtype=float),
)
def test_numeric_factor(self):
constants = self.constants
num_dates = 5
dates = self.dates[10:10 + num_dates]
high, low = EquityPricing.high, EquityPricing.low
open, close = EquityPricing.open, EquityPricing.close
high_minus_low = RollingSumDifference(inputs=[high, low])
open_minus_close = RollingSumDifference(inputs=[open, close])
avg = (high_minus_low + open_minus_close) / 2
results = self.engine.run_pipeline(
Pipeline(
columns={
'high_low': high_minus_low,
'open_close': open_minus_close,
'avg': avg,
},
),
dates[0],
dates[-1],
)
high_low_result = results['high_low'].unstack()
expected_high_low = 3.0 * (constants[high] - constants[low])
assert_frame_equal(
high_low_result,
DataFrame(expected_high_low, index=dates, columns=self.assets),
)
open_close_result = results['open_close'].unstack()
expected_open_close = 3.0 * (constants[open] - constants[close])
assert_frame_equal(
open_close_result,
DataFrame(expected_open_close, index=dates, columns=self.assets),
)
avg_result = results['avg'].unstack()
expected_avg = (expected_high_low + expected_open_close) / 2.0
assert_frame_equal(
avg_result,
DataFrame(expected_avg, index=dates, columns=self.assets),
)
def test_masked_factor(self):
"""
Test that a Custom Factor computes the correct values when passed a
mask. The mask/filter should be applied prior to computing any values,
as opposed to computing the factor across the entire universe of
assets. Any assets that are filtered out should be filled with missing
values.
"""
dates = self.dates[5:8]
assets = self.assets
asset_ids = self.asset_ids
constants = self.constants
num_dates = len(dates)
num_assets = len(assets)
open = EquityPricing.open
close = EquityPricing.close
factor1_value = constants[open]
factor2_value = 3.0 * (constants[open] - constants[close])
def create_expected_results(expected_value, mask):
expected_values = where(mask, expected_value, nan)
return DataFrame(expected_values, index=dates, columns=assets)
cascading_mask = AssetIDPlusDay() < (asset_ids[-1] + dates[0].day)
expected_cascading_mask_result = make_cascading_boolean_array(
shape=(num_dates, num_assets),
)
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
expected_alternating_mask_result = make_alternating_boolean_array(
shape=(num_dates, num_assets), first_value=False,
)
masks = cascading_mask, alternating_mask
expected_mask_results = (
expected_cascading_mask_result,
expected_alternating_mask_result,
)
for mask, expected_mask in zip(masks, expected_mask_results):
# Test running a pipeline with a single masked factor.
columns = {'factor1': OpenPrice(mask=mask), 'mask': mask}
pipeline = Pipeline(columns=columns)
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
mask_results = results['mask'].unstack()
check_arrays(mask_results.values, expected_mask)
factor1_results = results['factor1'].unstack()
factor1_expected = create_expected_results(factor1_value,
mask_results)
assert_frame_equal(factor1_results, factor1_expected)
# Test running a pipeline with a second factor. This ensures that
# adding another factor to the pipeline with a different window
# length does not cause any unexpected behavior, especially when
# both factors share the same mask.
columns['factor2'] = RollingSumDifference(mask=mask)
pipeline = Pipeline(columns=columns)
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
mask_results = results['mask'].unstack()
check_arrays(mask_results.values, expected_mask)
factor1_results = results['factor1'].unstack()
factor2_results = results['factor2'].unstack()
factor1_expected = create_expected_results(factor1_value,
mask_results)
factor2_expected = create_expected_results(factor2_value,
mask_results)
assert_frame_equal(factor1_results, factor1_expected)
assert_frame_equal(factor2_results, factor2_expected)
def test_rolling_and_nonrolling(self):
open_ = EquityPricing.open
close = EquityPricing.close
volume = EquityPricing.volume
# Test for thirty days up to the last day that we think all
# the assets existed.
dates_to_test = self.dates[-30:]
constants = {
open_: 1,
close: 2,
volume: 3,
}
loader = PrecomputedLoader(
constants=constants,
dates=self.dates,
sids=self.asset_ids,
)
engine = SimplePipelineEngine(lambda column: loader, self.asset_finder)
sumdiff = RollingSumDifference()
result = engine.run_pipeline(
Pipeline(
columns={
'sumdiff': sumdiff,
'open': open_.latest,
'close': close.latest,
'volume': volume.latest,
},
domain=self.domain,
),
dates_to_test[0],
dates_to_test[-1]
)
self.assertIsNotNone(result)
self.assertEqual(
{'sumdiff', 'open', 'close', 'volume'},
set(result.columns)
)
result_index = self.asset_ids * len(dates_to_test)
result_shape = (len(result_index),)
check_arrays(
result['sumdiff'],
Series(
index=result_index,
data=full(result_shape, -3, dtype=float),
),
)
for name, const in [('open', 1), ('close', 2), ('volume', 3)]:
check_arrays(
result[name],
Series(
index=result_index,
data=full(result_shape, const, dtype=float),
),
)
def test_factor_with_single_output(self):
"""
Test passing an `outputs` parameter of length 1 to a CustomFactor.
"""
dates = self.dates[5:10]
assets = self.assets
num_dates = len(dates)
open = EquityPricing.open
open_values = [self.constants[open]] * num_dates
open_values_as_tuple = [(self.constants[open],)] * num_dates
single_output = OpenPrice(outputs=['open'])
pipeline = Pipeline(
columns={
'open_instance': single_output,
'open_attribute': single_output.open,
},
)
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
# The instance `single_output` itself will compute a numpy.recarray
# when added as a column to our pipeline, so we expect its output
# values to be 1-tuples.
open_instance_expected = {
asset: open_values_as_tuple for asset in assets
}
open_attribute_expected = {asset: open_values for asset in assets}
for colname, expected_values in (
('open_instance', open_instance_expected),
('open_attribute', open_attribute_expected)):
column_results = results[colname].unstack()
expected_results = DataFrame(
expected_values, index=dates, columns=assets, dtype=float64,
)
assert_frame_equal(column_results, expected_results)
def test_factor_with_multiple_outputs(self):
dates = self.dates[5:10]
assets = self.assets
asset_ids = self.asset_ids
constants = self.constants
num_dates = len(dates)
num_assets = len(assets)
open = EquityPricing.open
close = EquityPricing.close
def create_expected_results(expected_value, mask):
expected_values = where(mask, expected_value, nan)
return DataFrame(expected_values, index=dates, columns=assets)
cascading_mask = AssetIDPlusDay() < (asset_ids[-1] + dates[0].day)
expected_cascading_mask_result = make_cascading_boolean_array(
shape=(num_dates, num_assets),
)
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
expected_alternating_mask_result = make_alternating_boolean_array(
shape=(num_dates, num_assets), first_value=False,
)
expected_no_mask_result = full(
shape=(num_dates, num_assets), fill_value=True, dtype=bool_dtype,
)
masks = cascading_mask, alternating_mask, NotSpecified
expected_mask_results = (
expected_cascading_mask_result,
expected_alternating_mask_result,
expected_no_mask_result,
)
for mask, expected_mask in zip(masks, expected_mask_results):
open_price, close_price = MultipleOutputs(mask=mask)
pipeline = Pipeline(
columns={'open_price': open_price, 'close_price': close_price},
)
if mask is not NotSpecified:
pipeline.add(mask, 'mask')
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
for colname, case_column in (('open_price', open),
('close_price', close)):
if mask is not NotSpecified:
mask_results = results['mask'].unstack()
check_arrays(mask_results.values, expected_mask)
output_results = results[colname].unstack()
output_expected = create_expected_results(
constants[case_column], expected_mask,
)
assert_frame_equal(output_results, output_expected)
def test_instance_of_factor_with_multiple_outputs(self):
"""
Test adding a CustomFactor instance, which has multiple outputs, as a
pipeline column directly. Its computed values should be tuples
containing the computed values of each of its outputs.
"""
dates = self.dates[5:10]
assets = self.assets
num_dates = len(dates)
num_assets = len(assets)
constants = self.constants
open_values = [constants[EquityPricing.open]] * num_assets
close_values = [constants[EquityPricing.close]] * num_assets
expected_values = [list(zip(open_values, close_values))] * num_dates
expected_results = DataFrame(
expected_values, index=dates, columns=assets, dtype=float64,
)
multiple_outputs = MultipleOutputs()
pipeline = Pipeline(columns={'instance': multiple_outputs})
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
instance_results = results['instance'].unstack()
assert_frame_equal(instance_results, expected_results)
def test_custom_factor_outputs_parameter(self):
dates = self.dates[5:10]
assets = self.assets
num_dates = len(dates)
num_assets = len(assets)
constants = self.constants
def create_expected_results(expected_value):
expected_values = full(
(num_dates, num_assets), expected_value, float64,
)
return DataFrame(expected_values, index=dates, columns=assets)
for window_length in range(1, 3):
sum_, diff = OpenCloseSumAndDiff(
outputs=['sum_', 'diff'], window_length=window_length,
)
pipeline = Pipeline(columns={'sum_': sum_, 'diff': diff})
results = self.engine.run_pipeline(pipeline, dates[0], dates[-1])
for colname, op in ('sum_', add), ('diff', sub):
output_results = results[colname].unstack()
output_expected = create_expected_results(
op(
constants[EquityPricing.open] * window_length,
constants[EquityPricing.close] * window_length,
)
)
assert_frame_equal(output_results, output_expected)
def test_loader_given_multiple_columns(self):
class Loader1DataSet1(DataSet):
col1 = Column(float)
col2 = Column(float32)
domain = self.domain
class Loader1DataSet2(DataSet):
col1 = Column(float32)
col2 = Column(float32)
domain = self.domain
class Loader2DataSet(DataSet):
col1 = Column(float32)
col2 = Column(float32)
domain = self.domain
constants1 = {Loader1DataSet1.col1: 1,
Loader1DataSet1.col2: 2,
Loader1DataSet2.col1: 3,
Loader1DataSet2.col2: 4}
loader1 = RecordingPrecomputedLoader(constants=constants1,
dates=self.dates,
sids=self.assets)
constants2 = {Loader2DataSet.col1: 5,
Loader2DataSet.col2: 6}
loader2 = RecordingPrecomputedLoader(constants=constants2,
dates=self.dates,
sids=self.assets)
engine = SimplePipelineEngine(
lambda column:
loader2 if column.dataset == Loader2DataSet else loader1,
self.asset_finder,
)
pipe_col1 = RollingSumSum(inputs=[Loader1DataSet1.col1,
Loader1DataSet2.col1,
Loader2DataSet.col1],
window_length=2)
pipe_col2 = RollingSumSum(inputs=[Loader1DataSet1.col2,
Loader1DataSet2.col2,
Loader2DataSet.col2],
window_length=3)
pipe_col3 = RollingSumSum(inputs=[Loader2DataSet.col1],
window_length=3)
columns = OrderedDict([
('pipe_col1', pipe_col1),
('pipe_col2', pipe_col2),
('pipe_col3', pipe_col3),
])
result = engine.run_pipeline(
Pipeline(columns=columns, domain=self.domain),
self.dates[2], # index is >= the largest window length - 1
self.dates[-1]
)
min_window = min(pip_col.window_length
for pip_col in itervalues(columns))
col_to_val = ChainMap(constants1, constants2)
vals = {name: (sum(col_to_val[col] for col in pipe_col.inputs)
* pipe_col.window_length)
for name, pipe_col in iteritems(columns)}
index = MultiIndex.from_product([self.dates[2:], self.assets])
def expected_for_col(col):
val = vals[col]
offset = columns[col].window_length - min_window
return concatenate(
[
full(offset * index.levshape[1], nan),
full(
(index.levshape[0] - offset) * index.levshape[1],
val,
float,
)
],
)
expected = DataFrame(
data={col: expected_for_col(col) for col in vals},
index=index,
columns=columns,
)
assert_frame_equal(result, expected)
self.assertEqual(set(loader1.load_calls),
{ColumnArgs.sorted_by_ds(Loader1DataSet1.col1,
Loader1DataSet2.col1),
ColumnArgs.sorted_by_ds(Loader1DataSet1.col2,
Loader1DataSet2.col2)})
self.assertEqual(set(loader2.load_calls),
{ColumnArgs.sorted_by_ds(Loader2DataSet.col1,
Loader2DataSet.col2)})
# Use very large sids that don't fit in that doesn't fit in an int32 as a
# regression test against bugs with 32 bit integer overflow in the adjustment
# reader.
HUGE_SID = np.iinfo('int32').max + 1
class FrameInputTestCase(zf.WithAssetFinder,
zf.WithTradingCalendars,
zf.ZiplineTestCase):
asset_ids = ASSET_FINDER_EQUITY_SIDS = range(HUGE_SID, HUGE_SID + 3)
start = START_DATE = Timestamp('2015-01-01', tz='utc')
end = END_DATE = Timestamp('2015-01-31', tz='utc')
ASSET_FINDER_COUNTRY_CODE = 'US'
@classmethod
def init_class_fixtures(cls):
super(FrameInputTestCase, cls).init_class_fixtures()
cls.dates = date_range(
cls.start,
cls.end,
freq=cls.trading_calendar.day,
tz='UTC',
)
cls.assets = cls.asset_finder.retrieve_all(cls.asset_ids)
cls.domain = US_EQUITIES
@lazyval
def base_mask(self):
return self.make_frame(True)
def make_frame(self, data):
return DataFrame(data, columns=self.assets, index=self.dates)
def test_compute_with_adjustments(self):
dates, asset_ids = self.dates, self.asset_ids
low, high = EquityPricing.low, EquityPricing.high
apply_idxs = [3, 10, 16]
def apply_date(idx, offset=0):
return dates[apply_idxs[idx] + offset]
adjustments = DataFrame.from_records(
[
dict(
kind=MULTIPLY,
sid=asset_ids[1],
value=2.0,
start_date=None,
end_date=apply_date(0, offset=-1),
apply_date=apply_date(0),
),
dict(
kind=MULTIPLY,
sid=asset_ids[1],
value=3.0,
start_date=None,
end_date=apply_date(1, offset=-1),
apply_date=apply_date(1),
),
dict(
kind=MULTIPLY,
sid=asset_ids[1],
value=5.0,
start_date=None,
end_date=apply_date(2, offset=-1),
apply_date=apply_date(2),
),
]
)
low_base = DataFrame(self.make_frame(30.0))
low_loader = DataFrameLoader(low, low_base.copy(), adjustments=None)
# Pre-apply inverse of adjustments to the baseline.
high_base = DataFrame(self.make_frame(30.0))
high_base.iloc[:apply_idxs[0], 1] /= 2.0
high_base.iloc[:apply_idxs[1], 1] /= 3.0
high_base.iloc[:apply_idxs[2], 1] /= 5.0
high_loader = DataFrameLoader(high, high_base, adjustments)
# Dispatch uses the concrete specializations, not generic columns.
get_loader = {
USEquityPricing.low: low_loader,
USEquityPricing.high: high_loader
}.__getitem__
engine = SimplePipelineEngine(get_loader, self.asset_finder)
for window_length in range(1, 4):
low_mavg = SimpleMovingAverage(
inputs=[EquityPricing.low],
window_length=window_length,
)
high_mavg = SimpleMovingAverage(
inputs=[EquityPricing.high],
window_length=window_length,
)
bounds = product_upper_triangle(range(window_length, len(dates)))
for start, stop in bounds:
results = engine.run_pipeline(
Pipeline(
columns={'low': low_mavg, 'high': high_mavg},
domain=self.domain,
),
dates[start],
dates[stop],
)
self.assertEqual(set(results.columns), {'low', 'high'})
iloc_bounds = slice(start, stop + 1) # +1 to include end date
low_results = results.unstack()['low']
assert_frame_equal(low_results, low_base.iloc[iloc_bounds])
high_results = results.unstack()['high']
assert_frame_equal(high_results, high_base.iloc[iloc_bounds])
class SyntheticBcolzTestCase(zf.WithAdjustmentReader,
zf.WithAssetFinder,
zf.ZiplineTestCase):
first_asset_start = Timestamp('2015-04-01', tz='UTC')
START_DATE = Timestamp('2015-01-01', tz='utc')
END_DATE = Timestamp('2015-08-01', tz='utc')
@classmethod
def make_equity_info(cls):
cls.equity_info = ret = make_rotating_equity_info(
num_assets=6,
first_start=cls.first_asset_start,
frequency=cls.trading_calendar.day,
periods_between_starts=4,
asset_lifetime=8,
exchange='NYSE',
)
return ret
@classmethod
def make_exchanges_info(cls, *args, **kwargs):
return DataFrame({'exchange': ['NYSE'], 'country_code': ['US']})
@classmethod
def make_equity_daily_bar_data(cls, country_code, sids):
return make_bar_data(
cls.equity_info,
cls.equity_daily_bar_days,
)
@classmethod
def init_class_fixtures(cls):
super(SyntheticBcolzTestCase, cls).init_class_fixtures()
cls.all_asset_ids = cls.asset_finder.sids
cls.last_asset_end = cls.equity_info['end_date'].max()
cls.pipeline_loader = EquityPricingLoader.without_fx(
cls.bcolz_equity_daily_bar_reader,
cls.adjustment_reader,
)
cls.engine = SimplePipelineEngine(
lambda c: cls.pipeline_loader,
cls.asset_finder,
default_domain=US_EQUITIES,
)
def write_nans(self, df):
"""
Write nans to the locations in data corresponding to the (date, asset)
pairs for which we wouldn't have data for `asset` on `date` in a
backtest.
Parameters
----------
df : pd.DataFrame
A DataFrame with a DatetimeIndex as index and an object index of
Assets as columns.
This means that we write nans for dates after an asset's end_date and
**on or before** an asset's start_date. The assymetry here is because
of the fact that, on the morning of an asset's first date, we haven't
yet seen any trades for that asset, so we wouldn't be able to show any
useful data to the user.
"""
# Mask out with nans all the dates on which each asset didn't exist
index = df.index
min_, max_ = index[[0, -1]]
for asset in df.columns:
if asset.start_date >= min_:
start = index.get_loc(asset.start_date, method='bfill')
df.loc[:start + 1, asset] = nan # +1 to overwrite start_date
if asset.end_date <= max_:
end = index.get_loc(asset.end_date)
df.ix[end + 1:, asset] = nan # +1 to *not* overwrite end_date
def test_SMA(self):
window_length = 5
asset_ids = self.all_asset_ids
dates = date_range(
self.first_asset_start + self.trading_calendar.day,
self.last_asset_end,
freq=self.trading_calendar.day,
)
dates_to_test = dates[window_length:]
SMA = SimpleMovingAverage(
inputs=(EquityPricing.close,),
window_length=window_length,
)
results = self.engine.run_pipeline(
Pipeline(columns={'sma': SMA}),
dates_to_test[0],
dates_to_test[-1],
)
# Shift back the raw inputs by a trading day because we expect our
# computed results to be computed using values anchored on the
# **previous** day's data.
expected_raw = DataFrame(
expected_bar_values_2d(
dates - self.trading_calendar.day,
asset_ids,
self.equity_info,
'close',
),
).rolling(window_length, min_periods=1).mean().values
expected = DataFrame(
# Truncate off the extra rows needed to compute the SMAs.
expected_raw[window_length:],
index=dates_to_test, # dates_to_test is dates[window_length:]
columns=self.asset_finder.retrieve_all(asset_ids),
)
self.write_nans(expected)
result = results['sma'].unstack()
assert_frame_equal(result, expected)
def test_drawdown(self):
# The monotonically-increasing data produced by SyntheticDailyBarWriter
# exercises two pathological cases for MaxDrawdown. The actual
# computed results are pretty much useless (everything is either NaN)
# or zero, but verifying we correctly handle those corner cases is
# valuable.
window_length = 5
asset_ids = self.all_asset_ids
dates = date_range(
self.first_asset_start + self.trading_calendar.day,
self.last_asset_end,
freq=self.trading_calendar.day,
)
dates_to_test = dates[window_length:]
drawdown = MaxDrawdown(
inputs=(EquityPricing.close,),
window_length=window_length,
)
results = self.engine.run_pipeline(
Pipeline(columns={'drawdown': drawdown}),
dates_to_test[0],
dates_to_test[-1],
)
# We expect NaNs when the asset was undefined, otherwise 0 everywhere,
# since the input is always increasing.
expected = DataFrame(
data=zeros((len(dates_to_test), len(asset_ids)), dtype=float),
index=dates_to_test,
columns=self.asset_finder.retrieve_all(asset_ids),
)
self.write_nans(expected)
result = results['drawdown'].unstack()
assert_frame_equal(expected, result)
class ParameterizedFactorTestCase(zf.WithAssetFinder,
zf.WithTradingCalendars,
zf.ZiplineTestCase):
sids = ASSET_FINDER_EQUITY_SIDS = Int64Index([1, 2, 3])
START_DATE = Timestamp('2015-01-31', tz='UTC')
END_DATE = Timestamp('2015-03-01', tz='UTC')
ASSET_FINDER_COUNTRY_CODE = '??'
@classmethod
def init_class_fixtures(cls):
super(ParameterizedFactorTestCase, cls).init_class_fixtures()
day = cls.trading_calendar.day
cls.dates = dates = date_range(
'2015-02-01',
'2015-02-28',
freq=day,
tz='UTC',
)
sids = cls.sids
cls.raw_data = DataFrame(
data=arange(len(dates) * len(sids), dtype=float).reshape(
len(dates), len(sids),
),
index=dates,
columns=cls.asset_finder.retrieve_all(sids),
)
cls.raw_data_with_nans = cls.raw_data.where((cls.raw_data % 2) != 0)
open_loader = DataFrameLoader(
EquityPricing.open,
cls.raw_data_with_nans,
)
close_loader = DataFrameLoader(EquityPricing.close, cls.raw_data)
volume_loader = DataFrameLoader(
EquityPricing.volume,
cls.raw_data * 2,
)
loader_map = {
EquityPricing.open: open_loader,
EquityPricing.close: close_loader,
EquityPricing.volume: volume_loader,
}
def get_loader(c):
return loader_map[c.unspecialize()]
cls.engine = SimplePipelineEngine(
get_loader,
cls.asset_finder,
default_domain=EquitySessionDomain(cls.dates, '??'),
)
def expected_ewma(self, window_length, decay_rate):
alpha = 1 - decay_rate
span = (2 / alpha) - 1
# XXX: This is a comically inefficient way to compute a windowed EWMA.
# Don't use it outside of testing. We're using rolling-apply of an
# ewma (which is itself a rolling-window function) because we only want
# to look at ``window_length`` rows at a time.
return self.raw_data.rolling(window_length).apply(
lambda subarray: (DataFrame(subarray)
.ewm(span=span)
.mean()
.values[-1])
)[window_length:]
def expected_ewmstd(self, window_length, decay_rate):
alpha = 1 - decay_rate
span = (2 / alpha) - 1
# XXX: This is a comically inefficient way to compute a windowed
# EWMSTD. Don't use it outside of testing. We're using rolling-apply
# of an ewma (which is itself a rolling-window function) because we
# only want to look at ``window_length`` rows at a time.
return self.raw_data.rolling(window_length).apply(
lambda subarray: (DataFrame(subarray)
.ewm(span=span)
.std()
.values[-1])
)[window_length:]
@parameterized.expand([
(3,),
(5,),
])
def test_ewm_stats(self, window_length):
def ewma_name(decay_rate):
return 'ewma_%s' % decay_rate
def ewmstd_name(decay_rate):
return 'ewmstd_%s' % decay_rate
decay_rates = [0.25, 0.5, 0.75]
ewmas = {
ewma_name(decay_rate): EWMA(
inputs=(EquityPricing.close,),
window_length=window_length,
decay_rate=decay_rate,
)
for decay_rate in decay_rates
}
ewmstds = {
ewmstd_name(decay_rate): EWMSTD(
inputs=(EquityPricing.close,),
window_length=window_length,
decay_rate=decay_rate,
)
for decay_rate in decay_rates
}
all_results = self.engine.run_pipeline(
Pipeline(columns=merge(ewmas, ewmstds)),
self.dates[window_length],
self.dates[-1],
)
for decay_rate in decay_rates:
ewma_result = all_results[ewma_name(decay_rate)].unstack()
ewma_expected = self.expected_ewma(window_length, decay_rate)
assert_frame_equal(ewma_result, ewma_expected)
ewmstd_result = all_results[ewmstd_name(decay_rate)].unstack()
ewmstd_expected = self.expected_ewmstd(window_length, decay_rate)
assert_frame_equal(ewmstd_result, ewmstd_expected)
@staticmethod
def decay_rate_to_span(decay_rate):
alpha = 1 - decay_rate
return (2 / alpha) - 1
@staticmethod
def decay_rate_to_com(decay_rate):
alpha = 1 - decay_rate
return (1 / alpha) - 1
@staticmethod
def decay_rate_to_halflife(decay_rate):
return log(.5) / log(decay_rate)
def ewm_cases():
return product([EWMSTD, EWMA], [3, 5, 10])
@parameterized.expand(ewm_cases())
def test_from_span(self, type_, span):
from_span = type_.from_span(
inputs=[EquityPricing.close],
window_length=20,
span=span,
)
implied_span = self.decay_rate_to_span(from_span.params['decay_rate'])
assert_almost_equal(span, implied_span)
@parameterized.expand(ewm_cases())
def test_from_halflife(self, type_, halflife):
from_hl = EWMA.from_halflife(
inputs=[EquityPricing.close],
window_length=20,
halflife=halflife,
)
implied_hl = self.decay_rate_to_halflife(from_hl.params['decay_rate'])
assert_almost_equal(halflife, implied_hl)
@parameterized.expand(ewm_cases())
def test_from_com(self, type_, com):
from_com = EWMA.from_center_of_mass(
inputs=[EquityPricing.close],
window_length=20,
center_of_mass=com,
)
implied_com = self.decay_rate_to_com(from_com.params['decay_rate'])
assert_almost_equal(com, implied_com)
del ewm_cases
def test_ewm_aliasing(self):
self.assertIs(ExponentialWeightedMovingAverage, EWMA)
self.assertIs(ExponentialWeightedMovingStdDev, EWMSTD)
def test_dollar_volume(self):
results = self.engine.run_pipeline(
Pipeline(
columns={
'dv1': AverageDollarVolume(window_length=1),
'dv5': AverageDollarVolume(window_length=5),
'dv1_nan': AverageDollarVolume(
window_length=1,
inputs=[EquityPricing.open, EquityPricing.volume],
),
'dv5_nan': AverageDollarVolume(
window_length=5,
inputs=[EquityPricing.open, EquityPricing.volume],
),
}
),
self.dates[5],
self.dates[-1],
)
expected_1 = (self.raw_data[5:] ** 2) * 2
assert_frame_equal(results['dv1'].unstack(), expected_1)
expected_5 = ((self.raw_data ** 2) * 2).rolling(5).mean()[5:]
assert_frame_equal(results['dv5'].unstack(), expected_5)
# The following two use EquityPricing.open and .volume as inputs.
# The former uses self.raw_data_with_nans, and the latter uses
# .raw_data * 2. Thus we multiply instead of squaring as above.
expected_1_nan = (self.raw_data_with_nans[5:]
* self.raw_data[5:] * 2).fillna(0)
assert_frame_equal(results['dv1_nan'].unstack(), expected_1_nan)
expected_5_nan = ((self.raw_data_with_nans * self.raw_data * 2)
.fillna(0)
.rolling(5).mean()
[5:])
assert_frame_equal(results['dv5_nan'].unstack(), expected_5_nan)
class StringColumnTestCase(zf.WithSeededRandomPipelineEngine,
zf.ZiplineTestCase):
ASSET_FINDER_COUNTRY_CODE = 'US'
SEEDED_RANDOM_PIPELINE_DEFAULT_DOMAIN = US_EQUITIES
@skipIf(new_pandas, skip_pipeline_new_pandas)
def test_string_classifiers_produce_categoricals(self):
"""
Test that string-based classifiers produce pandas categoricals as their
outputs.
"""
col = TestingDataSet.categorical_col
pipe = Pipeline(columns={'c': col.latest})
run_dates = self.trading_days[-10:]
start_date, end_date = run_dates[[0, -1]]
result = self.run_pipeline(pipe, start_date, end_date)
assert isinstance(result.c.values, Categorical)
expected_raw_data = self.raw_expected_values(
col,
start_date,
end_date,
)
expected_labels = LabelArray(expected_raw_data, col.missing_value)
expected_final_result = expected_labels.as_categorical_frame(
index=run_dates,
columns=self.asset_finder.retrieve_all(self.asset_finder.sids),
)
assert_frame_equal(result.c.unstack(), expected_final_result)
class WindowSafetyPropagationTestCase(zf.WithSeededRandomPipelineEngine,
zf.ZiplineTestCase):
ASSET_FINDER_COUNTRY_CODE = 'US'
SEEDED_RANDOM_PIPELINE_DEFAULT_DOMAIN = US_EQUITIES
SEEDED_RANDOM_PIPELINE_SEED = 5
def test_window_safety_propagation(self):
dates = self.trading_days[-30:]
start_date, end_date = dates[[-10, -1]]
col = TestingDataSet.float_col
pipe = Pipeline(
columns={
'average_of_rank_plus_one': SimpleMovingAverage(
inputs=[col.latest.rank() + 1],
window_length=10,
),
'average_of_aliased_rank_plus_one': SimpleMovingAverage(
inputs=[col.latest.rank().alias('some_alias') + 1],
window_length=10,
),
'average_of_rank_plus_one_aliased': SimpleMovingAverage(
inputs=[(col.latest.rank() + 1).alias('some_alias')],
window_length=10,
),
}
)
results = self.run_pipeline(pipe, start_date, end_date).unstack()
expected_ranks = DataFrame(
self.raw_expected_values(
col,
dates[-19],
dates[-1],
),
index=dates[-19:],
columns=self.asset_finder.retrieve_all(
self.ASSET_FINDER_EQUITY_SIDS,
)
).rank(axis='columns')
# All three expressions should be equivalent and evaluate to this.
expected_result = (
(expected_ranks + 1)
.rolling(10)
.mean()
.dropna(how='any')
)
for colname in results.columns.levels[0]:
assert_equal(expected_result, results[colname])
class PopulateInitialWorkspaceTestCase(WithConstantInputs,
zf.WithAssetFinder,
zf.WithTradingCalendars,
zf.ZiplineTestCase):
@parameter_space(window_length=[3, 5], pipeline_length=[5, 10])
def test_populate_initial_workspace(self, window_length, pipeline_length):
column = EquityPricing.low
base_term = column.latest
# Take a Z-Score here so that the precomputed term is window-safe. The
# z-score will never actually get computed because we swap it out.
precomputed_term = (base_term.zscore()).alias('precomputed_term')
# A term that has `precomputed_term` as an input.
depends_on_precomputed_term = precomputed_term + 1
# A term that requires a window of `precomputed_term`.
depends_on_window_of_precomputed_term = SimpleMovingAverage(
inputs=[precomputed_term],
window_length=window_length,
)
precomputed_term_with_window = SimpleMovingAverage(
inputs=(column,),
window_length=window_length,
).alias('precomputed_term_with_window')
depends_on_precomputed_term_with_window = (
precomputed_term_with_window + 1
)
column_value = self.constants[column]
precomputed_term_value = -column_value
precomputed_term_with_window_value = -(column_value + 1)
def populate_initial_workspace(initial_workspace,
root_mask_term,
execution_plan,
dates,
assets):
def shape_for_term(term):
ndates = len(execution_plan.mask_and_dates_for_term(
term,
root_mask_term,
initial_workspace,
dates,
)[1])
nassets = len(assets)
return (ndates, nassets)
ws = initial_workspace.copy()
ws[precomputed_term] = full(
shape_for_term(precomputed_term),
precomputed_term_value,
dtype=float64,
)
ws[precomputed_term_with_window] = full(
shape_for_term(precomputed_term_with_window),
precomputed_term_with_window_value,
dtype=float64,
)
return ws
def dispatcher(c):
self.assertIsNot(
c, column, "Shouldn't need to dispatch precomputed term input!"
)
return self.loader
engine = SimplePipelineEngine(
dispatcher,
self.asset_finder,
populate_initial_workspace=populate_initial_workspace,
)
results = engine.run_pipeline(
Pipeline({
'precomputed_term': precomputed_term,
'precomputed_term_with_window': precomputed_term_with_window,
'depends_on_precomputed_term': depends_on_precomputed_term,
'depends_on_precomputed_term_with_window':
depends_on_precomputed_term_with_window,
'depends_on_window_of_precomputed_term':
depends_on_window_of_precomputed_term,
}, domain=self.domain),
self.dates[-pipeline_length],
self.dates[-1],
)
assert_equal(
results['precomputed_term'].values,
full_like(
results['precomputed_term'],
precomputed_term_value,
),
),
assert_equal(
results['precomputed_term_with_window'].values,
full_like(
results['precomputed_term_with_window'],
precomputed_term_with_window_value,
),
),
assert_equal(
results['depends_on_precomputed_term'].values,
full_like(
results['depends_on_precomputed_term'],
precomputed_term_value + 1,
),
)
assert_equal(
results['depends_on_precomputed_term_with_window'].values,
full_like(
results['depends_on_precomputed_term_with_window'],
precomputed_term_with_window_value + 1,
),
)
assert_equal(
results['depends_on_window_of_precomputed_term'].values,
full_like(
results['depends_on_window_of_precomputed_term'],
precomputed_term_value,
),
)
class ChunkedPipelineTestCase(zf.WithSeededRandomPipelineEngine,
zf.ZiplineTestCase):
PIPELINE_START_DATE = Timestamp('2006-01-05', tz='UTC')
END_DATE = Timestamp('2006-12-29', tz='UTC')
ASSET_FINDER_COUNTRY_CODE = 'US'
def test_run_chunked_pipeline(self):
"""
Test that running a pipeline in chunks produces the same result as if
it were run all at once
"""
pipe = Pipeline(
columns={
'float': TestingDataSet.float_col.latest,
'custom_factor': SimpleMovingAverage(
inputs=[TestingDataSet.float_col],
window_length=10,
),
},
domain=US_EQUITIES,
)
if not new_pandas:
# Categoricals only work on old pandas.
pipe.add(TestingDataSet.categorical_col.latest, 'categorical')
pipeline_result = self.run_pipeline(
pipe,
start_date=self.PIPELINE_START_DATE,
end_date=self.END_DATE,
)
chunked_result = self.run_chunked_pipeline(
pipeline=pipe,
start_date=self.PIPELINE_START_DATE,
end_date=self.END_DATE,
chunksize=22
)
self.assertTrue(chunked_result.equals(pipeline_result))
def test_concatenate_empty_chunks(self):
# Test that we correctly handle concatenating chunked pipelines when
# some of the chunks are empty. This is slightly tricky b/c pandas
# DataFrames lose dtype information when they're empty.
class FalseOnOddMonths(CustomFilter):
"""Filter that returns False for all assets during odd months.
"""
inputs = ()
window_length = 1
def compute(self, today, assets, out):
out[:] = (today.month % 2 == 0)
pipe = Pipeline(
columns={
'float': TestingDataSet.float_col.latest,
'bool': TestingDataSet.bool_col.latest,
},
# Define a screen that's False for all assets a significant portion
# of the time.
screen=FalseOnOddMonths(),
domain=US_EQUITIES,
)
if not new_pandas:
# Categoricals only work on old pandas.
pipe.add(TestingDataSet.categorical_col.latest, 'categorical')
self.run_chunked_pipeline(
pipeline=pipe,
start_date=self.PIPELINE_START_DATE,
end_date=self.END_DATE,
# Make chunksize small enough that some chunks are guaranteed to
# have no assets pass the screen.
chunksize=5,
)
class MaximumRegressionTest(zf.WithSeededRandomPipelineEngine,
zf.ZiplineTestCase):
ASSET_FINDER_EQUITY_SIDS = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
def test_no_groupby_maximum(self):
# This is a regression test for a bug where factor.top(1) would fail
# when not passed a groupby parameter.
factor = TestingDataSet.float_col.latest
maximum = factor.top(1)
pipe = Pipeline(
{'factor': factor, 'maximum': maximum},
domain=EquitySessionDomain(
self.trading_days,
self.ASSET_FINDER_COUNTRY_CODE,
),
)
result = self.run_pipeline(
pipe, self.trading_days[-5], self.trading_days[-1]
)
# We should have one maximum every day.
maxes_per_day = result.groupby(level=0)['maximum'].sum()
self.assertTrue((maxes_per_day == 1).all())
# The maximum computed by pipeline should match the maximum computed by
# doing a groupby in pandas.
groupby_max = result.groupby(level=0).factor.max()
pipeline_max = (result.factor[result.maximum]
.reset_index(level=1, drop=True))
assert_equal(groupby_max, pipeline_max)
class ResolveDomainTestCase(zf.ZiplineTestCase):
def test_resolve_domain(self):
# we need to pass a get_loader and an asset_finder to construct
# SimplePipelineEngine, but do not expect to use them
get_loader = NamedExplodingObject(
'self._get_loader',
'SimplePipelineEngine does not currently depend on get_loader '
'at construction time. Update this test if it now does.'
)
asset_finder = NamedExplodingObject(
'self._finder',
'SimplePipelineEngine does not currently depend on asset_finder '
'at construction time. Update this test if it now does.'
)
engine_generic = SimplePipelineEngine(
get_loader, asset_finder, default_domain=GENERIC
)
engine_jp = SimplePipelineEngine(
get_loader, asset_finder, default_domain=JP_EQUITIES
)
pipe_generic = Pipeline()
pipe_us = Pipeline(domain=US_EQUITIES)
# the engine should resolve a pipeline that already has a domain
# to that domain
self.assertIs(
engine_jp.resolve_domain(pipe_us),
US_EQUITIES
)
# the engine should resolve a pipeline without a domain to the engine's
# default
self.assertIs(
engine_jp.resolve_domain(pipe_generic),
JP_EQUITIES
)
# a generic engine should resolve to the pipeline's domain
# if it has one
self.assertIs(
engine_generic.resolve_domain(pipe_us),
US_EQUITIES
)
# an engine with a default of GENERIC should raise a ValueError when
# trying to infer a pipeline whose domain is also GENERIC
with self.assertRaises(ValueError):
engine_generic.resolve_domain(pipe_generic)
# infer domain from the column if the pipeline and engine have
# a GENERIC domain
pipe = Pipeline({'close': USEquityPricing.close.latest})
self.assertIs(
engine_generic.resolve_domain(pipe),
US_EQUITIES,
)
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