""" Tests for SimplePipelineEngine """ from __future__ import division from collections import OrderedDict from itertools import product from operator import add, sub from nose_parameterized import parameterized 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 catalyst.assets.synthetic import make_rotating_equity_info from catalyst.errors import NoFurtherDataError from catalyst.lib.adjustment import MULTIPLY from catalyst.lib.labelarray import LabelArray from catalyst.pipeline import CustomFactor, Pipeline from catalyst.pipeline.data import Column, DataSet, USEquityPricing from catalyst.pipeline.data.testing import TestingDataSet from catalyst.pipeline.engine import SimplePipelineEngine from catalyst.pipeline.factors.equity import ( AverageDollarVolume, EWMA, EWMSTD, ExponentialWeightedMovingAverage, ExponentialWeightedMovingStdDev, MaxDrawdown, Returns, SimpleMovingAverage, ) from catalyst.pipeline.loaders.equity_pricing_loader import ( USEquityPricingLoader, ) from catalyst.pipeline.loaders.frame import DataFrameLoader from catalyst.pipeline.loaders.synthetic import ( PrecomputedLoader, make_bar_data, expected_bar_values_2d, ) from catalyst.pipeline.sentinels import NotSpecified from catalyst.pipeline.term import InputDates from catalyst.testing import ( AssetID, AssetIDPlusDay, ExplodingObject, check_arrays, make_alternating_boolean_array, make_cascading_boolean_array, OpenPrice, parameter_space, product_upper_triangle, ) from catalyst.testing.fixtures import ( WithAdjustmentReader, WithEquityPricingPipelineEngine, WithSeededRandomPipelineEngine, WithTradingEnvironment, ZiplineTestCase, ) from catalyst.testing.predicates import assert_equal from catalyst.utils.memoize import lazyval from catalyst.utils.numpy_utils import bool_dtype, datetime64ns_dtype class RollingSumDifference(CustomFactor): window_length = 3 inputs = [USEquityPricing.open, USEquityPricing.close] def compute(self, today, assets, out, open, close): out[:] = (open - close).sum(axis=0) class MultipleOutputs(CustomFactor): window_length = 1 inputs = [USEquityPricing.open, USEquityPricing.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 = [USEquityPricing.open, USEquityPricing.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, columns, dates, assets, mask): self.load_calls.append(ColumnArgs(*columns)) return super(RecordingPrecomputedLoader, self).load_adjusted_array( columns, dates, assets, 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(WithTradingEnvironment): 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') @classmethod def init_class_fixtures(cls): super(WithConstantInputs, cls).init_class_fixtures() cls.constants = { # Every day, assume every stock starts at 2, goes down to 1, # goes up to 4, and finishes at 3. USEquityPricing.low: 1, USEquityPricing.open: 2, USEquityPricing.close: 3, USEquityPricing.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) class ConstantInputTestCase(WithConstantInputs, ZiplineTestCase): def test_bad_dates(self): loader = self.loader engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) p = Pipeline() msg = "start_date must be before or equal to end_date .*" with self.assertRaisesRegexp(ValueError, msg): engine.run_pipeline(p, self.dates[2], self.dates[1]) def test_fail_usefully_on_insufficient_data(self): loader = self.loader engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) class SomeFactor(CustomFactor): inputs = [USEquityPricing.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. 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): engine.run_pipeline(p, self.dates[8], self.dates[8]) def test_input_dates_provided_by_default(self): loader = self.loader engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) class TestFactor(CustomFactor): inputs = [InputDates(), USEquityPricing.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 = 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): loader = self.loader engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) 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 = engine.run_pipeline(p, self.dates[1], self.dates[1]) self.assertEqual(result['f'][0], 1.0) def test_screen(self): loader = self.loader finder = self.asset_finder asset_ids = array(self.asset_ids) engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) 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 = engine.run_pipeline(p, dates[0], dates[-1]) expected_sids = asset_ids[asset_ids <= asset_id] expected_assets = 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): loader = self.loader assets = self.assets engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) 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 = 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): loader = self.loader assets = self.assets engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) 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=[USEquityPricing.open, USEquityPricing.high], ) pipeline = Pipeline( columns={ 'short': short_factor, 'long': long_factor, 'high': high_factor, } ) results = 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 loader = self.loader engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) num_dates = 5 dates = self.dates[10:10 + num_dates] high, low = USEquityPricing.high, USEquityPricing.low open, close = USEquityPricing.open, USEquityPricing.close high_minus_low = RollingSumDifference(inputs=[high, low]) open_minus_close = RollingSumDifference(inputs=[open, close]) avg = (high_minus_low + open_minus_close) / 2 results = 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. """ loader = self.loader 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 = USEquityPricing.open close = USEquityPricing.close engine = SimplePipelineEngine( lambda column: loader, self.dates, self.asset_finder, ) 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 = 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 = 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_ = USEquityPricing.open close = USEquityPricing.close volume = USEquityPricing.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.dates, self.asset_finder, ) sumdiff = RollingSumDifference() result = engine.run_pipeline( Pipeline( columns={ 'sumdiff': sumdiff, 'open': open_.latest, 'close': close.latest, 'volume': volume.latest, }, ), 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 = USEquityPricing.open open_values = [self.constants[open]] * num_dates open_values_as_tuple = [(self.constants[open],)] * num_dates engine = SimplePipelineEngine( lambda column: self.loader, self.dates, self.asset_finder, ) single_output = OpenPrice(outputs=['open']) pipeline = Pipeline( columns={ 'open_instance': single_output, 'open_attribute': single_output.open, }, ) results = 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 = USEquityPricing.open close = USEquityPricing.close engine = SimplePipelineEngine( lambda column: self.loader, self.dates, self.asset_finder, ) 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 = 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 engine = SimplePipelineEngine( lambda column: self.loader, self.dates, self.asset_finder, ) open_values = [constants[USEquityPricing.open]] * num_assets close_values = [constants[USEquityPricing.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 = 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 engine = SimplePipelineEngine( lambda column: self.loader, self.dates, self.asset_finder, ) 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 = 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[USEquityPricing.open] * window_length, constants[USEquityPricing.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) class Loader1DataSet2(DataSet): col1 = Column(float32) col2 = Column(float32) class Loader2DataSet(DataSet): col1 = Column(float32) col2 = Column(float32) 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.dates, 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), 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)}) class FrameInputTestCase(WithTradingEnvironment, ZiplineTestCase): asset_ids = ASSET_FINDER_EQUITY_SIDS = 1, 2, 3 start = START_DATE = Timestamp('2015-01-01', tz='utc') end = END_DATE = Timestamp('2015-01-31', tz='utc') @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) @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 = USEquityPricing.low, USEquityPricing.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) engine = SimplePipelineEngine( {low: low_loader, high: high_loader}.__getitem__, self.dates, self.asset_finder, ) for window_length in range(1, 4): low_mavg = SimpleMovingAverage( inputs=[USEquityPricing.low], window_length=window_length, ) high_mavg = SimpleMovingAverage( inputs=[USEquityPricing.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} ), 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(WithAdjustmentReader, 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, ) return ret @classmethod def make_equity_daily_bar_data(cls): 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 = USEquityPricingLoader( cls.bcolz_equity_daily_bar_reader, cls.adjustment_reader, USEquityPricing, ) 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): engine = SimplePipelineEngine( lambda column: self.pipeline_loader, self.trading_calendar.all_sessions, self.asset_finder, ) 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=(USEquityPricing.close,), window_length=window_length, ) results = 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, 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. engine = SimplePipelineEngine( lambda column: self.pipeline_loader, self.trading_calendar.all_sessions, self.asset_finder, ) 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=(USEquityPricing.close,), window_length=window_length, ) results = 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(WithTradingEnvironment, 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') @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( USEquityPricing.open, cls.raw_data_with_nans, ) close_loader = DataFrameLoader(USEquityPricing.close, cls.raw_data) volume_loader = DataFrameLoader( USEquityPricing.volume, cls.raw_data * 2, ) cls.engine = SimplePipelineEngine( { USEquityPricing.open: open_loader, USEquityPricing.close: close_loader, USEquityPricing.volume: volume_loader, }.__getitem__, cls.dates, cls.asset_finder, ) 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=(USEquityPricing.close,), window_length=window_length, decay_rate=decay_rate, ) for decay_rate in decay_rates } ewmstds = { ewmstd_name(decay_rate): EWMSTD( inputs=(USEquityPricing.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=[USEquityPricing.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=[USEquityPricing.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=[USEquityPricing.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=[USEquityPricing.open, USEquityPricing.volume], ), 'dv5_nan': AverageDollarVolume( window_length=5, inputs=[USEquityPricing.open, USEquityPricing.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 USEquityPricing.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(WithSeededRandomPipelineEngine, ZiplineTestCase): 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(WithSeededRandomPipelineEngine, ZiplineTestCase): 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, ZiplineTestCase): @parameter_space(window_length=[3, 5], pipeline_length=[5, 10]) def test_populate_initial_workspace(self, window_length, pipeline_length): column = USEquityPricing.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): if c is column: # the base_term should never be loaded, its initial refcount # should be zero return ExplodingObject() return self.loader engine = SimplePipelineEngine( dispatcher, self.dates, 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, }), 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(WithEquityPricingPipelineEngine, ZiplineTestCase): PIPELINE_START_DATE = Timestamp('2006-01-05', tz='UTC') END_DATE = Timestamp('2006-12-29', tz='UTC') 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={ 'close': USEquityPricing.close.latest, 'returns': Returns(window_length=2), 'categorical': USEquityPricing.close.latest.quantiles(5) }, ) pipeline_result = self.pipeline_engine.run_pipeline( pipe, start_date=self.PIPELINE_START_DATE, end_date=self.END_DATE, ) chunked_result = self.pipeline_engine.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))