mirror of
https://github.com/wassname/catalyst.git
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Conner Fromknecht
805 lines
32 KiB
Python
805 lines
32 KiB
Python
"""
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Tests for statistical pipeline terms.
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"""
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from numpy import (
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arange,
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full,
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full_like,
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nan,
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where,
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)
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from pandas import (
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DataFrame,
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date_range,
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Int64Index,
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Timestamp,
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)
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from pandas.util.testing import assert_frame_equal
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from scipy.stats import linregress, pearsonr, spearmanr
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from catalyst.assets import Equity
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from catalyst.errors import IncompatibleTerms, NonExistentAssetInTimeFrame
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from catalyst.pipeline import CustomFactor, Pipeline
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from catalyst.pipeline.data import USEquityPricing
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from catalyst.pipeline.data.testing import TestingDataSet
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from catalyst.pipeline.engine import SimplePipelineEngine
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from catalyst.pipeline.factors.equity import (
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Returns,
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RollingLinearRegressionOfReturns,
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RollingPearsonOfReturns,
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RollingSpearmanOfReturns,
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)
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from catalyst.pipeline.loaders.frame import DataFrameLoader
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from catalyst.pipeline.sentinels import NotSpecified
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from catalyst.testing import (
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AssetID,
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AssetIDPlusDay,
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check_arrays,
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make_alternating_boolean_array,
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make_cascading_boolean_array,
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parameter_space,
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)
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from catalyst.testing.fixtures import (
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WithSeededRandomPipelineEngine,
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WithTradingEnvironment,
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ZiplineTestCase,
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)
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from catalyst.utils.numpy_utils import (
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bool_dtype,
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datetime64ns_dtype,
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float64_dtype,
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)
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class StatisticalBuiltInsTestCase(WithTradingEnvironment, ZiplineTestCase):
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sids = ASSET_FINDER_EQUITY_SIDS = Int64Index([1, 2, 3])
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START_DATE = Timestamp('2015-01-31', tz='UTC')
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END_DATE = Timestamp('2015-03-01', tz='UTC')
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@classmethod
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def init_class_fixtures(cls):
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super(StatisticalBuiltInsTestCase, cls).init_class_fixtures()
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day = cls.trading_calendar.day
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cls.dates = dates = date_range(
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'2015-02-01', '2015-02-28', freq=day, tz='UTC',
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)
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# Using these start and end dates because they are a contigous span of
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# 5 days (Monday - Friday) and they allow for plenty of days to look
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# back on when computing correlations and regressions.
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cls.start_date_index = start_date_index = 14
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cls.end_date_index = end_date_index = 18
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cls.pipeline_start_date = dates[start_date_index]
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cls.pipeline_end_date = dates[end_date_index]
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cls.num_days = num_days = end_date_index - start_date_index + 1
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sids = cls.sids
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cls.assets = assets = cls.asset_finder.retrieve_all(sids)
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cls.my_asset_column = my_asset_column = 0
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cls.my_asset = assets[my_asset_column]
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cls.num_assets = num_assets = len(assets)
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cls.raw_data = raw_data = DataFrame(
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data=arange(len(dates) * len(sids), dtype=float64_dtype).reshape(
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len(dates), len(sids),
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),
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index=dates,
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columns=assets,
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)
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# Using mock 'close' data here because the correlation and regression
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# built-ins use USEquityPricing.close as the input to their `Returns`
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# factors. Since there is no way to change that when constructing an
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# instance of these built-ins, we need to test with mock 'close' data
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# to most accurately reflect their true behavior and results.
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close_loader = DataFrameLoader(USEquityPricing.close, raw_data)
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cls.run_pipeline = SimplePipelineEngine(
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{USEquityPricing.close: close_loader}.__getitem__,
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dates,
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cls.asset_finder,
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).run_pipeline
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cls.cascading_mask = \
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AssetIDPlusDay() < (sids[-1] + dates[start_date_index].day)
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cls.expected_cascading_mask_result = make_cascading_boolean_array(
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shape=(num_days, num_assets),
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)
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cls.alternating_mask = (AssetIDPlusDay() % 2).eq(0)
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cls.expected_alternating_mask_result = make_alternating_boolean_array(
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shape=(num_days, num_assets),
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)
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cls.expected_no_mask_result = full(
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shape=(num_days, num_assets), fill_value=True, dtype=bool_dtype,
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)
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@parameter_space(returns_length=[2, 3], correlation_length=[3, 4])
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def test_correlation_factors(self, returns_length, correlation_length):
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"""
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Tests for the built-in factors `RollingPearsonOfReturns` and
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`RollingSpearmanOfReturns`.
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"""
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assets = self.assets
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my_asset = self.my_asset
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my_asset_column = self.my_asset_column
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dates = self.dates
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start_date = self.pipeline_start_date
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end_date = self.pipeline_end_date
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start_date_index = self.start_date_index
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end_date_index = self.end_date_index
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num_days = self.num_days
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run_pipeline = self.run_pipeline
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returns = Returns(window_length=returns_length)
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masks = (self.cascading_mask, self.alternating_mask, NotSpecified)
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expected_mask_results = (
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self.expected_cascading_mask_result,
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self.expected_alternating_mask_result,
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self.expected_no_mask_result,
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)
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for mask, expected_mask in zip(masks, expected_mask_results):
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pearson_factor = RollingPearsonOfReturns(
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target=my_asset,
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returns_length=returns_length,
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correlation_length=correlation_length,
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mask=mask,
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)
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spearman_factor = RollingSpearmanOfReturns(
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target=my_asset,
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returns_length=returns_length,
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correlation_length=correlation_length,
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mask=mask,
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)
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columns = {
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'pearson_factor': pearson_factor,
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'spearman_factor': spearman_factor,
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}
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pipeline = Pipeline(columns=columns)
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if mask is not NotSpecified:
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pipeline.add(mask, 'mask')
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results = run_pipeline(pipeline, start_date, end_date)
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pearson_results = results['pearson_factor'].unstack()
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spearman_results = results['spearman_factor'].unstack()
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if mask is not NotSpecified:
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mask_results = results['mask'].unstack()
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check_arrays(mask_results.values, expected_mask)
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# Run a separate pipeline that calculates returns starting
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# (correlation_length - 1) days prior to our start date. This is
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# because we need (correlation_length - 1) extra days of returns to
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# compute our expected correlations.
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results = run_pipeline(
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Pipeline(columns={'returns': returns}),
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dates[start_date_index - (correlation_length - 1)],
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dates[end_date_index],
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)
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returns_results = results['returns'].unstack()
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# On each day, calculate the expected correlation coefficients
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# between the asset we are interested in and each other asset. Each
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# correlation is calculated over `correlation_length` days.
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expected_pearson_results = full_like(pearson_results, nan)
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expected_spearman_results = full_like(spearman_results, nan)
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for day in range(num_days):
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todays_returns = returns_results.iloc[
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day:day + correlation_length
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]
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my_asset_returns = todays_returns.iloc[:, my_asset_column]
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for asset, other_asset_returns in todays_returns.iteritems():
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asset_column = int(asset) - 1
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expected_pearson_results[day, asset_column] = pearsonr(
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my_asset_returns, other_asset_returns,
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)[0]
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expected_spearman_results[day, asset_column] = spearmanr(
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my_asset_returns, other_asset_returns,
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)[0]
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expected_pearson_results = DataFrame(
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data=where(expected_mask, expected_pearson_results, nan),
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index=dates[start_date_index:end_date_index + 1],
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columns=assets,
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)
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assert_frame_equal(pearson_results, expected_pearson_results)
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expected_spearman_results = DataFrame(
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data=where(expected_mask, expected_spearman_results, nan),
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index=dates[start_date_index:end_date_index + 1],
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columns=assets,
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)
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assert_frame_equal(spearman_results, expected_spearman_results)
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@parameter_space(returns_length=[2, 3], regression_length=[3, 4])
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def test_regression_of_returns_factor(self,
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returns_length,
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regression_length):
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"""
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Tests for the built-in factor `RollingLinearRegressionOfReturns`.
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"""
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assets = self.assets
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my_asset = self.my_asset
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my_asset_column = self.my_asset_column
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dates = self.dates
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start_date = self.pipeline_start_date
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end_date = self.pipeline_end_date
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start_date_index = self.start_date_index
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end_date_index = self.end_date_index
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num_days = self.num_days
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run_pipeline = self.run_pipeline
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# The order of these is meant to align with the output of `linregress`.
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outputs = ['beta', 'alpha', 'r_value', 'p_value', 'stderr']
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returns = Returns(window_length=returns_length)
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masks = self.cascading_mask, self.alternating_mask, NotSpecified
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expected_mask_results = (
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self.expected_cascading_mask_result,
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self.expected_alternating_mask_result,
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self.expected_no_mask_result,
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)
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for mask, expected_mask in zip(masks, expected_mask_results):
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regression_factor = RollingLinearRegressionOfReturns(
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target=my_asset,
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returns_length=returns_length,
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regression_length=regression_length,
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mask=mask,
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)
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columns = {
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output: getattr(regression_factor, output)
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for output in outputs
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}
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pipeline = Pipeline(columns=columns)
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if mask is not NotSpecified:
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pipeline.add(mask, 'mask')
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results = run_pipeline(pipeline, start_date, end_date)
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if mask is not NotSpecified:
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mask_results = results['mask'].unstack()
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check_arrays(mask_results.values, expected_mask)
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output_results = {}
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expected_output_results = {}
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for output in outputs:
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output_results[output] = results[output].unstack()
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expected_output_results[output] = full_like(
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output_results[output], nan,
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)
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# Run a separate pipeline that calculates returns starting
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# (regression_length - 1) days prior to our start date. This is
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# because we need (regression_length - 1) extra days of returns to
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# compute our expected regressions.
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results = run_pipeline(
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Pipeline(columns={'returns': returns}),
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dates[start_date_index - (regression_length - 1)],
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dates[end_date_index],
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)
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returns_results = results['returns'].unstack()
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# On each day, calculate the expected regression results for Y ~ X
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# where Y is the asset we are interested in and X is each other
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# asset. Each regression is calculated over `regression_length`
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# days of data.
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for day in range(num_days):
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todays_returns = returns_results.iloc[
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day:day + regression_length
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]
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my_asset_returns = todays_returns.iloc[:, my_asset_column]
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for asset, other_asset_returns in todays_returns.iteritems():
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asset_column = int(asset) - 1
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expected_regression_results = linregress(
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y=other_asset_returns, x=my_asset_returns,
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)
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for i, output in enumerate(outputs):
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expected_output_results[output][day, asset_column] = \
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expected_regression_results[i]
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for output in outputs:
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output_result = output_results[output]
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expected_output_result = DataFrame(
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where(expected_mask, expected_output_results[output], nan),
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index=dates[start_date_index:end_date_index + 1],
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columns=assets,
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)
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assert_frame_equal(output_result, expected_output_result)
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def test_correlation_and_regression_with_bad_asset(self):
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"""
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Test that `RollingPearsonOfReturns`, `RollingSpearmanOfReturns` and
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`RollingLinearRegressionOfReturns` raise the proper exception when
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given a nonexistent target asset.
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"""
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my_asset = Equity(0, exchange="TEST")
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start_date = self.pipeline_start_date
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end_date = self.pipeline_end_date
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run_pipeline = self.run_pipeline
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# This filter is arbitrary; the important thing is that we test each
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# factor both with and without a specified mask.
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my_asset_filter = AssetID().eq(1)
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for mask in (NotSpecified, my_asset_filter):
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pearson_factor = RollingPearsonOfReturns(
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target=my_asset,
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returns_length=3,
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correlation_length=3,
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mask=mask,
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)
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spearman_factor = RollingSpearmanOfReturns(
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target=my_asset,
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returns_length=3,
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correlation_length=3,
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mask=mask,
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)
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regression_factor = RollingLinearRegressionOfReturns(
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target=my_asset,
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returns_length=3,
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regression_length=3,
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mask=mask,
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)
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with self.assertRaises(NonExistentAssetInTimeFrame):
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run_pipeline(
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Pipeline(columns={'pearson_factor': pearson_factor}),
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start_date,
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end_date,
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)
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with self.assertRaises(NonExistentAssetInTimeFrame):
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run_pipeline(
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Pipeline(columns={'spearman_factor': spearman_factor}),
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start_date,
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end_date,
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)
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with self.assertRaises(NonExistentAssetInTimeFrame):
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run_pipeline(
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Pipeline(columns={'regression_factor': regression_factor}),
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start_date,
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end_date,
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)
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def test_require_length_greater_than_one(self):
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my_asset = Equity(0, exchange="TEST")
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with self.assertRaises(ValueError):
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RollingPearsonOfReturns(
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target=my_asset,
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returns_length=3,
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correlation_length=1,
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)
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with self.assertRaises(ValueError):
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RollingSpearmanOfReturns(
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target=my_asset,
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returns_length=3,
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correlation_length=1,
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)
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with self.assertRaises(ValueError):
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RollingLinearRegressionOfReturns(
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target=my_asset,
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returns_length=3,
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regression_length=1,
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)
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class StatisticalMethodsTestCase(WithSeededRandomPipelineEngine,
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ZiplineTestCase):
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sids = ASSET_FINDER_EQUITY_SIDS = Int64Index([1, 2, 3])
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START_DATE = Timestamp('2015-01-31', tz='UTC')
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END_DATE = Timestamp('2015-03-01', tz='UTC')
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@classmethod
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def init_class_fixtures(cls):
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super(StatisticalMethodsTestCase, cls).init_class_fixtures()
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# Using these start and end dates because they are a contigous span of
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# 5 days (Monday - Friday) and they allow for plenty of days to look
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# back on when computing correlations and regressions.
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cls.dates = dates = cls.trading_days
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cls.start_date_index = start_date_index = 14
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cls.end_date_index = end_date_index = 18
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cls.pipeline_start_date = cls.trading_days[start_date_index]
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cls.pipeline_end_date = cls.trading_days[end_date_index]
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sids = cls.sids
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cls.assets = assets = cls.asset_finder.retrieve_all(sids)
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cls.my_asset_column = my_asset_column = 0
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cls.my_asset = assets[my_asset_column]
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cls.num_days = num_days = end_date_index - start_date_index + 1
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cls.num_assets = num_assets = len(assets)
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cls.cascading_mask = \
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AssetIDPlusDay() < (sids[-1] + dates[start_date_index].day)
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cls.expected_cascading_mask_result = make_cascading_boolean_array(
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shape=(num_days, num_assets),
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)
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cls.alternating_mask = (AssetIDPlusDay() % 2).eq(0)
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cls.expected_alternating_mask_result = make_alternating_boolean_array(
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shape=(num_days, num_assets),
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)
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cls.expected_no_mask_result = full(
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shape=(num_days, num_assets), fill_value=True, dtype=bool_dtype,
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)
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# Random input for factors.
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cls.col = TestingDataSet.float_col
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@parameter_space(returns_length=[2, 3], correlation_length=[3, 4])
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def test_factor_correlation_methods(self,
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returns_length,
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correlation_length):
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"""
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Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
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with the built-in factors `RollingPearsonOfReturns` and
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`RollingSpearmanOfReturns`.
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"""
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my_asset = self.my_asset
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start_date = self.pipeline_start_date
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end_date = self.pipeline_end_date
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run_pipeline = self.run_pipeline
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returns = Returns(window_length=returns_length, inputs=[self.col])
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returns_slice = returns[my_asset]
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pearson = returns.pearsonr(
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target=returns_slice, correlation_length=correlation_length,
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)
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spearman = returns.spearmanr(
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target=returns_slice, correlation_length=correlation_length,
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)
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expected_pearson = RollingPearsonOfReturns(
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target=my_asset,
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returns_length=returns_length,
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correlation_length=correlation_length,
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)
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expected_spearman = RollingSpearmanOfReturns(
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target=my_asset,
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returns_length=returns_length,
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correlation_length=correlation_length,
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)
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# These built-ins construct their own Returns factor to use as inputs,
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# so the only way to set our own inputs is to do so after the fact.
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# This should not be done in practice. It is necessary here because we
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# want Returns to use our random data as an input, but by default it is
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# using USEquityPricing.close.
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expected_pearson.inputs = [returns, returns_slice]
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expected_spearman.inputs = [returns, returns_slice]
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columns = {
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'pearson': pearson,
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'spearman': spearman,
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'expected_pearson': expected_pearson,
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'expected_spearman': expected_spearman,
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}
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results = run_pipeline(Pipeline(columns=columns), start_date, end_date)
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pearson_results = results['pearson'].unstack()
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spearman_results = results['spearman'].unstack()
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expected_pearson_results = results['expected_pearson'].unstack()
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expected_spearman_results = results['expected_spearman'].unstack()
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|
assert_frame_equal(pearson_results, expected_pearson_results)
|
|
assert_frame_equal(spearman_results, expected_spearman_results)
|
|
|
|
def test_correlation_methods_bad_type(self):
|
|
"""
|
|
Make sure we cannot call the Factor correlation methods on factors or
|
|
slices that are not of float or int dtype.
|
|
"""
|
|
# These are arbitrary for the purpose of this test.
|
|
returns_length = 2
|
|
correlation_length = 10
|
|
|
|
returns = Returns(window_length=returns_length, inputs=[self.col])
|
|
returns_slice = returns[self.my_asset]
|
|
|
|
class BadTypeFactor(CustomFactor):
|
|
inputs = []
|
|
window_length = 1
|
|
dtype = datetime64ns_dtype
|
|
window_safe = True
|
|
|
|
def compute(self, today, assets, out):
|
|
pass
|
|
|
|
bad_type_factor = BadTypeFactor()
|
|
bad_type_factor_slice = bad_type_factor[self.my_asset]
|
|
|
|
with self.assertRaises(TypeError):
|
|
bad_type_factor.pearsonr(
|
|
target=returns_slice, correlation_length=correlation_length,
|
|
)
|
|
with self.assertRaises(TypeError):
|
|
bad_type_factor.spearmanr(
|
|
target=returns_slice, correlation_length=correlation_length,
|
|
)
|
|
with self.assertRaises(TypeError):
|
|
returns.pearsonr(
|
|
target=bad_type_factor_slice,
|
|
correlation_length=correlation_length,
|
|
)
|
|
with self.assertRaises(TypeError):
|
|
returns.spearmanr(
|
|
target=bad_type_factor_slice,
|
|
correlation_length=correlation_length,
|
|
)
|
|
|
|
@parameter_space(returns_length=[2, 3], regression_length=[3, 4])
|
|
def test_factor_regression_method(self, returns_length, regression_length):
|
|
"""
|
|
Ensure that `Factor.linear_regression` is consistent with the built-in
|
|
factor `RollingLinearRegressionOfReturns`.
|
|
"""
|
|
my_asset = self.my_asset
|
|
start_date = self.pipeline_start_date
|
|
end_date = self.pipeline_end_date
|
|
run_pipeline = self.run_pipeline
|
|
|
|
returns = Returns(window_length=returns_length, inputs=[self.col])
|
|
returns_slice = returns[my_asset]
|
|
|
|
regression = returns.linear_regression(
|
|
target=returns_slice, regression_length=regression_length,
|
|
)
|
|
expected_regression = RollingLinearRegressionOfReturns(
|
|
target=my_asset,
|
|
returns_length=returns_length,
|
|
regression_length=regression_length,
|
|
)
|
|
|
|
# This built-in constructs its own Returns factor to use as an input,
|
|
# so the only way to set our own input is to do so after the fact. This
|
|
# should not be done in practice. It is necessary here because we want
|
|
# Returns to use our random data as an input, but by default it is
|
|
# using USEquityPricing.close.
|
|
expected_regression.inputs = [returns, returns_slice]
|
|
|
|
columns = {
|
|
'regression': regression,
|
|
'expected_regression': expected_regression,
|
|
}
|
|
|
|
results = run_pipeline(Pipeline(columns=columns), start_date, end_date)
|
|
regression_results = results['regression'].unstack()
|
|
expected_regression_results = results['expected_regression'].unstack()
|
|
|
|
assert_frame_equal(regression_results, expected_regression_results)
|
|
|
|
def test_regression_method_bad_type(self):
|
|
"""
|
|
Make sure we cannot call the Factor linear regression method on factors
|
|
or slices that are not of float or int dtype.
|
|
"""
|
|
# These are arbitrary for the purpose of this test.
|
|
returns_length = 2
|
|
regression_length = 10
|
|
|
|
returns = Returns(window_length=returns_length, inputs=[self.col])
|
|
returns_slice = returns[self.my_asset]
|
|
|
|
class BadTypeFactor(CustomFactor):
|
|
window_length = 1
|
|
inputs = []
|
|
dtype = datetime64ns_dtype
|
|
window_safe = True
|
|
|
|
def compute(self, today, assets, out):
|
|
pass
|
|
|
|
bad_type_factor = BadTypeFactor()
|
|
bad_type_factor_slice = bad_type_factor[self.my_asset]
|
|
|
|
with self.assertRaises(TypeError):
|
|
bad_type_factor.linear_regression(
|
|
target=returns_slice, regression_length=regression_length,
|
|
)
|
|
with self.assertRaises(TypeError):
|
|
returns.linear_regression(
|
|
target=bad_type_factor_slice,
|
|
regression_length=regression_length,
|
|
)
|
|
|
|
@parameter_space(correlation_length=[2, 3, 4])
|
|
def test_factor_correlation_methods_two_factors(self, correlation_length):
|
|
"""
|
|
Tests for `Factor.pearsonr` and `Factor.spearmanr` when passed another
|
|
2D factor instead of a Slice.
|
|
"""
|
|
assets = self.assets
|
|
dates = self.dates
|
|
start_date = self.pipeline_start_date
|
|
end_date = self.pipeline_end_date
|
|
start_date_index = self.start_date_index
|
|
end_date_index = self.end_date_index
|
|
num_days = self.num_days
|
|
run_pipeline = self.run_pipeline
|
|
|
|
# Ensure that the correlation methods cannot be called with two 2D
|
|
# factors which have different masks.
|
|
returns_masked_1 = Returns(
|
|
window_length=5, inputs=[self.col], mask=AssetID().eq(1),
|
|
)
|
|
returns_masked_2 = Returns(
|
|
window_length=5, inputs=[self.col], mask=AssetID().eq(2),
|
|
)
|
|
with self.assertRaises(IncompatibleTerms):
|
|
returns_masked_1.pearsonr(
|
|
target=returns_masked_2, correlation_length=correlation_length,
|
|
)
|
|
with self.assertRaises(IncompatibleTerms):
|
|
returns_masked_1.spearmanr(
|
|
target=returns_masked_2, correlation_length=correlation_length,
|
|
)
|
|
|
|
returns_5 = Returns(window_length=5, inputs=[self.col])
|
|
returns_10 = Returns(window_length=10, inputs=[self.col])
|
|
|
|
pearson_factor = returns_5.pearsonr(
|
|
target=returns_10, correlation_length=correlation_length,
|
|
)
|
|
spearman_factor = returns_5.spearmanr(
|
|
target=returns_10, correlation_length=correlation_length,
|
|
)
|
|
|
|
columns = {
|
|
'pearson_factor': pearson_factor,
|
|
'spearman_factor': spearman_factor,
|
|
}
|
|
pipeline = Pipeline(columns=columns)
|
|
|
|
results = run_pipeline(pipeline, start_date, end_date)
|
|
pearson_results = results['pearson_factor'].unstack()
|
|
spearman_results = results['spearman_factor'].unstack()
|
|
|
|
# Run a separate pipeline that calculates returns starting
|
|
# (correlation_length - 1) days prior to our start date. This is
|
|
# because we need (correlation_length - 1) extra days of returns to
|
|
# compute our expected correlations.
|
|
columns = {'returns_5': returns_5, 'returns_10': returns_10}
|
|
results = run_pipeline(
|
|
Pipeline(columns=columns),
|
|
dates[start_date_index - (correlation_length - 1)],
|
|
dates[end_date_index],
|
|
)
|
|
returns_5_results = results['returns_5'].unstack()
|
|
returns_10_results = results['returns_10'].unstack()
|
|
|
|
# On each day, calculate the expected correlation coefficients
|
|
# between each asset's 5 and 10 day rolling returns. Each correlation
|
|
# is calculated over `correlation_length` days.
|
|
expected_pearson_results = full_like(pearson_results, nan)
|
|
expected_spearman_results = full_like(spearman_results, nan)
|
|
for day in range(num_days):
|
|
todays_returns_5 = returns_5_results.iloc[
|
|
day:day + correlation_length
|
|
]
|
|
todays_returns_10 = returns_10_results.iloc[
|
|
day:day + correlation_length
|
|
]
|
|
for asset, asset_returns_5 in todays_returns_5.iteritems():
|
|
asset_column = int(asset) - 1
|
|
asset_returns_10 = todays_returns_10[asset]
|
|
expected_pearson_results[day, asset_column] = pearsonr(
|
|
asset_returns_5, asset_returns_10,
|
|
)[0]
|
|
expected_spearman_results[day, asset_column] = spearmanr(
|
|
asset_returns_5, asset_returns_10,
|
|
)[0]
|
|
|
|
expected_pearson_results = DataFrame(
|
|
data=expected_pearson_results,
|
|
index=dates[start_date_index:end_date_index + 1],
|
|
columns=assets,
|
|
)
|
|
assert_frame_equal(pearson_results, expected_pearson_results)
|
|
|
|
expected_spearman_results = DataFrame(
|
|
data=expected_spearman_results,
|
|
index=dates[start_date_index:end_date_index + 1],
|
|
columns=assets,
|
|
)
|
|
assert_frame_equal(spearman_results, expected_spearman_results)
|
|
|
|
@parameter_space(regression_length=[2, 3, 4])
|
|
def test_factor_regression_method_two_factors(self, regression_length):
|
|
"""
|
|
Tests for `Factor.linear_regression` when passed another 2D factor
|
|
instead of a Slice.
|
|
"""
|
|
assets = self.assets
|
|
dates = self.dates
|
|
start_date = self.pipeline_start_date
|
|
end_date = self.pipeline_end_date
|
|
start_date_index = self.start_date_index
|
|
end_date_index = self.end_date_index
|
|
num_days = self.num_days
|
|
run_pipeline = self.run_pipeline
|
|
|
|
# The order of these is meant to align with the output of `linregress`.
|
|
outputs = ['beta', 'alpha', 'r_value', 'p_value', 'stderr']
|
|
|
|
# Ensure that the `linear_regression` method cannot be called with two
|
|
# 2D factors which have different masks.
|
|
returns_masked_1 = Returns(
|
|
window_length=5, inputs=[self.col], mask=AssetID().eq(1),
|
|
)
|
|
returns_masked_2 = Returns(
|
|
window_length=5, inputs=[self.col], mask=AssetID().eq(2),
|
|
)
|
|
with self.assertRaises(IncompatibleTerms):
|
|
returns_masked_1.linear_regression(
|
|
target=returns_masked_2, regression_length=regression_length,
|
|
)
|
|
|
|
returns_5 = Returns(window_length=5, inputs=[self.col])
|
|
returns_10 = Returns(window_length=10, inputs=[self.col])
|
|
|
|
regression_factor = returns_5.linear_regression(
|
|
target=returns_10, regression_length=regression_length,
|
|
)
|
|
|
|
columns = {
|
|
output: getattr(regression_factor, output)
|
|
for output in outputs
|
|
}
|
|
pipeline = Pipeline(columns=columns)
|
|
|
|
results = run_pipeline(pipeline, start_date, end_date)
|
|
|
|
output_results = {}
|
|
expected_output_results = {}
|
|
for output in outputs:
|
|
output_results[output] = results[output].unstack()
|
|
expected_output_results[output] = full_like(
|
|
output_results[output], nan,
|
|
)
|
|
|
|
# Run a separate pipeline that calculates returns starting
|
|
# (regression_length - 1) days prior to our start date. This is because
|
|
# we need (regression_length - 1) extra days of returns to compute our
|
|
# expected regressions.
|
|
columns = {'returns_5': returns_5, 'returns_10': returns_10}
|
|
results = run_pipeline(
|
|
Pipeline(columns=columns),
|
|
dates[start_date_index - (regression_length - 1)],
|
|
dates[end_date_index],
|
|
)
|
|
returns_5_results = results['returns_5'].unstack()
|
|
returns_10_results = results['returns_10'].unstack()
|
|
|
|
# On each day, for each asset, calculate the expected regression
|
|
# results of Y ~ X where Y is the asset's rolling 5 day returns and X
|
|
# is the asset's rolling 10 day returns. Each regression is calculated
|
|
# over `regression_length` days of data.
|
|
for day in range(num_days):
|
|
todays_returns_5 = returns_5_results.iloc[
|
|
day:day + regression_length
|
|
]
|
|
todays_returns_10 = returns_10_results.iloc[
|
|
day:day + regression_length
|
|
]
|
|
for asset, asset_returns_5 in todays_returns_5.iteritems():
|
|
asset_column = int(asset) - 1
|
|
asset_returns_10 = todays_returns_10[asset]
|
|
expected_regression_results = linregress(
|
|
y=asset_returns_5, x=asset_returns_10,
|
|
)
|
|
for i, output in enumerate(outputs):
|
|
expected_output_results[output][day, asset_column] = \
|
|
expected_regression_results[i]
|
|
|
|
for output in outputs:
|
|
output_result = output_results[output]
|
|
expected_output_result = DataFrame(
|
|
expected_output_results[output],
|
|
index=dates[start_date_index:end_date_index + 1],
|
|
columns=assets,
|
|
)
|
|
assert_frame_equal(output_result, expected_output_result)
|