def test_read_with_adjustments(self): columns = [USEquityPricing.high, USEquityPricing.volume] query_days = self.calendar_days_between(TEST_QUERY_START, TEST_QUERY_STOP) # Our expected results for each day are based on values from the # previous day. shifted_query_days = self.calendar_days_between( TEST_QUERY_START, TEST_QUERY_STOP, shift=-1, ) pricing_loader = USEquityPricingLoader.without_fx( self.bcolz_equity_daily_bar_reader, self.adjustment_reader, ) results = pricing_loader.load_adjusted_array( domain=US_EQUITIES, columns=columns, dates=query_days, sids=Int64Index(arange(1, 7)), mask=ones((len(query_days), 6), dtype=bool), ) highs, volumes = map(getitem(results), columns) expected_baseline_highs = expected_bar_values_2d( shifted_query_days, self.sids, self.asset_info, 'high', ) expected_baseline_volumes = expected_bar_values_2d( shifted_query_days, self.sids, self.asset_info, 'volume', ) # At each point in time, the AdjustedArrays should yield the baseline # with all adjustments up to that date applied. for windowlen in range(1, len(query_days) + 1): for offset, window in enumerate(highs.traverse(windowlen)): baseline = expected_baseline_highs[offset:offset + windowlen] baseline_dates = query_days[offset:offset + windowlen] expected_adjusted_highs = self.apply_adjustments( baseline_dates, self.sids, baseline, # Apply all adjustments. concat([SPLITS, MERGERS, DIVIDENDS_EXPECTED], ignore_index=True), ) assert_allclose(expected_adjusted_highs, window) for offset, window in enumerate(volumes.traverse(windowlen)): baseline = expected_baseline_volumes[offset:offset + windowlen] baseline_dates = query_days[offset:offset + windowlen] # Apply only splits and invert the ratio. adjustments = SPLITS.copy() adjustments.ratio = 1 / adjustments.ratio expected_adjusted_volumes = self.apply_adjustments( baseline_dates, self.sids, baseline, adjustments, ) # FIXME: Make AdjustedArray properly support integral types. assert_array_equal( expected_adjusted_volumes, window.astype(uint32), ) # Verify that we checked up to the longest possible window. with self.assertRaises(WindowLengthTooLong): highs.traverse(windowlen + 1) with self.assertRaises(WindowLengthTooLong): volumes.traverse(windowlen + 1)
def test_read_no_adjustments(self): adjustment_reader = NullAdjustmentReader() columns = [USEquityPricing.close, USEquityPricing.volume] query_days = self.calendar_days_between(TEST_QUERY_START, TEST_QUERY_STOP) # Our expected results for each day are based on values from the # previous day. shifted_query_days = self.calendar_days_between( TEST_QUERY_START, TEST_QUERY_STOP, shift=-1, ) adjustments = adjustment_reader.load_pricing_adjustments( [c.name for c in columns], query_days, self.sids, ) self.assertEqual(adjustments, [{}, {}]) pricing_loader = USEquityPricingLoader.without_fx( self.bcolz_equity_daily_bar_reader, adjustment_reader, ) results = pricing_loader.load_adjusted_array( domain=US_EQUITIES, columns=columns, dates=query_days, sids=self.sids, mask=ones((len(query_days), len(self.sids)), dtype=bool), ) closes, volumes = map(getitem(results), columns) expected_baseline_closes = expected_bar_values_2d( shifted_query_days, self.sids, self.asset_info, 'close', ) expected_baseline_volumes = expected_bar_values_2d( shifted_query_days, self.sids, self.asset_info, 'volume', ) # AdjustedArrays should yield the same data as the expected baseline. for windowlen in range(1, len(query_days) + 1): for offset, window in enumerate(closes.traverse(windowlen)): assert_array_equal( expected_baseline_closes[offset:offset + windowlen], window, ) for offset, window in enumerate(volumes.traverse(windowlen)): assert_array_equal( expected_baseline_volumes[offset:offset + windowlen], window, ) # Verify that we checked up to the longest possible window. with self.assertRaises(WindowLengthTooLong): closes.traverse(windowlen + 1) with self.assertRaises(WindowLengthTooLong): volumes.traverse(windowlen + 1)