def test_read_no_adjustments(self):
adjustment_reader = NullAdjustmentReader()
columns = [USEquityPricing.close, USEquityPricing.volume]
query_days = self.calendar_days_between(TEST_QUERY_START, TEST_QUERY_STOP)
# Our expected results for each day are based on values from the
# previous day.
shifted_query_days = self.calendar_days_between(TEST_QUERY_START, TEST_QUERY_STOP, shift=-1)
adjustments = adjustment_reader.load_adjustments(columns, query_days, self.assets)
self.assertEqual(adjustments, [{}, {}])
baseline_reader = BcolzDailyBarReader(self.bcolz_path)
pricing_loader = USEquityPricingLoader(baseline_reader, adjustment_reader)
closes, volumes = pricing_loader.load_adjusted_array(
columns, dates=query_days, assets=self.assets, mask=ones((len(query_days), len(self.assets)), dtype=bool)
)
expected_baseline_closes = self.bcolz_writer.expected_values_2d(shifted_query_days, self.assets, "close")
expected_baseline_volumes = self.bcolz_writer.expected_values_2d(shifted_query_days, self.assets, "volume")
# AdjustedArrays should yield the same data as the expected baseline.
for windowlen in range(1, len(query_days) + 1):
for offset, window in enumerate(closes.traverse(windowlen)):
assert_array_equal(expected_baseline_closes[offset : offset + windowlen], window)
for offset, window in enumerate(volumes.traverse(windowlen)):
assert_array_equal(expected_baseline_volumes[offset : offset + windowlen], window)
# Verify that we checked up to the longest possible window.
with self.assertRaises(WindowLengthTooLong):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
def 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
)
adjustments = adjustment_reader.load_adjustments(
columns,
query_days,
self.assets,
)
self.assertEqual(adjustments, [{}, {}])
baseline_reader = BcolzDailyBarReader(self.bcolz_path)
pricing_loader = USEquityPricingLoader(
baseline_reader,
adjustment_reader,
)
closes, volumes = pricing_loader.load_adjusted_array(
columns,
DataFrame(True, index=query_days, columns=self.assets),
)
expected_baseline_closes = self.bcolz_writer.expected_values_2d(
query_days,
self.assets,
'close',
)
expected_baseline_volumes = self.bcolz_writer.expected_values_2d(
query_days,
self.assets,
'volume',
)
# AdjustedArrays should yield the same data as the expected baseline.
for windowlen in range(1, len(query_days) + 1):
for offset, window in enumerate(closes.traverse(windowlen)):
assert_array_equal(
expected_baseline_closes[offset:offset + windowlen],
window,
)
for offset, window in enumerate(volumes.traverse(windowlen)):
assert_array_equal(
expected_baseline_volumes[offset:offset + windowlen],
window,
)
# Verify that we checked up to the longest possible window.
with self.assertRaises(WindowLengthTooLong):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
def 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)
adjustments = adjustment_reader.load_adjustments(
columns,
query_days,
self.assets,
)
self.assertEqual(adjustments, [{}, {}])
baseline_reader = BcolzDailyBarReader(self.bcolz_path)
pricing_loader = USEquityPricingLoader(
baseline_reader,
adjustment_reader,
)
closes, volumes = pricing_loader.load_adjusted_array(
columns,
DataFrame(True, index=query_days, columns=self.assets),
)
expected_baseline_closes = self.bcolz_writer.expected_values_2d(
query_days,
self.assets,
'close',
)
expected_baseline_volumes = self.bcolz_writer.expected_values_2d(
query_days,
self.assets,
'volume',
)
# AdjustedArrays should yield the same data as the expected baseline.
for windowlen in range(1, len(query_days) + 1):
for offset, window in enumerate(closes.traverse(windowlen)):
assert_array_equal(
expected_baseline_closes[offset:offset + windowlen],
window,
)
for offset, window in enumerate(volumes.traverse(windowlen)):
assert_array_equal(
expected_baseline_volumes[offset:offset + windowlen],
window,
)
# Verify that we checked up to the longest possible window.
with self.assertRaises(WindowLengthTooLong):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
def 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)
baseline_reader = BcolzDailyBarReader(self.bcolz_path)
adjustment_reader = SQLiteAdjustmentReader(self.db_path)
pricing_loader = USEquityPricingLoader(baseline_reader, adjustment_reader)
highs, volumes = pricing_loader.load_adjusted_array(
columns, dates=query_days, assets=Int64Index(arange(1, 7)), mask=ones((len(query_days), 6), dtype=bool)
)
expected_baseline_highs = self.bcolz_writer.expected_values_2d(shifted_query_days, self.assets, "high")
expected_baseline_volumes = self.bcolz_writer.expected_values_2d(shifted_query_days, self.assets, "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.assets,
baseline,
# Apply all adjustments.
concat([SPLITS, MERGERS, DIVIDENDS], 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.assets, 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_with_adjustments(self):
columns = [USEquityPricing.high, USEquityPricing.volume]
query_days = self.calendar_days_between(TEST_QUERY_START,
TEST_QUERY_STOP)
baseline_reader = BcolzDailyBarReader(self.bcolz_path)
adjustment_reader = SQLiteAdjustmentReader(self.db_path)
pricing_loader = USEquityPricingLoader(
baseline_reader,
adjustment_reader,
)
closes, volumes = pricing_loader.load_adjusted_array(
columns,
DataFrame(True, index=query_days, columns=arange(1, 7)),
)
expected_baseline_highs = self.bcolz_writer.expected_values_2d(
query_days,
self.assets,
'high',
)
expected_baseline_volumes = self.bcolz_writer.expected_values_2d(
query_days,
self.assets,
'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(closes.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.assets,
baseline,
# Apply all adjustments.
concat([SPLITS, MERGERS, DIVIDENDS], 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.assets,
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):
closes.traverse(windowlen + 1)
with self.assertRaises(WindowLengthTooLong):
volumes.traverse(windowlen + 1)
