mirror of
https://github.com/wassname/catalyst.git
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Conner Fromknecht
616 lines
22 KiB
Python
616 lines
22 KiB
Python
#
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# Copyright 2015 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Tests for USEquityPricingLoader and related classes.
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"""
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from nose_parameterized import parameterized
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from numpy import (
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arange,
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datetime64,
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float64,
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ones,
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uint32,
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)
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from numpy.testing import (
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assert_allclose,
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assert_array_equal,
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)
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from pandas import (
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concat,
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DataFrame,
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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 toolz.curried.operator import getitem
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from catalyst.lib.adjustment import Float64Multiply
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from catalyst.pipeline.loaders.synthetic import (
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NullAdjustmentReader,
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make_bar_data,
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expected_bar_values_2d,
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)
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from catalyst.pipeline.loaders.equity_pricing_loader import (
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USEquityPricingLoader,
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)
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from catalyst.errors import WindowLengthTooLong
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from catalyst.pipeline.data import USEquityPricing
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from catalyst.testing import (
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seconds_to_timestamp,
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str_to_seconds,
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MockDailyBarReader,
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)
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from catalyst.testing.fixtures import (
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WithAdjustmentReader,
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ZiplineTestCase,
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)
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# Test calendar ranges over the month of June 2015
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# June 2015
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# Mo Tu We Th Fr Sa Su
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# 1 2 3 4 5 6 7
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# 8 9 10 11 12 13 14
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# 15 16 17 18 19 20 21
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# 22 23 24 25 26 27 28
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# 29 30
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TEST_CALENDAR_START = Timestamp('2015-06-01', tz='UTC')
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TEST_CALENDAR_STOP = Timestamp('2015-06-30', tz='UTC')
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TEST_QUERY_START = Timestamp('2015-06-10', tz='UTC')
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TEST_QUERY_STOP = Timestamp('2015-06-19', tz='UTC')
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# One asset for each of the cases enumerated in load_raw_arrays_from_bcolz.
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EQUITY_INFO = DataFrame(
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[
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# 1) The equity's trades start and end before query.
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{'start_date': '2015-06-01', 'end_date': '2015-06-05'},
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# 2) The equity's trades start and end after query.
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{'start_date': '2015-06-22', 'end_date': '2015-06-30'},
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# 3) The equity's data covers all dates in range.
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{'start_date': '2015-06-02', 'end_date': '2015-06-30'},
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# 4) The equity's trades start before the query start, but stop
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# before the query end.
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{'start_date': '2015-06-01', 'end_date': '2015-06-15'},
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# 5) The equity's trades start and end during the query.
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{'start_date': '2015-06-12', 'end_date': '2015-06-18'},
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# 6) The equity's trades start during the query, but extend through
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# the whole query.
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{'start_date': '2015-06-15', 'end_date': '2015-06-25'},
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],
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index=arange(1, 7),
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columns=['start_date', 'end_date'],
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).astype(datetime64)
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EQUITY_INFO['symbol'] = [chr(ord('A') + n) for n in range(len(EQUITY_INFO))]
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TEST_QUERY_ASSETS = EQUITY_INFO.index
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# ADJUSTMENTS use the following scheme to indicate information about the value
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# upon inspection.
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#
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# 1s place is the equity
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#
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# 0.1s place is the action type, with:
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#
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# splits, 1
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# mergers, 2
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# dividends, 3
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#
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# 0.001s is the date
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SPLITS = DataFrame(
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[
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# Before query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-03'),
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'ratio': 1.103,
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'sid': 1},
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# First day of query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-10'),
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'ratio': 3.110,
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'sid': 3},
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# Third day of query range, should have last_row of 2
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{'effective_date': str_to_seconds('2015-06-12'),
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'ratio': 3.112,
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'sid': 3},
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# After query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-21'),
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'ratio': 6.121,
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'sid': 6},
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# Another action in query range, should have last_row of 1
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{'effective_date': str_to_seconds('2015-06-11'),
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'ratio': 3.111,
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'sid': 3},
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# Last day of range. Should have last_row of 7
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{'effective_date': str_to_seconds('2015-06-19'),
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'ratio': 3.119,
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'sid': 3},
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],
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columns=['effective_date', 'ratio', 'sid'],
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)
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MERGERS = DataFrame(
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[
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# Before query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-03'),
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'ratio': 1.203,
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'sid': 1},
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# First day of query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-10'),
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'ratio': 3.210,
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'sid': 3},
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# Third day of query range, should have last_row of 2
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{'effective_date': str_to_seconds('2015-06-12'),
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'ratio': 3.212,
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'sid': 3},
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# After query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-25'),
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'ratio': 6.225,
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'sid': 6},
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# Another action in query range, should have last_row of 2
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{'effective_date': str_to_seconds('2015-06-12'),
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'ratio': 4.212,
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'sid': 4},
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# Last day of range. Should have last_row of 7
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{'effective_date': str_to_seconds('2015-06-19'),
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'ratio': 3.219,
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'sid': 3},
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],
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columns=['effective_date', 'ratio', 'sid'],
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)
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DIVIDENDS = DataFrame(
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[
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# Before query range, should be excluded.
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{'declared_date': Timestamp('2015-05-01', tz='UTC').to_datetime64(),
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'ex_date': Timestamp('2015-06-01', tz='UTC').to_datetime64(),
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'record_date': Timestamp('2015-06-03', tz='UTC').to_datetime64(),
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'pay_date': Timestamp('2015-06-05', tz='UTC').to_datetime64(),
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'amount': 90.0,
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'sid': 1},
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# First day of query range, should be excluded.
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{'declared_date': Timestamp('2015-06-01', tz='UTC').to_datetime64(),
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'ex_date': Timestamp('2015-06-10', tz='UTC').to_datetime64(),
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'record_date': Timestamp('2015-06-15', tz='UTC').to_datetime64(),
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'pay_date': Timestamp('2015-06-17', tz='UTC').to_datetime64(),
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'amount': 80.0,
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'sid': 3},
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# Third day of query range, should have last_row of 2
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{'declared_date': Timestamp('2015-06-01', tz='UTC').to_datetime64(),
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'ex_date': Timestamp('2015-06-12', tz='UTC').to_datetime64(),
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'record_date': Timestamp('2015-06-15', tz='UTC').to_datetime64(),
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'pay_date': Timestamp('2015-06-17', tz='UTC').to_datetime64(),
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'amount': 70.0,
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'sid': 3},
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# After query range, should be excluded.
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{'declared_date': Timestamp('2015-06-01', tz='UTC').to_datetime64(),
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'ex_date': Timestamp('2015-06-25', tz='UTC').to_datetime64(),
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'record_date': Timestamp('2015-06-28', tz='UTC').to_datetime64(),
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'pay_date': Timestamp('2015-06-30', tz='UTC').to_datetime64(),
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'amount': 60.0,
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'sid': 6},
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# Another action in query range, should have last_row of 3
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{'declared_date': Timestamp('2015-06-01', tz='UTC').to_datetime64(),
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'ex_date': Timestamp('2015-06-15', tz='UTC').to_datetime64(),
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'record_date': Timestamp('2015-06-18', tz='UTC').to_datetime64(),
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'pay_date': Timestamp('2015-06-20', tz='UTC').to_datetime64(),
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'amount': 50.0,
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'sid': 3},
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# Last day of range. Should have last_row of 7
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{'declared_date': Timestamp('2015-06-01', tz='UTC').to_datetime64(),
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'ex_date': Timestamp('2015-06-19', tz='UTC').to_datetime64(),
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'record_date': Timestamp('2015-06-22', tz='UTC').to_datetime64(),
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'pay_date': Timestamp('2015-06-30', tz='UTC').to_datetime64(),
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'amount': 40.0,
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'sid': 3},
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],
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columns=['declared_date',
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'ex_date',
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'record_date',
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'pay_date',
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'amount',
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'sid'],
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)
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DIVIDENDS_EXPECTED = DataFrame(
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[
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# Before query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-01'),
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'ratio': 0.1,
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'sid': 1},
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# First day of query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-10'),
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'ratio': 0.20,
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'sid': 3},
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# Third day of query range, should have last_row of 2
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{'effective_date': str_to_seconds('2015-06-12'),
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'ratio': 0.30,
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'sid': 3},
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# After query range, should be excluded.
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{'effective_date': str_to_seconds('2015-06-25'),
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'ratio': 0.40,
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'sid': 6},
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# Another action in query range, should have last_row of 3
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{'effective_date': str_to_seconds('2015-06-15'),
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'ratio': 0.50,
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'sid': 3},
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# Last day of range. Should have last_row of 7
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{'effective_date': str_to_seconds('2015-06-19'),
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'ratio': 0.60,
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'sid': 3},
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],
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columns=['effective_date', 'ratio', 'sid'],
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)
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class USEquityPricingLoaderTestCase(WithAdjustmentReader,
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ZiplineTestCase):
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START_DATE = TEST_CALENDAR_START
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END_DATE = TEST_CALENDAR_STOP
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asset_ids = 1, 2, 3
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@classmethod
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def make_equity_info(cls):
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return EQUITY_INFO
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@classmethod
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def make_splits_data(cls):
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return SPLITS
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@classmethod
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def make_mergers_data(cls):
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return MERGERS
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@classmethod
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def make_dividends_data(cls):
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return DIVIDENDS
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@classmethod
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def make_adjustment_writer_equity_daily_bar_reader(cls):
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return MockDailyBarReader()
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@classmethod
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def make_equity_daily_bar_data(cls):
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return make_bar_data(
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EQUITY_INFO,
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cls.equity_daily_bar_days,
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)
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@classmethod
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def init_class_fixtures(cls):
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super(USEquityPricingLoaderTestCase, cls).init_class_fixtures()
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cls.assets = TEST_QUERY_ASSETS
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cls.asset_info = EQUITY_INFO
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def test_input_sanity(self):
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# Ensure that the input data doesn't contain adjustments during periods
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# where the corresponding asset didn't exist.
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for table in SPLITS, MERGERS:
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for eff_date_secs, _, sid in table.itertuples(index=False):
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eff_date = Timestamp(eff_date_secs, unit='s')
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asset_start, asset_end = EQUITY_INFO.ix[
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sid, ['start_date', 'end_date']
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]
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self.assertGreaterEqual(eff_date, asset_start)
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self.assertLessEqual(eff_date, asset_end)
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def calendar_days_between(self, start_date, end_date, shift=0):
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slice_ = self.equity_daily_bar_days.slice_indexer(start_date, end_date)
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start = slice_.start + shift
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stop = slice_.stop + shift
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if start < 0:
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raise KeyError(start_date, shift)
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return self.equity_daily_bar_days[start:stop]
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def expected_adjustments(self, start_date, end_date):
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price_adjustments = {}
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volume_adjustments = {}
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query_days = self.calendar_days_between(start_date, end_date)
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start_loc = query_days.get_loc(start_date)
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for table in SPLITS, MERGERS, DIVIDENDS_EXPECTED:
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for eff_date_secs, ratio, sid in table.itertuples(index=False):
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eff_date = Timestamp(eff_date_secs, unit='s', tz='UTC')
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# Ignore adjustments outside the query bounds.
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if not (start_date <= eff_date <= end_date):
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continue
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eff_date_loc = query_days.get_loc(eff_date)
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delta = eff_date_loc - start_loc
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# Pricing adjustments should be applied on the date
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# corresponding to the effective date of the input data. They
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# should affect all rows **before** the effective date.
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price_adjustments.setdefault(delta, []).append(
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Float64Multiply(
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first_row=0,
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last_row=delta,
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first_col=sid - 1,
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last_col=sid - 1,
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value=ratio,
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)
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)
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# Volume is *inversely* affected by *splits only*.
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if table is SPLITS:
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volume_adjustments.setdefault(delta, []).append(
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Float64Multiply(
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first_row=0,
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last_row=delta,
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first_col=sid - 1,
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last_col=sid - 1,
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value=1.0 / ratio,
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)
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)
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return price_adjustments, volume_adjustments
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def test_load_adjustments_from_sqlite(self):
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columns = [USEquityPricing.close, USEquityPricing.volume]
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query_days = self.calendar_days_between(
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TEST_QUERY_START,
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TEST_QUERY_STOP,
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)
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adjustments = self.adjustment_reader.load_adjustments(
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[c.name for c in columns],
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query_days,
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self.assets,
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)
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close_adjustments = adjustments[0]
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volume_adjustments = adjustments[1]
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expected_close_adjustments, expected_volume_adjustments = \
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self.expected_adjustments(TEST_QUERY_START, TEST_QUERY_STOP)
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for key in expected_close_adjustments:
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close_adjustment = close_adjustments[key]
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for j, adj in enumerate(close_adjustment):
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expected = expected_close_adjustments[key][j]
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self.assertEqual(adj.first_row, expected.first_row)
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self.assertEqual(adj.last_row, expected.last_row)
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self.assertEqual(adj.first_col, expected.first_col)
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self.assertEqual(adj.last_col, expected.last_col)
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assert_allclose(adj.value, expected.value)
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for key in expected_volume_adjustments:
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volume_adjustment = volume_adjustments[key]
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for j, adj in enumerate(volume_adjustment):
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expected = expected_volume_adjustments[key][j]
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self.assertEqual(adj.first_row, expected.first_row)
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self.assertEqual(adj.last_row, expected.last_row)
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self.assertEqual(adj.first_col, expected.first_col)
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self.assertEqual(adj.last_col, expected.last_col)
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assert_allclose(adj.value, expected.value)
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@parameterized([(True,), (False,)])
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def test_load_adjustments_to_df(self, convert_dts):
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reader = self.adjustment_reader
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adjustment_dfs = reader.unpack_db_to_component_dfs(
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convert_dates=convert_dts
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)
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name_and_raw = (
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('splits', SPLITS),
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('mergers', MERGERS),
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('dividends', DIVIDENDS_EXPECTED)
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)
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def create_expected_table(df, name):
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expected_df = df.copy()
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if convert_dts:
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for colname in reader._datetime_int_cols[name]:
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expected_df[colname] = expected_df[colname].astype(
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'datetime64[s]'
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)
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return expected_df
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def create_expected_div_table(df, name):
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expected_df = df.copy()
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if not convert_dts:
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for colname in reader._datetime_int_cols[name]:
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expected_df[colname] = expected_df[colname].astype(
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'datetime64[s]'
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).astype(int)
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return expected_df
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for action_name, raw_tbl in name_and_raw:
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exp = create_expected_table(raw_tbl, action_name)
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assert_frame_equal(
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adjustment_dfs[action_name],
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exp
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)
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# DIVIDENDS is in the opposite form from the rest of the dataframes, so
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# needs to be converted separately.
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div_name = 'dividend_payouts'
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assert_frame_equal(
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adjustment_dfs[div_name],
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create_expected_div_table(DIVIDENDS, div_name)
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)
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def test_read_no_adjustments(self):
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adjustment_reader = NullAdjustmentReader()
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columns = [USEquityPricing.close, USEquityPricing.volume]
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query_days = self.calendar_days_between(
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TEST_QUERY_START,
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TEST_QUERY_STOP
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)
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# Our expected results for each day are based on values from the
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# previous day.
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shifted_query_days = self.calendar_days_between(
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TEST_QUERY_START,
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TEST_QUERY_STOP,
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shift=-1,
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)
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adjustments = adjustment_reader.load_adjustments(
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[c.name for c in columns],
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query_days,
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self.assets,
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)
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self.assertEqual(adjustments, [{}, {}])
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pricing_loader = USEquityPricingLoader(
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self.bcolz_equity_daily_bar_reader,
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adjustment_reader,
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USEquityPricing,
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)
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results = pricing_loader.load_adjusted_array(
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columns,
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dates=query_days,
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assets=self.assets,
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mask=ones((len(query_days), len(self.assets)), dtype=bool),
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)
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closes, volumes = map(getitem(results), columns)
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|
expected_baseline_closes = expected_bar_values_2d(
|
|
shifted_query_days,
|
|
self.asset_info,
|
|
'close',
|
|
)
|
|
expected_baseline_volumes = expected_bar_values_2d(
|
|
shifted_query_days,
|
|
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)
|
|
|
|
def apply_adjustments(self, dates, assets, baseline_values, adjustments):
|
|
min_date, max_date = dates[[0, -1]]
|
|
# HACK: Simulate the coercion to float64 we do in adjusted_array. This
|
|
# should be removed when AdjustedArray properly supports
|
|
# non-floating-point types.
|
|
orig_dtype = baseline_values.dtype
|
|
values = baseline_values.astype(float64).copy()
|
|
for eff_date_secs, ratio, sid in adjustments.itertuples(index=False):
|
|
eff_date = seconds_to_timestamp(eff_date_secs)
|
|
# Don't apply adjustments that aren't in the current date range.
|
|
if eff_date not in dates:
|
|
continue
|
|
eff_date_loc = dates.get_loc(eff_date)
|
|
asset_col = assets.get_loc(sid)
|
|
# Apply ratio multiplicatively to the asset column on all rows less
|
|
# than or equal adjustment effective date.
|
|
values[:eff_date_loc + 1, asset_col] *= ratio
|
|
return values.astype(orig_dtype)
|
|
|
|
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(
|
|
self.bcolz_equity_daily_bar_reader,
|
|
self.adjustment_reader,
|
|
USEquityPricing,
|
|
)
|
|
|
|
results = pricing_loader.load_adjusted_array(
|
|
columns,
|
|
dates=query_days,
|
|
assets=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.asset_info,
|
|
'high',
|
|
)
|
|
expected_baseline_volumes = expected_bar_values_2d(
|
|
shifted_query_days,
|
|
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.assets,
|
|
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.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)
|