mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-15 11:22:18 +08:00
Joe Jevnik
59c8e371a2
Adds the data bundle concept which makes it easy for users to register loading functions to build out minute and daily data along with an assets db and adjustments db. By default we have provided a `quandl` bundle which pulls from the public domain WIKI dataset. Users may register new bundles by decorating an ingest function with `zipline.data.bundles.register(<name>)`. This also provides a `yahoo_equities` function for creating an ingestion function that will load a static set of assets from yahoo. The cli is now structured as a couple of subcommands and has been changed to `python -m zipline`. The old behavior of `run_algo.py` has been moved to the `run` subcommand. This is almost entirely the same except that it now takes the name of the data bundle to use, defaulting to `quandl`. The next subcommand is `ingest` which takes the name of a data bundle to ingest. This will run the loading machinery and write the data to a specified location that `run` can find. There is also a `clean` subcommand which deletes the data that was written with `ingest`. Extensions have also been added to zipline. This is an experimental feature where users can provide an extra set of python files to run at the start of the process. These can be used to configure aspects of zipline. Right now the only thing that is supported in an extension file is the registration of a new data bundle.
560 lines
20 KiB
Python
560 lines
20 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 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 toolz.curried.operator import getitem
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from zipline.lib.adjustment import Float64Multiply
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from zipline.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 zipline.pipeline.loaders.equity_pricing_loader import (
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USEquityPricingLoader,
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)
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from zipline.errors import WindowLengthTooLong
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from zipline.pipeline.data import USEquityPricing
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from zipline.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 zipline.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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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_daily_bar_reader(cls):
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return MockDailyBarReader()
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@classmethod
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def make_daily_bar_data(cls):
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return make_bar_data(
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EQUITY_INFO,
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cls.bcolz_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.bcolz_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.bcolz_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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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_daily_bar_reader,
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adjustment_reader,
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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(
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shifted_query_days,
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self.asset_info,
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'close',
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)
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expected_baseline_volumes = expected_bar_values_2d(
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shifted_query_days,
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self.asset_info,
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'volume',
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)
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# AdjustedArrays should yield the same data as the expected baseline.
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for windowlen in range(1, len(query_days) + 1):
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for offset, window in enumerate(closes.traverse(windowlen)):
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assert_array_equal(
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expected_baseline_closes[offset:offset + windowlen],
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window,
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)
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for offset, window in enumerate(volumes.traverse(windowlen)):
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assert_array_equal(
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expected_baseline_volumes[offset:offset + windowlen],
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window,
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)
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# Verify that we checked up to the longest possible window.
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with self.assertRaises(WindowLengthTooLong):
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closes.traverse(windowlen + 1)
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with self.assertRaises(WindowLengthTooLong):
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volumes.traverse(windowlen + 1)
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def apply_adjustments(self, dates, assets, baseline_values, adjustments):
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min_date, max_date = dates[[0, -1]]
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# 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_daily_bar_reader,
|
|
self.adjustment_reader,
|
|
)
|
|
|
|
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)
|