mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 23:08:01 +08:00
Scott Sanderson
26fd6fda8b
- Fixes an error where Modeling API data known as of the close of `day N` would be shown to algorithms during `before_trading_start` as of the close of the same day. Algorithms should now only receive data during `before_trading_start/handle_data` that was known as of the simulation time at which the function would be called. - All Term instances now have a `mask` attribute that must be a `Filter` or an instance of `AssetExists()`. `mask` can be used to specify that a Factor should be computed in a manner that ignores the values that were not `True` in the mask. - Changed the interface for `FFCLoader.load_adjusted_array` and `Term._compute` from `(columns, mask)`, with mask as a DataFrame, to `(columns, dates, assets, mask)`, where mask is a numpy array. This is primarily to avoid having to reconstruct extra DataFrames when using masks produced by non `AssetExists` filters. - Adds `BoundColumn.latest`, which gives the most-recently-known value of a column.
673 lines
24 KiB
Python
673 lines
24 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 zipline.data.ffc.loaders.us_equity_pricing
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"""
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from unittest import TestCase
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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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DatetimeIndex,
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Int64Index,
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Timestamp,
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)
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from pandas.util.testing import assert_index_equal
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from testfixtures import TempDirectory
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from zipline.lib.adjustment import Float64Multiply
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from zipline.data.equities import USEquityPricing
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from zipline.data.ffc.synthetic import (
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NullAdjustmentReader,
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SyntheticDailyBarWriter,
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)
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from zipline.data.ffc.loaders.us_equity_pricing import (
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BcolzDailyBarReader,
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SQLiteAdjustmentReader,
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SQLiteAdjustmentWriter,
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USEquityPricingLoader,
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)
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from zipline.errors import WindowLengthTooLong
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from zipline.finance.trading import TradingEnvironment
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from zipline.utils.test_utils import (
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seconds_to_timestamp,
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str_to_seconds,
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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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class BcolzDailyBarTestCase(TestCase):
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@classmethod
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def setUpClass(cls):
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all_trading_days = TradingEnvironment().trading_days
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cls.trading_days = all_trading_days[
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all_trading_days.get_loc(TEST_CALENDAR_START):
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all_trading_days.get_loc(TEST_CALENDAR_STOP) + 1
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]
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def setUp(self):
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self.asset_info = EQUITY_INFO
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self.writer = SyntheticDailyBarWriter(
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self.asset_info,
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self.trading_days,
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)
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self.dir_ = TempDirectory()
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self.dir_.create()
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self.dest = self.dir_.getpath('daily_equity_pricing.bcolz')
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def tearDown(self):
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self.dir_.cleanup()
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@property
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def assets(self):
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return self.asset_info.index
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def trading_days_between(self, start, end):
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return self.trading_days[self.trading_days.slice_indexer(start, end)]
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def asset_start(self, asset_id):
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return self.writer.asset_start(asset_id)
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def asset_end(self, asset_id):
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return self.writer.asset_end(asset_id)
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def dates_for_asset(self, asset_id):
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start, end = self.asset_start(asset_id), self.asset_end(asset_id)
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return self.trading_days_between(start, end)
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def test_write_ohlcv_content(self):
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result = self.writer.write(self.dest, self.trading_days, self.assets)
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for column in SyntheticDailyBarWriter.OHLCV:
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idx = 0
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data = result[column][:]
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multiplier = 1 if column == 'volume' else 1000
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for asset_id in self.assets:
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for date in self.dates_for_asset(asset_id):
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self.assertEqual(
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SyntheticDailyBarWriter.expected_value(
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asset_id,
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date,
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column
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) * multiplier,
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data[idx],
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)
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idx += 1
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self.assertEqual(idx, len(data))
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def test_write_day_and_id(self):
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result = self.writer.write(self.dest, self.trading_days, self.assets)
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idx = 0
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ids = result['id']
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days = result['day']
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for asset_id in self.assets:
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for date in self.dates_for_asset(asset_id):
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self.assertEqual(ids[idx], asset_id)
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self.assertEqual(date, seconds_to_timestamp(days[idx]))
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idx += 1
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def test_write_attrs(self):
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result = self.writer.write(self.dest, self.trading_days, self.assets)
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expected_first_row = {
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'1': 0,
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'2': 5, # Asset 1 has 5 trading days.
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'3': 12, # Asset 2 has 7 trading days.
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'4': 33, # Asset 3 has 21 trading days.
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'5': 44, # Asset 4 has 11 trading days.
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'6': 49, # Asset 5 has 5 trading days.
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}
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expected_last_row = {
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'1': 4,
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'2': 11,
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'3': 32,
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'4': 43,
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'5': 48,
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'6': 57, # Asset 6 has 9 trading days.
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}
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expected_calendar_offset = {
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'1': 0, # Starts on 6-01, 1st trading day of month.
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'2': 15, # Starts on 6-22, 16th trading day of month.
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'3': 1, # Starts on 6-02, 2nd trading day of month.
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'4': 0, # Starts on 6-01, 1st trading day of month.
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'5': 9, # Starts on 6-12, 10th trading day of month.
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'6': 10, # Starts on 6-15, 11th trading day of month.
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}
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self.assertEqual(result.attrs['first_row'], expected_first_row)
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self.assertEqual(result.attrs['last_row'], expected_last_row)
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self.assertEqual(
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result.attrs['calendar_offset'],
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expected_calendar_offset,
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)
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assert_index_equal(
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self.trading_days,
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DatetimeIndex(result.attrs['calendar'], tz='UTC'),
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)
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def _check_read_results(self, columns, assets, start_date, end_date):
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table = self.writer.write(self.dest, self.trading_days, self.assets)
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reader = BcolzDailyBarReader(table)
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results = reader.load_raw_arrays(columns, start_date, end_date, assets)
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dates = self.trading_days_between(start_date, end_date)
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for column, result in zip(columns, results):
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assert_array_equal(
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result,
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self.writer.expected_values_2d(
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dates,
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assets,
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column.name,
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)
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)
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@parameterized.expand([
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([USEquityPricing.open],),
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([USEquityPricing.close, USEquityPricing.volume],),
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([USEquityPricing.volume, USEquityPricing.high, USEquityPricing.low],),
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(USEquityPricing.columns,),
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])
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def test_read(self, columns):
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self._check_read_results(
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columns,
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self.assets,
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TEST_QUERY_START,
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TEST_QUERY_STOP,
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)
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def test_start_on_asset_start(self):
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"""
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Test loading with queries that starts on the first day of each asset's
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lifetime.
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"""
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columns = [USEquityPricing.high, USEquityPricing.volume]
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for asset in self.assets:
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self._check_read_results(
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columns,
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self.assets,
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start_date=self.asset_start(asset),
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end_date=self.trading_days[-1],
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)
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def test_start_on_asset_end(self):
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"""
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Test loading with queries that start on the last day of each asset's
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lifetime.
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"""
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columns = [USEquityPricing.close, USEquityPricing.volume]
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for asset in self.assets:
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self._check_read_results(
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columns,
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self.assets,
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start_date=self.asset_end(asset),
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end_date=self.trading_days[-1],
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)
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def test_end_on_asset_start(self):
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"""
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Test loading with queries that end on the first day of each asset's
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lifetime.
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"""
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columns = [USEquityPricing.close, USEquityPricing.volume]
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for asset in self.assets:
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self._check_read_results(
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columns,
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self.assets,
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start_date=self.trading_days[0],
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end_date=self.asset_start(asset),
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)
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def test_end_on_asset_end(self):
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"""
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Test loading with queries that end on the last day of each asset's
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lifetime.
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"""
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columns = [USEquityPricing.close, USEquityPricing.volume]
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for asset in self.assets:
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self._check_read_results(
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columns,
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self.assets,
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start_date=self.trading_days[0],
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end_date=self.asset_end(asset),
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)
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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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{'effective_date': str_to_seconds('2015-06-01'),
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'ratio': 1.301,
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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': 3.310,
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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': 3.312,
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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': 6.325,
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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': 3.315,
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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': 3.319,
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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(TestCase):
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@classmethod
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def setUpClass(cls):
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cls.test_data_dir = TempDirectory()
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cls.db_path = cls.test_data_dir.getpath('adjustments.db')
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writer = SQLiteAdjustmentWriter(cls.db_path)
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writer.write(SPLITS, MERGERS, DIVIDENDS)
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cls.assets = TEST_QUERY_ASSETS
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all_days = TradingEnvironment().trading_days
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cls.calendar_days = all_days[
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all_days.slice_indexer(TEST_CALENDAR_START, TEST_CALENDAR_STOP)
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]
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cls.asset_info = EQUITY_INFO
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cls.bcolz_writer = SyntheticDailyBarWriter(
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cls.asset_info,
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cls.calendar_days,
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)
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cls.bcolz_path = cls.test_data_dir.getpath('equity_pricing.bcolz')
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cls.bcolz_writer.write(cls.bcolz_path, cls.calendar_days, cls.assets)
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@classmethod
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def tearDownClass(cls):
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cls.test_data_dir.cleanup()
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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, DIVIDENDS:
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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.calendar_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.calendar_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:
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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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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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col=sid - 1,
|
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value=1.0 / ratio,
|
|
)
|
|
)
|
|
return price_adjustments, volume_adjustments
|
|
|
|
def test_load_adjustments_from_sqlite(self):
|
|
reader = SQLiteAdjustmentReader(self.db_path)
|
|
columns = [USEquityPricing.close, USEquityPricing.volume]
|
|
query_days = self.calendar_days_between(
|
|
TEST_QUERY_START,
|
|
TEST_QUERY_STOP,
|
|
)
|
|
|
|
adjustments = reader.load_adjustments(
|
|
columns,
|
|
query_days,
|
|
self.assets,
|
|
)
|
|
|
|
close_adjustments = adjustments[0]
|
|
volume_adjustments = adjustments[1]
|
|
|
|
expected_close_adjustments, expected_volume_adjustments = \
|
|
self.expected_adjustments(TEST_QUERY_START, TEST_QUERY_STOP)
|
|
self.assertEqual(close_adjustments, expected_close_adjustments)
|
|
self.assertEqual(volume_adjustments, expected_volume_adjustments)
|
|
|
|
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 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,
|
|
)
|
|
|
|
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)
|