mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-27 23:52:53 +08:00
Joe Jevnik
f8f7f2fc4c
Previously, all specs had to be pre-allocated by using the 'add_history' function. This is now no longer required and instead serves as a hint to the HistoryContainer to pre-allocate the space for the given spec. History can grow by increasing the length for a frequency, adding a frequency, or adding a field. It can grow with any combination of these. HistoryContainer now is aware of the data_frequency of the algorithm, and no longer uses the daily_at_midnight flag; instead, this is the default behavior.
280 lines
9.5 KiB
Python
280 lines
9.5 KiB
Python
#
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# Copyright 2013 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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from collections import deque
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import pytz
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import numpy as np
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import pandas as pd
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from datetime import datetime
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from unittest import TestCase
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from zipline.utils.test_utils import setup_logger
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from zipline.sources.data_source import DataSource
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import zipline.utils.factory as factory
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from zipline.transforms import batch_transform
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from zipline.test_algorithms import (BatchTransformAlgorithm,
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BatchTransformAlgorithmMinute,
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ReturnPriceBatchTransform)
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from zipline.algorithm import TradingAlgorithm
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from zipline.utils.tradingcalendar import trading_days
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from copy import deepcopy
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@batch_transform
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def return_price(data):
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return data.price
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class BatchTransformAlgorithmSetSid(TradingAlgorithm):
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def initialize(self, sids=None):
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self.history = []
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self.batch_transform = return_price(
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refresh_period=1,
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window_length=10,
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clean_nans=False,
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sids=sids,
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compute_only_full=False
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)
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def handle_data(self, data):
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self.history.append(
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deepcopy(self.batch_transform.handle_data(data)))
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class DifferentSidSource(DataSource):
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def __init__(self):
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self.dates = pd.date_range('1990-01-01', periods=180, tz='utc')
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self.start = self.dates[0]
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self.end = self.dates[-1]
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self._raw_data = None
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self.sids = range(90)
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self.sid = 0
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self.trading_days = []
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@property
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def instance_hash(self):
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return '1234'
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@property
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def raw_data(self):
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if not self._raw_data:
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self._raw_data = self.raw_data_gen()
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return self._raw_data
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@property
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def mapping(self):
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return {
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'dt': (lambda x: x, 'dt'),
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'sid': (lambda x: x, 'sid'),
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'price': (float, 'price'),
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'volume': (int, 'volume'),
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}
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def raw_data_gen(self):
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# Create differente sid for each event
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for date in self.dates:
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if date not in trading_days:
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continue
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event = {'dt': date,
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'sid': self.sid,
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'price': self.sid,
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'volume': self.sid}
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self.sid += 1
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self.trading_days.append(date)
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yield event
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class TestChangeOfSids(TestCase):
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def setUp(self):
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self.sids = range(90)
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self.sim_params = factory.create_simulation_parameters(
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start=datetime(1990, 1, 1, tzinfo=pytz.utc),
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end=datetime(1990, 1, 8, tzinfo=pytz.utc)
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)
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def test_all_sids_passed(self):
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algo = BatchTransformAlgorithmSetSid(sim_params=self.sim_params)
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source = DifferentSidSource()
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algo.run(source)
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for i, (df, date) in enumerate(zip(algo.history, source.trading_days)):
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self.assertEqual(df.index[-1], date, "Newest event doesn't \
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match.")
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for sid in self.sids[:i]:
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self.assertIn(sid, df.columns)
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self.assertEqual(df.iloc[-1].iloc[-1], i)
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class TestBatchTransformMinutely(TestCase):
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def setUp(self):
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start = pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
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end = pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
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self.sim_params = factory.create_simulation_parameters(
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start=start,
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end=end,
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)
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self.sim_params.emission_rate = 'daily'
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self.sim_params.data_frequency = 'minute'
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setup_logger(self)
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self.source, self.df = \
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factory.create_test_df_source(bars='minute')
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def test_core(self):
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algo = BatchTransformAlgorithmMinute(sim_params=self.sim_params)
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algo.run(self.source)
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wl = int(algo.window_length * 6.5 * 60)
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for bt in algo.history[wl:]:
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self.assertEqual(len(bt), wl)
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def test_window_length(self):
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algo = BatchTransformAlgorithmMinute(sim_params=self.sim_params,
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window_length=1, refresh_period=0)
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algo.run(self.source)
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wl = int(algo.window_length * 6.5 * 60)
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np.testing.assert_array_equal(algo.history[:(wl - 1)],
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[None] * (wl - 1))
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for bt in algo.history[wl:]:
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self.assertEqual(len(bt), wl)
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class TestBatchTransform(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters(
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start=datetime(1990, 1, 1, tzinfo=pytz.utc),
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end=datetime(1990, 1, 8, tzinfo=pytz.utc)
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)
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setup_logger(self)
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self.source, self.df = \
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factory.create_test_df_source(self.sim_params)
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def test_core_functionality(self):
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algo = BatchTransformAlgorithm(sim_params=self.sim_params)
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algo.run(self.source)
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wl = algo.window_length
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# The following assertion depend on window length of 3
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self.assertEqual(wl, 3)
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# If window_length is 3, there should be 2 None events, as the
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# window fills up on the 3rd day.
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n_none_events = 2
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self.assertEqual(algo.history_return_price_class[:n_none_events],
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[None] * n_none_events,
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"First two iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_class,))
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self.assertEqual(algo.history_return_price_decorator[:n_none_events],
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[None] * n_none_events,
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"First two iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_decorator,))
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# After three Nones, the next value should be a data frame
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self.assertTrue(isinstance(
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algo.history_return_price_class[wl],
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pd.DataFrame)
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)
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# Test whether arbitrary fields can be added to datapanel
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field = algo.history_return_arbitrary_fields[-1]
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self.assertTrue(
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'arbitrary' in field.items,
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'datapanel should contain column arbitrary'
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)
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self.assertTrue(all(
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field['arbitrary'].values.flatten() ==
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[123] * algo.window_length),
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'arbitrary dataframe should contain only "test"'
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)
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for data in algo.history_return_sid_filter[wl:]:
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self.assertIn(0, data.columns)
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self.assertNotIn(1, data.columns)
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for data in algo.history_return_field_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertNotIn('ignore', data.items)
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for data in algo.history_return_field_no_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertIn('ignore', data.items)
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for data in algo.history_return_ticks[wl:]:
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self.assertTrue(isinstance(data, deque))
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for data in algo.history_return_not_full:
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self.assertIsNot(data, None)
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# test overloaded class
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for test_history in [algo.history_return_price_class,
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algo.history_return_price_decorator]:
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# starting at window length, the window should contain
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# consecutive (of window length) numbers up till the end.
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for i in range(algo.window_length, len(test_history)):
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np.testing.assert_array_equal(
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range(i - algo.window_length + 2, i + 2),
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test_history[i].values.flatten()
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)
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def test_passing_of_args(self):
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algo = BatchTransformAlgorithm(1, kwarg='str',
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sim_params=self.sim_params)
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self.assertEqual(algo.args, (1,))
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self.assertEqual(algo.kwargs, {'kwarg': 'str'})
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algo.run(self.source)
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expected_item = ((1, ), {'kwarg': 'str'})
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self.assertEqual(
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algo.history_return_args,
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[
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# 1990-01-01 - market holiday, no event
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# 1990-01-02 - window not full
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None,
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# 1990-01-03 - window not full
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None,
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# 1990-01-04 - window now full, 3rd event
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expected_item,
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# 1990-01-05 - window now full
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expected_item,
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# 1990-01-08 - window now full
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expected_item
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])
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def run_batchtransform(window_length=10):
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sim_params = factory.create_simulation_parameters(
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start=datetime(1990, 1, 1, tzinfo=pytz.utc),
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end=datetime(1995, 1, 8, tzinfo=pytz.utc)
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)
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source, df = factory.create_test_df_source(sim_params)
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return_price_class = ReturnPriceBatchTransform(
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refresh_period=1,
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window_length=window_length,
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clean_nans=False
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)
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for raw_event in source:
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raw_event['datetime'] = raw_event.dt
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event = {0: raw_event}
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return_price_class.handle_data(event)
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