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fawce
2c7355a0dc
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.
Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.
================
Details:
- adding delorean to the requirements.
- made index symbol a parameter for loading the benchmark data. changed
messagepack storage to be symbol specific.
- ported risk, performance, algorithm, transforms, batch transforms
and associated tests to use simulation parameters and global environment
- factory and sim factory use global state and sim params
- factory method parameter names now reflect the class expected
158 lines
5.1 KiB
Python
158 lines
5.1 KiB
Python
#
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# Copyright 2012 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 unittest import TestCase
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from datetime import timedelta
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import numpy as np
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from zipline.utils.test_utils import setup_logger
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import zipline.utils.factory as factory
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from zipline.test_algorithms import (TestRegisterTransformAlgorithm,
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RecordAlgorithm)
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from zipline.sources import (SpecificEquityTrades,
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DataFrameSource,
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DataPanelSource)
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from zipline.transforms import MovingAverage
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class TestRecordAlgorithm(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters()
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trade_history = factory.create_trade_history(
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133,
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[10.0, 10.0, 11.0, 11.0],
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[100, 100, 100, 300],
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timedelta(days=1),
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self.sim_params
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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self.df_source, self.df = \
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factory.create_test_df_source(self.sim_params)
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def test_record_incr(self):
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algo = RecordAlgorithm()
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output = algo.run(self.source)
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np.testing.assert_array_equal(output['incr'].values,
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range(1, len(output) + 1))
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class TestTransformAlgorithm(TestCase):
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def setUp(self):
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setup_logger(self)
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self.sim_params = factory.create_simulation_parameters()
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setup_logger(self)
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trade_history = factory.create_trade_history(
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133,
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[10.0, 10.0, 11.0, 11.0],
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[100, 100, 100, 300],
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timedelta(days=1),
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self.sim_params
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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self.df_source, self.df = \
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factory.create_test_df_source(self.sim_params)
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self.panel_source, self.panel = \
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factory.create_test_panel_source(self.sim_params)
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def test_source_as_input(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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sids=[133]
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)
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algo.run(self.source)
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self.assertEqual(len(algo.sources), 1)
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assert isinstance(algo.sources[0], SpecificEquityTrades)
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def test_multi_source_as_input_no_start_end(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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sids=[133]
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)
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with self.assertRaises(AssertionError):
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algo.run([self.source, self.df_source])
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def test_multi_source_as_input(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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sids=[0, 1, 133]
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)
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algo.run([self.source, self.df_source],
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start=self.df.index[0], end=self.df.index[-1])
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self.assertEqual(len(algo.sources), 2)
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def test_df_as_input(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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sids=[0, 1]
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)
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algo.run(self.df)
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assert isinstance(algo.sources[0], DataFrameSource)
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def test_panel_as_input(self):
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algo = TestRegisterTransformAlgorithm(sids=[0, 1])
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algo.run(self.panel)
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assert isinstance(algo.sources[0], DataPanelSource)
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def test_run_twice(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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sids=[0, 1]
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)
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res1 = algo.run(self.df)
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res2 = algo.run(self.df)
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np.testing.assert_array_equal(res1, res2)
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def test_transform_registered(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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sids=[133]
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)
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algo.run(self.source)
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assert 'mavg' in algo.registered_transforms
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assert algo.registered_transforms['mavg']['args'] == (['price'],)
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assert algo.registered_transforms['mavg']['kwargs'] == \
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{'window_length': 2, 'market_aware': True}
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assert algo.registered_transforms['mavg']['class'] is MovingAverage
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def test_data_frequency_setting(self):
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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data_frequency='daily'
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)
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self.assertEqual(algo.data_frequency, 'daily')
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self.assertEqual(algo.annualizer, 250)
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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data_frequency='minute'
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)
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self.assertEqual(algo.data_frequency, 'minute')
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self.assertEqual(algo.annualizer, 250 * 6 * 60)
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algo = TestRegisterTransformAlgorithm(
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self.sim_params,
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data_frequency='minute',
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annualizer=10
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)
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self.assertEqual(algo.data_frequency, 'minute')
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self.assertEqual(algo.annualizer, 10)
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