mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 20:38:21 +08:00
Eddie Hebert
7904773d00
The latest flake8 release in now 1.5, which pulls in pep8: 1.3.4a0 The upgrade pep8 has changes to what it picks up as lint. Making code base compatible, so that new devs can install pep8 from PyPI and not have friction over the version difference. Currently using these ignores in the config file: ``` [pep8] ignore = E124,E125,E126 ``` Ignoring these since they are difficult to squash while maintaining an 80 char line length, and appear spurious. Should address later. Updates Travis config, README, and pip requirements to reflect change.
141 lines
5.0 KiB
Python
141 lines
5.0 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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import unittest
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import datetime
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import pytz
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import numpy as np
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import zipline.finance.risk as risk
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from zipline.utils import factory
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from zipline.finance.trading import TradingEnvironment
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from test_risk import RETURNS
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class RiskCompareIterativeToBatch(unittest.TestCase):
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"""
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Assert that RiskMetricsIterative and RiskMetricsBatch
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behave in the same way.
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"""
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def setUp(self):
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self.start_date = datetime.datetime(
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year=2006,
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month=1,
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day=1,
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hour=0,
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minute=0,
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tzinfo=pytz.utc)
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self.end_date = datetime.datetime(
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year=2006, month=12, day=31, tzinfo=pytz.utc)
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self.benchmark_returns, self.treasury_curves = \
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factory.load_market_data()
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self.trading_env = TradingEnvironment(
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self.benchmark_returns,
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self.treasury_curves,
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period_start=self.start_date,
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period_end=self.end_date,
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capital_base=1000.0
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)
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self.oneday = datetime.timedelta(days=1)
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def test_risk_metrics_returns(self):
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risk_metrics_refactor = risk.RiskMetricsIterative(
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self.start_date, self.trading_env)
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todays_date = self.start_date
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cur_returns = []
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for i, ret in enumerate(RETURNS):
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todays_return_obj = risk.DailyReturn(
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todays_date,
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ret
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)
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cur_returns.append(todays_return_obj)
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try:
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risk_metrics_original = risk.RiskMetricsBatch(
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start_date=self.start_date,
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end_date=todays_date + self.oneday,
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returns=cur_returns,
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trading_environment=self.trading_env
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)
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except Exception as e:
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#assert that when original raises exception, same
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#exception is raised by risk_metrics_refactor
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np.testing.assert_raises(
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type(e), risk_metrics_refactor.update, ret, self.oneday)
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continue
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risk_metrics_refactor.update(ret, self.oneday)
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todays_date += self.oneday
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self.assertEqual(
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risk_metrics_original.start_date,
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risk_metrics_refactor.start_date)
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self.assertEqual(
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risk_metrics_original.end_date,
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risk_metrics_refactor.end_date)
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self.assertEqual(
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risk_metrics_original.treasury_duration,
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risk_metrics_refactor.treasury_duration)
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self.assertEqual(
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risk_metrics_original.treasury_curve,
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risk_metrics_refactor.treasury_curve)
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self.assertEqual(
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risk_metrics_original.treasury_period_return,
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risk_metrics_refactor.treasury_period_return)
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self.assertEqual(
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risk_metrics_original.benchmark_returns,
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risk_metrics_refactor.benchmark_returns)
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self.assertEqual(
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risk_metrics_original.algorithm_returns,
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risk_metrics_refactor.algorithm_returns)
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risk_original_dict = risk_metrics_original.to_dict()
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risk_refactor_dict = risk_metrics_refactor.to_dict()
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self.assertEqual(set(risk_original_dict.keys()),
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set(risk_refactor_dict.keys()))
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err_msg_format = """\
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"In update step {iter}: {measure} should be {truth} but is {returned}!"""
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for measure in risk_original_dict.iterkeys():
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if measure == 'max_drawdown':
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np.testing.assert_almost_equal(
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risk_refactor_dict[measure],
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risk_original_dict[measure],
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err_msg=err_msg_format.format(
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iter=i,
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measure=measure,
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truth=risk_original_dict[measure],
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returned=risk_refactor_dict[measure]))
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else:
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np.testing.assert_equal(
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risk_original_dict[measure],
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risk_refactor_dict[measure],
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err_msg_format.format(
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iter=i,
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measure=measure,
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truth=risk_original_dict[measure],
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returned=risk_refactor_dict[measure])
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)
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