mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-03 13:09:04 +08:00
Eddie Hebert
94e70a394b
Converts the risk iterative and batch comparison tests to use the trading environments next date, instead of just advancing by day, so that the returns being passed into the RiskMetrics in the unit test are using the same trading calendar as the internal checks for trading days. Fixes a case where empty return periods were being into `calculate_period_returns` Clearing the way for the pandas based optimization of the risk module.
134 lines
4.9 KiB
Python
134 lines
4.9 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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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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import zipline.finance.trading as trading
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from zipline.protocol import DailyReturn
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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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def test_risk_metrics_returns(self):
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trading.environment = trading.TradingEnvironment()
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# Advance start date to first date in the trading calendar
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if trading.environment.is_trading_day(self.start_date):
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start_date = self.start_date
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else:
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start_date = trading.environment.next_trading_day(self.start_date)
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risk_metrics_refactor = risk.RiskMetricsIterative(start_date)
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todays_date = 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 = 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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# Move forward day counter to next trading day
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todays_date = trading.environment.next_trading_day(todays_date)
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try:
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risk_metrics_original = risk.RiskMetricsBatch(
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start_date=start_date,
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end_date=todays_date,
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returns=cur_returns
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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, todays_date, ret)
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continue
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risk_metrics_refactor.update(todays_date, ret)
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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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