/tests/risk/test_risk_compare_batch_iterative.py
Python | 164 lines | 123 code | 19 blank | 22 comment | 7 complexity | 610d1698c9e77f4cb8255267fc3b4994 MD5 | raw file
Possible License(s): Apache-2.0
- #
- # Copyright 2013 Quantopian, Inc.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import numbers
- import unittest
- import datetime
- import pytz
- import numpy as np
- import pandas as pd
- import zipline.finance.risk as risk
- import zipline.finance.trading as trading
- from zipline.finance.trading import SimulationParameters
- from zipline.protocol import DailyReturn
- from test_risk import RETURNS
- class RiskCompareIterativeToBatch(unittest.TestCase):
- """
- Assert that RiskMetricsIterative and RiskMetricsBatch
- behave in the same way.
- """
- def setUp(self):
- self.start_date = datetime.datetime(
- year=2006,
- month=1,
- day=1,
- hour=0,
- minute=0,
- tzinfo=pytz.utc)
- self.end_date = datetime.datetime(
- year=2006, month=12, day=31, tzinfo=pytz.utc)
- def test_risk_metrics_returns(self):
- trading.environment = trading.TradingEnvironment()
- # Advance start date to first date in the trading calendar
- if trading.environment.is_trading_day(self.start_date):
- start_date = self.start_date
- else:
- start_date = trading.environment.next_trading_day(self.start_date)
- self.all_benchmark_returns = pd.Series({
- x.date: x.returns
- for x in trading.environment.benchmark_returns
- if x.date >= self.start_date
- })
- start_index = trading.environment.trading_days.searchsorted(start_date)
- end_date = trading.environment.trading_days[
- start_index + len(RETURNS)]
- sim_params = SimulationParameters(start_date, end_date)
- risk_metrics_refactor = risk.RiskMetricsIterative(sim_params)
- todays_date = start_date
- cur_returns = []
- for i, ret in enumerate(RETURNS):
- todays_return_obj = DailyReturn(
- todays_date,
- ret
- )
- cur_returns.append(todays_return_obj)
- try:
- risk_metrics_original = risk.RiskMetricsBatch(
- start_date=start_date,
- end_date=todays_date,
- returns=cur_returns
- )
- except Exception as e:
- #assert that when original raises exception, same
- #exception is raised by risk_metrics_refactor
- np.testing.assert_raises(
- type(e),
- risk_metrics_refactor.update,
- todays_date,
- self.all_benchmark_returns[todays_return_obj.date]
- )
- continue
- risk_metrics_refactor.update(
- todays_date,
- ret,
- self.all_benchmark_returns[todays_return_obj.date])
- # Move forward day counter to next trading day
- todays_date = trading.environment.next_trading_day(todays_date)
- self.assertEqual(
- risk_metrics_original.start_date,
- risk_metrics_refactor.start_date)
- self.assertEqual(
- risk_metrics_original.end_date,
- risk_metrics_refactor.algorithm_returns.index[-1])
- self.assertEqual(
- risk_metrics_original.treasury_period_return,
- risk_metrics_refactor.treasury_period_return)
- np.testing.assert_allclose(
- risk_metrics_original.benchmark_returns,
- risk_metrics_refactor.benchmark_returns,
- rtol=0.001
- )
- np.testing.assert_allclose(
- risk_metrics_original.algorithm_returns,
- risk_metrics_refactor.algorithm_returns,
- rtol=0.001
- )
- risk_original_dict = risk_metrics_original.to_dict()
- risk_refactor_dict = risk_metrics_refactor.to_dict()
- self.assertEqual(set(risk_original_dict.keys()),
- set(risk_refactor_dict.keys()))
- err_msg_format = """\
- "In update step {iter}: {measure} should be {truth} but is {returned}!"""
- for measure in risk_original_dict.iterkeys():
- if measure == 'max_drawdown':
- np.testing.assert_almost_equal(
- risk_refactor_dict[measure],
- risk_original_dict[measure],
- err_msg=err_msg_format.format(
- iter=i,
- measure=measure,
- truth=risk_original_dict[measure],
- returned=risk_refactor_dict[measure]))
- else:
- if isinstance(risk_original_dict[measure], numbers.Real):
- np.testing.assert_allclose(
- risk_original_dict[measure],
- risk_refactor_dict[measure],
- rtol=0.001,
- err_msg=err_msg_format.format(
- iter=i,
- measure=measure,
- truth=risk_original_dict[measure],
- returned=risk_refactor_dict[measure])
- )
- else:
- np.testing.assert_equal(
- risk_original_dict[measure],
- risk_refactor_dict[measure],
- err_msg=err_msg_format.format(
- iter=i,
- measure=measure,
- truth=risk_original_dict[measure],
- returned=risk_refactor_dict[measure])
- )