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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 calendar |
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import numpy as np |
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import pytz |
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from itertools import chain |
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from six import itervalues |
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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 SimulationParameters, TradingEnvironment |
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from . import answer_key |
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from . answer_key import AnswerKey |
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ANSWER_KEY = AnswerKey() |
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RETURNS = ANSWER_KEY.RETURNS |
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class TestRisk(unittest.TestCase): |
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@classmethod |
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def setUpClass(cls): |
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cls.env = TradingEnvironment() |
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@classmethod |
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def tearDownClass(cls): |
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del cls.env |
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def setUp(self): |
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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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end_date = datetime.datetime( |
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year=2006, month=12, day=31, tzinfo=pytz.utc) |
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self.sim_params = SimulationParameters( |
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period_start=start_date, |
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period_end=end_date, |
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env=self.env, |
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) |
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self.algo_returns_06 = factory.create_returns_from_list( |
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RETURNS, |
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self.sim_params |
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) |
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self.benchmark_returns_06 = \ |
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answer_key.RETURNS_DATA['Benchmark Returns'] |
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self.metrics_06 = risk.RiskReport( |
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self.algo_returns_06, |
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self.sim_params, |
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benchmark_returns=self.benchmark_returns_06, |
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env=self.env, |
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) |
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start_08 = datetime.datetime( |
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year=2008, |
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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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end_08 = datetime.datetime( |
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year=2008, |
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month=12, |
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day=31, |
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tzinfo=pytz.utc |
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) |
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self.sim_params08 = SimulationParameters( |
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period_start=start_08, |
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period_end=end_08, |
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env=self.env, |
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) |
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def tearDown(self): |
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return |
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def test_factory(self): |
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returns = [0.1] * 100 |
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r_objects = factory.create_returns_from_list(returns, self.sim_params) |
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self.assertTrue(r_objects.index[-1] <= |
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datetime.datetime( |
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year=2006, month=12, day=31, tzinfo=pytz.utc)) |
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def test_drawdown(self): |
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returns = factory.create_returns_from_list( |
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[1.0, -0.5, 0.8, .17, 1.0, -0.1, -0.45], self.sim_params) |
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# 200, 100, 180, 210.6, 421.2, 379.8, 208.494 |
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metrics = risk.RiskMetricsPeriod( |
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returns.index[0], |
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returns.index[-1], |
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returns, |
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env=self.env, |
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benchmark_returns=self.env.benchmark_returns, |
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) |
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self.assertEqual(metrics.max_drawdown, 0.505) |
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def test_benchmark_returns_06(self): |
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np.testing.assert_almost_equal( |
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[x.benchmark_period_returns |
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for x in self.metrics_06.month_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['Monthly']) |
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np.testing.assert_almost_equal( |
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[x.benchmark_period_returns |
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for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['3-Month']) |
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np.testing.assert_almost_equal( |
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[x.benchmark_period_returns |
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for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['6-month']) |
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np.testing.assert_almost_equal( |
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[x.benchmark_period_returns |
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for x in self.metrics_06.year_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['year']) |
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def test_trading_days_06(self): |
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returns = factory.create_returns_from_range(self.sim_params) |
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metrics = risk.RiskReport(returns, self.sim_params, env=self.env) |
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self.assertEqual([x.num_trading_days for x in metrics.year_periods], |
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[251]) |
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self.assertEqual([x.num_trading_days for x in metrics.month_periods], |
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[20, 19, 23, 19, 22, 22, 20, 23, 20, 22, 21, 20]) |
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def test_benchmark_volatility_06(self): |
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np.testing.assert_almost_equal( |
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[x.benchmark_volatility |
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for x in self.metrics_06.month_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['Monthly']) |
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np.testing.assert_almost_equal( |
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[x.benchmark_volatility |
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for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['3-Month']) |
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np.testing.assert_almost_equal( |
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[x.benchmark_volatility |
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for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['6-month']) |
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np.testing.assert_almost_equal( |
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[x.benchmark_volatility |
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for x in self.metrics_06.year_periods], |
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ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['year']) |
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def test_algorithm_returns_06(self): |
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np.testing.assert_almost_equal( |
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[x.algorithm_period_returns |
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for x in self.metrics_06.month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['Monthly']) |
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np.testing.assert_almost_equal( |
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[x.algorithm_period_returns |
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for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['3-Month']) |
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np.testing.assert_almost_equal( |
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[x.algorithm_period_returns |
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for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['6-month']) |
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np.testing.assert_almost_equal( |
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[x.algorithm_period_returns |
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for x in self.metrics_06.year_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['year']) |
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def test_algorithm_volatility_06(self): |
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np.testing.assert_almost_equal( |
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[x.algorithm_volatility |
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for x in self.metrics_06.month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['Monthly']) |
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np.testing.assert_almost_equal( |
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[x.algorithm_volatility |
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for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['3-Month']) |
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np.testing.assert_almost_equal( |
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[x.algorithm_volatility |
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for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['6-month']) |
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np.testing.assert_almost_equal( |
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[x.algorithm_volatility |
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for x in self.metrics_06.year_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['year']) |
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def test_algorithm_sharpe_06(self): |
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np.testing.assert_almost_equal( |
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[x.sharpe for x in self.metrics_06.month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['Monthly']) |
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np.testing.assert_almost_equal( |
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[x.sharpe for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['3-Month']) |
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np.testing.assert_almost_equal( |
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[x.sharpe for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['6-month']) |
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np.testing.assert_almost_equal( |
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[x.sharpe for x in self.metrics_06.year_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['year']) |
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def test_algorithm_downside_risk_06(self): |
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np.testing.assert_almost_equal( |
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[x.downside_risk for x in self.metrics_06.month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['Monthly'], |
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decimal=4) |
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np.testing.assert_almost_equal( |
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[x.downside_risk for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['3-Month'], |
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decimal=4) |
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np.testing.assert_almost_equal( |
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[x.downside_risk for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['6-month'], |
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decimal=4) |
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np.testing.assert_almost_equal( |
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[x.downside_risk for x in self.metrics_06.year_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['year'], |
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decimal=4) |
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def test_algorithm_sortino_06(self): |
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np.testing.assert_almost_equal( |
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[x.sortino for x in self.metrics_06.month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['Monthly'], |
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decimal=3) |
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np.testing.assert_almost_equal( |
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[x.sortino for x in self.metrics_06.three_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['3-Month'], |
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decimal=3) |
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np.testing.assert_almost_equal( |
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[x.sortino for x in self.metrics_06.six_month_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['6-month'], |
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decimal=3) |
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np.testing.assert_almost_equal( |
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[x.sortino for x in self.metrics_06.year_periods], |
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ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['year'], |
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decimal=3) |
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def test_algorithm_information_06(self): |
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self.assertEqual([round(x.information, 3) |
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for x in self.metrics_06.month_periods], |
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[0.131, |
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-0.11, |
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-0.067, |
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0.136, |
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0.301, |
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-0.387, |
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0.107, |
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-0.032, |
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-0.058, |
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0.069, |
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0.095, |
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-0.123]) |
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self.assertEqual([round(x.information, 3) |
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for x in self.metrics_06.three_month_periods], |
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276
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[-0.013, |
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-0.009, |
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0.111, |
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-0.014, |
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280
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-0.017, |
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281
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-0.108, |
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282
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0.011, |
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283
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-0.004, |
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0.032, |
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285
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0.011]) |
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286
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self.assertEqual([round(x.information, 3) |
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287
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for x in self.metrics_06.six_month_periods], |
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288
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[-0.013, |
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289
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-0.014, |
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290
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-0.003, |
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291
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-0.002, |
|
292
|
|
|
-0.011, |
|
293
|
|
|
-0.041, |
|
294
|
|
|
0.011]) |
|
295
|
|
|
self.assertEqual([round(x.information, 3) |
|
296
|
|
|
for x in self.metrics_06.year_periods], |
|
297
|
|
|
[-0.001]) |
|
298
|
|
|
|
|
299
|
|
|
def test_algorithm_beta_06(self): |
|
|
|
|
|
|
300
|
|
|
np.testing.assert_almost_equal( |
|
301
|
|
|
[x.beta for x in self.metrics_06.month_periods], |
|
302
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BETA['Monthly']) |
|
303
|
|
|
np.testing.assert_almost_equal( |
|
304
|
|
|
[x.beta for x in self.metrics_06.three_month_periods], |
|
305
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BETA['3-Month']) |
|
306
|
|
|
np.testing.assert_almost_equal( |
|
307
|
|
|
[x.beta for x in self.metrics_06.six_month_periods], |
|
308
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BETA['6-month']) |
|
309
|
|
|
np.testing.assert_almost_equal( |
|
310
|
|
|
[x.beta for x in self.metrics_06.year_periods], |
|
311
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BETA['year']) |
|
312
|
|
|
|
|
313
|
|
|
def test_algorithm_alpha_06(self): |
|
|
|
|
|
|
314
|
|
|
np.testing.assert_almost_equal( |
|
315
|
|
|
[x.alpha for x in self.metrics_06.month_periods], |
|
316
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['Monthly']) |
|
317
|
|
|
np.testing.assert_almost_equal( |
|
318
|
|
|
[x.alpha for x in self.metrics_06.three_month_periods], |
|
319
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['3-Month']) |
|
320
|
|
|
np.testing.assert_almost_equal( |
|
321
|
|
|
[x.alpha for x in self.metrics_06.six_month_periods], |
|
322
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['6-month']) |
|
323
|
|
|
np.testing.assert_almost_equal( |
|
324
|
|
|
[x.alpha for x in self.metrics_06.year_periods], |
|
325
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['year']) |
|
326
|
|
|
|
|
327
|
|
|
# FIXME: Covariance is not matching excel precisely enough to run the test. |
|
328
|
|
|
# Month 4 seems to be the problem. Variance is disabled |
|
329
|
|
|
# just to avoid distraction - it is much closer than covariance |
|
330
|
|
|
# and can probably pass with 6 significant digits instead of 7. |
|
331
|
|
|
# re-enable variance, alpha, and beta tests once this is resolved |
|
332
|
|
|
def test_algorithm_covariance_06(self): |
|
|
|
|
|
|
333
|
|
|
np.testing.assert_almost_equal( |
|
334
|
|
|
[x.algorithm_covariance for x in self.metrics_06.month_periods], |
|
335
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['Monthly']) |
|
336
|
|
|
np.testing.assert_almost_equal( |
|
337
|
|
|
[x.algorithm_covariance |
|
338
|
|
|
for x in self.metrics_06.three_month_periods], |
|
339
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['3-Month']) |
|
340
|
|
|
np.testing.assert_almost_equal( |
|
341
|
|
|
[x.algorithm_covariance |
|
342
|
|
|
for x in self.metrics_06.six_month_periods], |
|
343
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['6-month']) |
|
344
|
|
|
np.testing.assert_almost_equal( |
|
345
|
|
|
[x.algorithm_covariance |
|
346
|
|
|
for x in self.metrics_06.year_periods], |
|
347
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['year']) |
|
348
|
|
|
|
|
349
|
|
|
def test_benchmark_variance_06(self): |
|
|
|
|
|
|
350
|
|
|
np.testing.assert_almost_equal( |
|
351
|
|
|
[x.benchmark_variance |
|
352
|
|
|
for x in self.metrics_06.month_periods], |
|
353
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['Monthly']) |
|
354
|
|
|
np.testing.assert_almost_equal( |
|
355
|
|
|
[x.benchmark_variance |
|
356
|
|
|
for x in self.metrics_06.three_month_periods], |
|
357
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['3-Month']) |
|
358
|
|
|
np.testing.assert_almost_equal( |
|
359
|
|
|
[x.benchmark_variance |
|
360
|
|
|
for x in self.metrics_06.six_month_periods], |
|
361
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['6-month']) |
|
362
|
|
|
np.testing.assert_almost_equal( |
|
363
|
|
|
[x.benchmark_variance |
|
364
|
|
|
for x in self.metrics_06.year_periods], |
|
365
|
|
|
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['year']) |
|
366
|
|
|
|
|
367
|
|
|
def test_benchmark_returns_08(self): |
|
368
|
|
|
returns = factory.create_returns_from_range(self.sim_params08) |
|
369
|
|
|
metrics = risk.RiskReport(returns, self.sim_params08, env=self.env) |
|
370
|
|
|
|
|
371
|
|
|
self.assertEqual([round(x.benchmark_period_returns, 3) |
|
372
|
|
|
for x in metrics.month_periods], |
|
373
|
|
|
[-0.061, |
|
374
|
|
|
-0.035, |
|
375
|
|
|
-0.006, |
|
376
|
|
|
0.048, |
|
377
|
|
|
0.011, |
|
378
|
|
|
-0.086, |
|
379
|
|
|
-0.01, |
|
380
|
|
|
0.012, |
|
381
|
|
|
-0.091, |
|
382
|
|
|
-0.169, |
|
383
|
|
|
-0.075, |
|
384
|
|
|
0.008]) |
|
385
|
|
|
|
|
386
|
|
|
self.assertEqual([round(x.benchmark_period_returns, 3) |
|
387
|
|
|
for x in metrics.three_month_periods], |
|
388
|
|
|
[-0.099, |
|
389
|
|
|
0.005, |
|
390
|
|
|
0.052, |
|
391
|
|
|
-0.032, |
|
392
|
|
|
-0.085, |
|
393
|
|
|
-0.084, |
|
394
|
|
|
-0.089, |
|
395
|
|
|
-0.236, |
|
396
|
|
|
-0.301, |
|
397
|
|
|
-0.226]) |
|
398
|
|
|
|
|
399
|
|
|
self.assertEqual([round(x.benchmark_period_returns, 3) |
|
400
|
|
|
for x in metrics.six_month_periods], |
|
401
|
|
|
[-0.128, |
|
402
|
|
|
-0.081, |
|
403
|
|
|
-0.036, |
|
404
|
|
|
-0.118, |
|
405
|
|
|
-0.301, |
|
406
|
|
|
-0.36, |
|
407
|
|
|
-0.294]) |
|
408
|
|
|
|
|
409
|
|
|
self.assertEqual([round(x.benchmark_period_returns, 3) |
|
410
|
|
|
for x in metrics.year_periods], |
|
411
|
|
|
[-0.385]) |
|
412
|
|
|
|
|
413
|
|
|
def test_trading_days_08(self): |
|
|
|
|
|
|
414
|
|
|
returns = factory.create_returns_from_range(self.sim_params08) |
|
415
|
|
|
metrics = risk.RiskReport(returns, self.sim_params08, env=self.env) |
|
416
|
|
|
self.assertEqual([x.num_trading_days for x in metrics.year_periods], |
|
417
|
|
|
[253]) |
|
418
|
|
|
|
|
419
|
|
|
self.assertEqual([x.num_trading_days for x in metrics.month_periods], |
|
420
|
|
|
[21, 20, 20, 22, 21, 21, 22, 21, 21, 23, 19, 22]) |
|
421
|
|
|
|
|
422
|
|
|
def test_benchmark_volatility_08(self): |
|
|
|
|
|
|
423
|
|
|
returns = factory.create_returns_from_range(self.sim_params08) |
|
424
|
|
|
metrics = risk.RiskReport(returns, self.sim_params08, env=self.env) |
|
425
|
|
|
|
|
426
|
|
|
self.assertEqual([round(x.benchmark_volatility, 3) |
|
427
|
|
|
for x in metrics.month_periods], |
|
428
|
|
|
[0.07, |
|
429
|
|
|
0.058, |
|
430
|
|
|
0.082, |
|
431
|
|
|
0.054, |
|
432
|
|
|
0.041, |
|
433
|
|
|
0.057, |
|
434
|
|
|
0.068, |
|
435
|
|
|
0.06, |
|
436
|
|
|
0.157, |
|
437
|
|
|
0.244, |
|
438
|
|
|
0.195, |
|
439
|
|
|
0.145]) |
|
440
|
|
|
|
|
441
|
|
|
self.assertEqual([round(x.benchmark_volatility, 3) |
|
442
|
|
|
for x in metrics.three_month_periods], |
|
443
|
|
|
[0.12, |
|
444
|
|
|
0.113, |
|
445
|
|
|
0.105, |
|
446
|
|
|
0.09, |
|
447
|
|
|
0.098, |
|
448
|
|
|
0.107, |
|
449
|
|
|
0.179, |
|
450
|
|
|
0.293, |
|
451
|
|
|
0.344, |
|
452
|
|
|
0.34]) |
|
453
|
|
|
|
|
454
|
|
|
self.assertEqual([round(x.benchmark_volatility, 3) |
|
455
|
|
|
for x in metrics.six_month_periods], |
|
456
|
|
|
[0.15, |
|
457
|
|
|
0.149, |
|
458
|
|
|
0.15, |
|
459
|
|
|
0.2, |
|
460
|
|
|
0.308, |
|
461
|
|
|
0.36, |
|
462
|
|
|
0.383]) |
|
463
|
|
|
# TODO: ugly, but I can't get the rounded float to match. |
|
464
|
|
|
# maybe we need a different test that checks the |
|
465
|
|
|
# difference between the numbers |
|
466
|
|
|
self.assertEqual([round(x.benchmark_volatility, 3) |
|
467
|
|
|
for x in metrics.year_periods], |
|
468
|
|
|
[0.411]) |
|
469
|
|
|
|
|
470
|
|
|
def test_treasury_returns_06(self): |
|
|
|
|
|
|
471
|
|
|
returns = factory.create_returns_from_range(self.sim_params) |
|
472
|
|
|
metrics = risk.RiskReport(returns, self.sim_params, env=self.env) |
|
473
|
|
|
self.assertEqual([round(x.treasury_period_return, 4) |
|
474
|
|
|
for x in metrics.month_periods], |
|
475
|
|
|
[0.0037, |
|
476
|
|
|
0.0034, |
|
477
|
|
|
0.0039, |
|
478
|
|
|
0.0038, |
|
479
|
|
|
0.0040, |
|
480
|
|
|
0.0037, |
|
481
|
|
|
0.0043, |
|
482
|
|
|
0.0043, |
|
483
|
|
|
0.0038, |
|
484
|
|
|
0.0044, |
|
485
|
|
|
0.0043, |
|
486
|
|
|
0.004]) |
|
487
|
|
|
|
|
488
|
|
|
self.assertEqual([round(x.treasury_period_return, 4) |
|
489
|
|
|
for x in metrics.three_month_periods], |
|
490
|
|
|
[0.0114, |
|
491
|
|
|
0.0116, |
|
492
|
|
|
0.0122, |
|
493
|
|
|
0.0125, |
|
494
|
|
|
0.0129, |
|
495
|
|
|
0.0127, |
|
496
|
|
|
0.0123, |
|
497
|
|
|
0.0128, |
|
498
|
|
|
0.0125, |
|
499
|
|
|
0.0127]) |
|
500
|
|
|
self.assertEqual([round(x.treasury_period_return, 4) |
|
501
|
|
|
for x in metrics.six_month_periods], |
|
502
|
|
|
[0.0260, |
|
503
|
|
|
0.0257, |
|
504
|
|
|
0.0258, |
|
505
|
|
|
0.0252, |
|
506
|
|
|
0.0259, |
|
507
|
|
|
0.0256, |
|
508
|
|
|
0.0257]) |
|
509
|
|
|
|
|
510
|
|
|
self.assertEqual([round(x.treasury_period_return, 4) |
|
511
|
|
|
for x in metrics.year_periods], |
|
512
|
|
|
[0.0500]) |
|
513
|
|
|
|
|
514
|
|
|
def test_benchmarkrange(self): |
|
515
|
|
|
self.check_year_range( |
|
516
|
|
|
datetime.datetime( |
|
517
|
|
|
year=2008, month=1, day=1, tzinfo=pytz.utc), |
|
518
|
|
|
2) |
|
519
|
|
|
|
|
520
|
|
|
def test_partial_month(self): |
|
521
|
|
|
|
|
522
|
|
|
start = datetime.datetime( |
|
523
|
|
|
year=1991, |
|
524
|
|
|
month=1, |
|
525
|
|
|
day=1, |
|
526
|
|
|
hour=0, |
|
527
|
|
|
minute=0, |
|
528
|
|
|
tzinfo=pytz.utc) |
|
529
|
|
|
|
|
530
|
|
|
# 1992 and 1996 were leap years |
|
531
|
|
|
total_days = 365 * 5 + 2 |
|
532
|
|
|
end = start + datetime.timedelta(days=total_days) |
|
533
|
|
|
sim_params90s = SimulationParameters( |
|
534
|
|
|
period_start=start, |
|
535
|
|
|
period_end=end, |
|
536
|
|
|
env=self.env, |
|
537
|
|
|
) |
|
538
|
|
|
|
|
539
|
|
|
returns = factory.create_returns_from_range(sim_params90s) |
|
540
|
|
|
returns = returns[:-10] # truncate the returns series to end mid-month |
|
541
|
|
|
metrics = risk.RiskReport(returns, sim_params90s, env=self.env) |
|
542
|
|
|
total_months = 60 |
|
543
|
|
|
self.check_metrics(metrics, total_months, start) |
|
544
|
|
|
|
|
545
|
|
|
def check_year_range(self, start_date, years): |
|
546
|
|
|
sim_params = SimulationParameters( |
|
547
|
|
|
period_start=start_date, |
|
548
|
|
|
period_end=start_date.replace(year=(start_date.year + years)), |
|
549
|
|
|
env=self.env, |
|
550
|
|
|
) |
|
551
|
|
|
returns = factory.create_returns_from_range(sim_params) |
|
552
|
|
|
metrics = risk.RiskReport(returns, self.sim_params, env=self.env) |
|
553
|
|
|
total_months = years * 12 |
|
554
|
|
|
self.check_metrics(metrics, total_months, start_date) |
|
555
|
|
|
|
|
556
|
|
|
def check_metrics(self, metrics, total_months, start_date): |
|
557
|
|
|
""" |
|
558
|
|
|
confirm that the right number of riskmetrics were calculated for each |
|
559
|
|
|
window length. |
|
560
|
|
|
""" |
|
561
|
|
|
self.assert_range_length( |
|
562
|
|
|
metrics.month_periods, |
|
563
|
|
|
total_months, |
|
564
|
|
|
1, |
|
565
|
|
|
start_date |
|
566
|
|
|
) |
|
567
|
|
|
|
|
568
|
|
|
self.assert_range_length( |
|
569
|
|
|
metrics.three_month_periods, |
|
570
|
|
|
total_months, |
|
571
|
|
|
3, |
|
572
|
|
|
start_date |
|
573
|
|
|
) |
|
574
|
|
|
|
|
575
|
|
|
self.assert_range_length( |
|
576
|
|
|
metrics.six_month_periods, |
|
577
|
|
|
total_months, |
|
578
|
|
|
6, |
|
579
|
|
|
start_date |
|
580
|
|
|
) |
|
581
|
|
|
|
|
582
|
|
|
self.assert_range_length( |
|
583
|
|
|
metrics.year_periods, |
|
584
|
|
|
total_months, |
|
585
|
|
|
12, |
|
586
|
|
|
start_date |
|
587
|
|
|
) |
|
588
|
|
|
|
|
589
|
|
|
def assert_last_day(self, period_end): |
|
590
|
|
|
# 30 days has september, april, june and november |
|
591
|
|
|
if period_end.month in [9, 4, 6, 11]: |
|
592
|
|
|
self.assertEqual(period_end.day, 30) |
|
593
|
|
|
# all the rest have 31, except for february |
|
594
|
|
|
elif(period_end.month != 2): |
|
595
|
|
|
self.assertEqual(period_end.day, 31) |
|
596
|
|
|
else: |
|
597
|
|
|
if calendar.isleap(period_end.year): |
|
598
|
|
|
self.assertEqual(period_end.day, 29) |
|
599
|
|
|
else: |
|
600
|
|
|
self.assertEqual(period_end.day, 28) |
|
601
|
|
|
|
|
602
|
|
|
def assert_month(self, start_month, actual_end_month): |
|
603
|
|
|
if start_month == 1: |
|
604
|
|
|
expected_end_month = 12 |
|
605
|
|
|
else: |
|
606
|
|
|
expected_end_month = start_month - 1 |
|
607
|
|
|
|
|
608
|
|
|
self.assertEqual(expected_end_month, actual_end_month) |
|
609
|
|
|
|
|
610
|
|
|
def assert_range_length(self, col, total_months, |
|
611
|
|
|
period_length, start_date): |
|
612
|
|
|
if(period_length > total_months): |
|
613
|
|
|
self.assertEqual(len(col), 0) |
|
614
|
|
|
else: |
|
615
|
|
|
self.assertEqual( |
|
616
|
|
|
len(col), |
|
617
|
|
|
total_months - (period_length - 1), |
|
618
|
|
|
"mismatch for total months - \ |
|
619
|
|
|
expected:{total_months}/actual:{actual}, \ |
|
620
|
|
|
period:{period_length}, start:{start_date}, \ |
|
621
|
|
|
calculated end:{end}".format(total_months=total_months, |
|
622
|
|
|
period_length=period_length, |
|
623
|
|
|
start_date=start_date, |
|
624
|
|
|
end=col[-1].end_date, |
|
625
|
|
|
actual=len(col)) |
|
626
|
|
|
) |
|
627
|
|
|
self.assert_month(start_date.month, col[-1].end_date.month) |
|
628
|
|
|
self.assert_last_day(col[-1].end_date) |
|
629
|
|
|
|
|
630
|
|
|
def test_sparse_benchmark(self): |
|
631
|
|
|
benchmark_returns = self.benchmark_returns_06.copy() |
|
632
|
|
|
# Set every other day to nan. |
|
633
|
|
|
benchmark_returns.iloc[::2] = np.nan |
|
634
|
|
|
|
|
635
|
|
|
report = risk.RiskReport( |
|
636
|
|
|
self.algo_returns_06, |
|
637
|
|
|
self.sim_params, |
|
638
|
|
|
benchmark_returns=benchmark_returns, |
|
639
|
|
|
env=self.env, |
|
640
|
|
|
) |
|
641
|
|
|
for risk_period in chain.from_iterable(itervalues(report.to_dict())): |
|
642
|
|
|
self.assertIsNone(risk_period['beta']) |
|
643
|
|
|
|
quantopian /
zipline
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Duplicated code is one of the most pungent code smells. If you need to duplicate the same code in three or more different places, we strongly encourage you to look into extracting the code into a single class or operation.
You can also find more detailed suggestions in the “Code” section of your repository.