mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-18 12:20:12 +08:00
fawceandEddie Hebert
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
134 lines
4.8 KiB
Python
134 lines
4.8 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.finance.trading import TradingEnvironment
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import zipline.finance.trading as trading
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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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# setup the default trading environment
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trading.environment = TradingEnvironment()
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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(self.start_date)
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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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# Move forward day counter to next trading day
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todays_date += self.oneday
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while not trading.environment.is_trading_day(todays_date):
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todays_date += self.oneday
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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,
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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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