mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-05 07:20:07 +08:00
Jean Bredeche
6fb4923cc7
Instead of having separate ExchangeCalendar and TradingSchedule objects, we now just have TradingCalendar. The TradingCalendar keeps track of each session (defined as a contiguous set of minutes between an open and a close). It's also responsible for handling the grouping logic of any given minute to its containing session, or the next/previous session if it's not a market minute for the given calendar.
124 lines
4.9 KiB
Python
124 lines
4.9 KiB
Python
#
|
|
# Copyright 2016 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 numpy as np
|
|
import pandas as pd
|
|
import zipline.finance.risk as risk
|
|
from zipline.utils import factory
|
|
|
|
from zipline.testing.fixtures import WithTradingEnvironment, ZiplineTestCase
|
|
|
|
from zipline.finance.trading import SimulationParameters
|
|
from . import answer_key
|
|
ANSWER_KEY = answer_key.ANSWER_KEY
|
|
|
|
|
|
class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
|
|
|
def init_instance_fixtures(self):
|
|
super(TestRisk, self).init_instance_fixtures()
|
|
|
|
start_session = pd.Timestamp("2006-01-01", tz='UTC')
|
|
end_session = pd.Timestamp("2006-12-29", tz='UTC')
|
|
|
|
self.sim_params = SimulationParameters(
|
|
start_session=start_session,
|
|
end_session=end_session,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
self.algo_returns_06 = factory.create_returns_from_list(
|
|
answer_key.ALGORITHM_RETURNS.values,
|
|
self.sim_params
|
|
)
|
|
|
|
self.cumulative_metrics_06 = risk.RiskMetricsCumulative(
|
|
self.sim_params,
|
|
treasury_curves=self.env.treasury_curves,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
|
|
for dt, returns in answer_key.RETURNS_DATA.iterrows():
|
|
self.cumulative_metrics_06.update(dt,
|
|
returns['Algorithm Returns'],
|
|
returns['Benchmark Returns'],
|
|
0.0)
|
|
|
|
def test_algorithm_volatility_06(self):
|
|
algo_vol_answers = answer_key.RISK_CUMULATIVE.volatility
|
|
for dt, value in algo_vol_answers.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
self.cumulative_metrics_06.algorithm_volatility[dt_loc],
|
|
value,
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_sharpe_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.sharpe.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
self.cumulative_metrics_06.sharpe[dt_loc],
|
|
value,
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_downside_risk_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.downside_risk.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
value,
|
|
self.cumulative_metrics_06.downside_risk[dt_loc],
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_sortino_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.sortino.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
self.cumulative_metrics_06.sortino[dt_loc],
|
|
value,
|
|
decimal=4,
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_information_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.information.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
value,
|
|
self.cumulative_metrics_06.information[dt_loc],
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_alpha_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.alpha.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
self.cumulative_metrics_06.alpha[dt_loc],
|
|
value,
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_beta_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.beta.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
value,
|
|
self.cumulative_metrics_06.beta[dt_loc],
|
|
err_msg="Mismatch at %s" % (dt,))
|
|
|
|
def test_max_drawdown_06(self):
|
|
for dt, value in answer_key.RISK_CUMULATIVE.max_drawdown.iteritems():
|
|
dt_loc = self.cumulative_metrics_06.cont_index.get_loc(dt)
|
|
np.testing.assert_almost_equal(
|
|
self.cumulative_metrics_06.max_drawdowns[dt_loc],
|
|
value,
|
|
err_msg="Mismatch at %s" % (dt,))
|