mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 05:32:55 +08:00
Conner Fromknecht
147 lines
4.5 KiB
Python
147 lines
4.5 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 catalyst.finance.risk as risk
|
|
from catalyst.utils import factory
|
|
|
|
from catalyst.testing.fixtures import WithTradingEnvironment, ZiplineTestCase
|
|
|
|
from catalyst.finance.trading import SimulationParameters
|
|
|
|
RETURNS_BASE = 0.01
|
|
RETURNS = [RETURNS_BASE] * 251
|
|
|
|
BENCHMARK_BASE = 0.005
|
|
BENCHMARK = [BENCHMARK_BASE] * 251
|
|
DECIMAL_PLACES = 8
|
|
|
|
|
|
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 = factory.create_returns_from_list(
|
|
RETURNS,
|
|
self.sim_params
|
|
)
|
|
self.cumulative_metrics = risk.RiskMetricsCumulative(
|
|
self.sim_params,
|
|
treasury_curves=self.env.treasury_curves,
|
|
trading_calendar=self.trading_calendar,
|
|
)
|
|
for dt, returns in self.algo_returns.iteritems():
|
|
self.cumulative_metrics.update(
|
|
dt,
|
|
returns,
|
|
BENCHMARK_BASE,
|
|
0.0
|
|
)
|
|
|
|
def test_algorithm_volatility(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.algorithm_volatility)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.algorithm_volatility),
|
|
True
|
|
)
|
|
|
|
def test_sharpe(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.sharpe)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.sharpe),
|
|
True)
|
|
|
|
def test_downside_risk(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.downside_risk)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.downside_risk),
|
|
True)
|
|
|
|
def test_sortino(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.sortino)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.sortino),
|
|
True)
|
|
|
|
def test_information(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.information)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.information),
|
|
True)
|
|
|
|
def test_alpha(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.alpha)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.alpha),
|
|
True)
|
|
|
|
def test_beta(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.beta)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.beta),
|
|
True)
|
|
|
|
def test_max_drawdown(self):
|
|
np.testing.assert_equal(
|
|
len(self.algo_returns),
|
|
len(self.cumulative_metrics.max_drawdowns)
|
|
)
|
|
np.testing.assert_equal(
|
|
all(isinstance(x, float)
|
|
for x in self.cumulative_metrics.max_drawdowns),
|
|
True)
|
|
|
|
def test_representation(self):
|
|
assert all([metric in self.cumulative_metrics.__repr__() for metric in
|
|
self.cumulative_metrics.METRIC_NAMES])
|