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catalyst/tests/risk/test_risk_period.py
T
Jean Bredeche 6fb4923cc7 Re-implemented the Calendar API. 
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.
2016-07-12 13:13:50 -04:00

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#
# 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 datetime
import calendar
import pandas as pd
import numpy as np
import pytz
from itertools import chain
from six import itervalues
import zipline.finance.risk as risk
from zipline.utils import factory
from zipline.finance.trading import SimulationParameters
from zipline.testing.fixtures import WithTradingEnvironment, ZiplineTestCase
from . import answer_key
from . answer_key import AnswerKey
ANSWER_KEY = AnswerKey()
RETURNS = ANSWER_KEY.RETURNS
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 = self.trading_calendar.minute_to_session_label(
pd.Timestamp("2006-12-31", tz='UTC'),
direction="previous"
)
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(
RETURNS,
self.sim_params
)
self.benchmark_returns_06 = \
answer_key.RETURNS_DATA['Benchmark Returns']
self.metrics_06 = risk.RiskReport(
self.algo_returns_06,
self.sim_params,
benchmark_returns=self.benchmark_returns_06,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
)
self.sim_params08 = SimulationParameters(
start_session=pd.Timestamp("2008-01-01", tz='UTC'),
end_session=pd.Timestamp("2008-12-31", tz='UTC'),
trading_calendar=self.trading_calendar,
)
def test_factory(self):
returns = [0.1] * 100
r_objects = factory.create_returns_from_list(returns, self.sim_params)
self.assertTrue(r_objects.index[-1] <=
datetime.datetime(
year=2006, month=12, day=31, tzinfo=pytz.utc))
def test_drawdown(self):
returns = factory.create_returns_from_list(
[1.0, -0.5, 0.8, .17, 1.0, -0.1, -0.45], self.sim_params)
# 200, 100, 180, 210.6, 421.2, 379.8, 208.494
metrics = risk.RiskMetricsPeriod(
returns.index[0],
returns.index[-1],
returns,
trading_calendar=self.trading_calendar,
benchmark_returns=self.env.benchmark_returns,
treasury_curves=self.env.treasury_curves,
)
self.assertEqual(metrics.max_drawdown, 0.505)
def test_benchmark_returns_06(self):
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in self.metrics_06.month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['Monthly'])
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['3-Month'])
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['6-month'])
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in self.metrics_06.year_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['year'])
def test_trading_days_06(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = risk.RiskReport(returns, self.sim_params,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.assertEqual([x.num_trading_days for x in metrics.year_periods],
[251])
self.assertEqual([x.num_trading_days for x in metrics.month_periods],
[20, 19, 23, 19, 22, 22, 20, 23, 20, 22, 21, 20])
def test_benchmark_volatility_06(self):
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in self.metrics_06.month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['Monthly'])
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['3-Month'])
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['6-month'])
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in self.metrics_06.year_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['year'])
def test_algorithm_returns_06(self):
np.testing.assert_almost_equal(
[x.algorithm_period_returns
for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['Monthly'])
np.testing.assert_almost_equal(
[x.algorithm_period_returns
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['3-Month'])
np.testing.assert_almost_equal(
[x.algorithm_period_returns
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['6-month'])
np.testing.assert_almost_equal(
[x.algorithm_period_returns
for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_RETURNS['year'])
def test_algorithm_volatility_06(self):
np.testing.assert_almost_equal(
[x.algorithm_volatility
for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['Monthly'])
np.testing.assert_almost_equal(
[x.algorithm_volatility
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['3-Month'])
np.testing.assert_almost_equal(
[x.algorithm_volatility
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['6-month'])
np.testing.assert_almost_equal(
[x.algorithm_volatility
for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_VOLATILITY['year'])
def test_algorithm_sharpe_06(self):
np.testing.assert_almost_equal(
[x.sharpe for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['Monthly'])
np.testing.assert_almost_equal(
[x.sharpe for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['3-Month'])
np.testing.assert_almost_equal(
[x.sharpe for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['6-month'])
np.testing.assert_almost_equal(
[x.sharpe for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SHARPE['year'])
def test_algorithm_downside_risk_06(self):
np.testing.assert_almost_equal(
[x.downside_risk for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['Monthly'],
decimal=4)
np.testing.assert_almost_equal(
[x.downside_risk for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['3-Month'],
decimal=4)
np.testing.assert_almost_equal(
[x.downside_risk for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['6-month'],
decimal=4)
np.testing.assert_almost_equal(
[x.downside_risk for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_DOWNSIDE_RISK['year'],
decimal=4)
def test_algorithm_sortino_06(self):
np.testing.assert_almost_equal(
[x.sortino for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['Monthly'],
decimal=3)
np.testing.assert_almost_equal(
[x.sortino for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['3-Month'],
decimal=3)
np.testing.assert_almost_equal(
[x.sortino for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['6-month'],
decimal=3)
np.testing.assert_almost_equal(
[x.sortino for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_SORTINO['year'],
decimal=3)
def test_algorithm_information_06(self):
self.assertEqual([round(x.information, 3)
for x in self.metrics_06.month_periods],
[0.131,
-0.11,
-0.067,
0.136,
0.301,
-0.387,
0.107,
-0.032,
-0.058,
0.069,
0.095,
-0.123])
self.assertEqual([round(x.information, 3)
for x in self.metrics_06.three_month_periods],
[-0.013,
-0.009,
0.111,
-0.014,
-0.017,
-0.108,
0.011,
-0.004,
0.032,
0.011])
self.assertEqual([round(x.information, 3)
for x in self.metrics_06.six_month_periods],
[-0.013,
-0.014,
-0.003,
-0.002,
-0.011,
-0.041,
0.011])
self.assertEqual([round(x.information, 3)
for x in self.metrics_06.year_periods],
[-0.001])
def test_algorithm_beta_06(self):
np.testing.assert_almost_equal(
[x.beta for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BETA['Monthly'])
np.testing.assert_almost_equal(
[x.beta for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BETA['3-Month'])
np.testing.assert_almost_equal(
[x.beta for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BETA['6-month'])
np.testing.assert_almost_equal(
[x.beta for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BETA['year'])
def test_algorithm_alpha_06(self):
np.testing.assert_almost_equal(
[x.alpha for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['Monthly'])
np.testing.assert_almost_equal(
[x.alpha for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['3-Month'])
np.testing.assert_almost_equal(
[x.alpha for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['6-month'])
np.testing.assert_almost_equal(
[x.alpha for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_ALPHA['year'])
# FIXME: Covariance is not matching excel precisely enough to run the test.
# Month 4 seems to be the problem. Variance is disabled
# just to avoid distraction - it is much closer than covariance
# and can probably pass with 6 significant digits instead of 7.
# re-enable variance, alpha, and beta tests once this is resolved
def test_algorithm_covariance_06(self):
np.testing.assert_almost_equal(
[x.algorithm_covariance for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['Monthly'])
np.testing.assert_almost_equal(
[x.algorithm_covariance
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['3-Month'])
np.testing.assert_almost_equal(
[x.algorithm_covariance
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['6-month'])
np.testing.assert_almost_equal(
[x.algorithm_covariance
for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_COVARIANCE['year'])
def test_benchmark_variance_06(self):
np.testing.assert_almost_equal(
[x.benchmark_variance
for x in self.metrics_06.month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['Monthly'])
np.testing.assert_almost_equal(
[x.benchmark_variance
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['3-Month'])
np.testing.assert_almost_equal(
[x.benchmark_variance
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['6-month'])
np.testing.assert_almost_equal(
[x.benchmark_variance
for x in self.metrics_06.year_periods],
ANSWER_KEY.ALGORITHM_PERIOD_BENCHMARK_VARIANCE['year'])
def test_benchmark_returns_08(self):
returns = factory.create_returns_from_range(self.sim_params08)
metrics = risk.RiskReport(returns, self.sim_params08,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.assertEqual([round(x.benchmark_period_returns, 3)
for x in metrics.month_periods],
[-0.061,
-0.035,
-0.006,
0.048,
0.011,
-0.086,
-0.01,
0.012,
-0.091,
-0.169,
-0.075,
0.008])
self.assertEqual([round(x.benchmark_period_returns, 3)
for x in metrics.three_month_periods],
[-0.099,
0.005,
0.052,
-0.032,
-0.085,
-0.084,
-0.089,
-0.236,
-0.301,
-0.226])
self.assertEqual([round(x.benchmark_period_returns, 3)
for x in metrics.six_month_periods],
[-0.128,
-0.081,
-0.036,
-0.118,
-0.301,
-0.36,
-0.294])
self.assertEqual([round(x.benchmark_period_returns, 3)
for x in metrics.year_periods],
[-0.385])
def test_trading_days_08(self):
returns = factory.create_returns_from_range(self.sim_params08)
metrics = risk.RiskReport(returns, self.sim_params08,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.assertEqual([x.num_trading_days for x in metrics.year_periods],
[253])
self.assertEqual([x.num_trading_days for x in metrics.month_periods],
[21, 20, 20, 22, 21, 21, 22, 21, 21, 23, 19, 22])
def test_benchmark_volatility_08(self):
returns = factory.create_returns_from_range(self.sim_params08)
metrics = risk.RiskReport(returns, self.sim_params08,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.assertEqual([round(x.benchmark_volatility, 3)
for x in metrics.month_periods],
[0.07,
0.058,
0.082,
0.054,
0.041,
0.057,
0.068,
0.06,
0.157,
0.244,
0.195,
0.145])
self.assertEqual([round(x.benchmark_volatility, 3)
for x in metrics.three_month_periods],
[0.12,
0.113,
0.105,
0.09,
0.098,
0.107,
0.179,
0.293,
0.344,
0.34])
self.assertEqual([round(x.benchmark_volatility, 3)
for x in metrics.six_month_periods],
[0.15,
0.149,
0.15,
0.2,
0.308,
0.36,
0.383])
# TODO: ugly, but I can't get the rounded float to match.
# maybe we need a different test that checks the
# difference between the numbers
self.assertEqual([round(x.benchmark_volatility, 3)
for x in metrics.year_periods],
[0.411])
def test_treasury_returns_06(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = risk.RiskReport(returns, self.sim_params,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.assertEqual([round(x.treasury_period_return, 4)
for x in metrics.month_periods],
[0.0037,
0.0034,
0.0039,
0.0038,
0.0040,
0.0037,
0.0043,
0.0043,
0.0038,
0.0044,
0.0043,
0.004])
self.assertEqual([round(x.treasury_period_return, 4)
for x in metrics.three_month_periods],
[0.0114,
0.0116,
0.0122,
0.0125,
0.0129,
0.0127,
0.0123,
0.0128,
0.0125,
0.0127])
self.assertEqual([round(x.treasury_period_return, 4)
for x in metrics.six_month_periods],
[0.0260,
0.0257,
0.0258,
0.0252,
0.0259,
0.0256,
0.0257])
self.assertEqual([round(x.treasury_period_return, 4)
for x in metrics.year_periods],
[0.0500])
def test_benchmarkrange(self):
start_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("2008-01-01", tz='UTC')
)
end_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("2010-01-01", tz='UTC'), direction="previous"
)
sim_params = SimulationParameters(
start_session=start_session,
end_session=end_session,
trading_calendar=self.trading_calendar,
)
returns = factory.create_returns_from_range(sim_params)
metrics = risk.RiskReport(returns, self.sim_params,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
self.check_metrics(metrics, 24, start_session)
# self.check_year_range(
# datetime.datetime(
# year=2008, month=1, day=1, tzinfo=pytz.utc),
# 2)
def test_partial_month(self):
start_session = self.trading_calendar.minute_to_session_label(
pd.Timestamp("1991-01-01", tz='UTC')
)
# 1992 and 1996 were leap years
total_days = 365 * 5 + 2
end_session = start_session + datetime.timedelta(days=total_days)
sim_params90s = SimulationParameters(
start_session=start_session,
end_session=end_session,
trading_calendar=self.trading_calendar,
)
returns = factory.create_returns_from_range(sim_params90s)
returns = returns[:-10] # truncate the returns series to end mid-month
metrics = risk.RiskReport(returns, sim_params90s,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
benchmark_returns=self.env.benchmark_returns)
total_months = 60
self.check_metrics(metrics, total_months, start_session)
def check_metrics(self, metrics, total_months, start_date):
"""
confirm that the right number of riskmetrics were calculated for each
window length.
"""
self.assert_range_length(
metrics.month_periods,
total_months,
1,
start_date
)
self.assert_range_length(
metrics.three_month_periods,
total_months,
3,
start_date
)
self.assert_range_length(
metrics.six_month_periods,
total_months,
6,
start_date
)
self.assert_range_length(
metrics.year_periods,
total_months,
12,
start_date
)
def assert_last_day(self, period_end):
# 30 days has september, april, june and november
if period_end.month in [9, 4, 6, 11]:
self.assertEqual(period_end.day, 30)
# all the rest have 31, except for february
elif(period_end.month != 2):
self.assertEqual(period_end.day, 31)
else:
if calendar.isleap(period_end.year):
self.assertEqual(period_end.day, 29)
else:
self.assertEqual(period_end.day, 28)
def assert_month(self, start_month, actual_end_month):
if start_month == 1:
expected_end_month = 12
else:
expected_end_month = start_month - 1
self.assertEqual(expected_end_month, actual_end_month)
def assert_range_length(self, col, total_months,
period_length, start_date):
if (period_length > total_months):
self.assertEqual(len(col), 0)
else:
self.assertEqual(
len(col),
total_months - (period_length - 1),
"mismatch for total months - \
expected:{total_months}/actual:{actual}, \
period:{period_length}, start:{start_date}, \
calculated end:{end}".format(total_months=total_months,
period_length=period_length,
start_date=start_date,
end=col[-1]._end_session,
actual=len(col))
)
self.assert_month(start_date.month, col[-1]._end_session.month)
self.assert_last_day(col[-1]._end_session)
def test_sparse_benchmark(self):
benchmark_returns = self.benchmark_returns_06.copy()
# Set every other day to nan.
benchmark_returns.iloc[::2] = np.nan
report = risk.RiskReport(
self.algo_returns_06,
self.sim_params,
benchmark_returns=benchmark_returns,
trading_calendar=self.trading_calendar,
treasury_curves=self.env.treasury_curves,
)
for risk_period in chain.from_iterable(itervalues(report.to_dict())):
self.assertIsNone(risk_period['beta'])
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