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https://github.com/wassname/catalyst.git
synced 2026-08-06 13:00:45 +08:00
MAINT: Split apart risk metrics classes.
Also remove test that compares risk metrics batch to iterative, since the 'iterative' calculations, replaced by the cumulative calculations, will intentionally drift from the results in the risk report due to annualization and other factors. Work towards having separate calculations for the fixed periods versus the cumulative/headline risk metrics. Different sumbodules for each type should help make the calculations type distinct and easier to find.
This commit is contained in:
Eddie Hebert
@@ -92,9 +92,9 @@ class TestRisk(unittest.TestCase):
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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.RiskMetricsBatch(returns[0].date,
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returns[-1].date,
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returns)
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metrics = risk.RiskMetricsPeriod(returns[0].date,
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returns[-1].date,
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returns)
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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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@@ -1,164 +0,0 @@
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#
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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 numbers
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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 pandas as pd
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import zipline.finance.risk as risk
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import zipline.finance.trading as trading
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from zipline.finance.trading import SimulationParameters
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from zipline.protocol import DailyReturn
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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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def test_risk_metrics_returns(self):
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trading.environment = trading.TradingEnvironment()
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# Advance start date to first date in the trading calendar
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if trading.environment.is_trading_day(self.start_date):
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start_date = self.start_date
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else:
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start_date = trading.environment.next_trading_day(self.start_date)
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self.all_benchmark_returns = pd.Series({
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x.date: x.returns
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for x in trading.environment.benchmark_returns
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if x.date >= self.start_date
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})
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start_index = trading.environment.trading_days.searchsorted(start_date)
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end_date = trading.environment.trading_days[
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start_index + len(RETURNS)]
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sim_params = SimulationParameters(start_date, end_date)
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risk_metrics_refactor = risk.RiskMetricsIterative(sim_params)
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todays_date = 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 = 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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try:
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risk_metrics_original = risk.RiskMetricsBatch(
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start_date=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),
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risk_metrics_refactor.update,
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todays_date,
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self.all_benchmark_returns[todays_return_obj.date]
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)
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continue
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risk_metrics_refactor.update(
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todays_date,
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ret,
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self.all_benchmark_returns[todays_return_obj.date])
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# Move forward day counter to next trading day
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todays_date = trading.environment.next_trading_day(todays_date)
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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.algorithm_returns.index[-1])
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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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np.testing.assert_allclose(
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risk_metrics_original.benchmark_returns,
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risk_metrics_refactor.benchmark_returns,
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rtol=0.001
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)
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np.testing.assert_allclose(
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risk_metrics_original.algorithm_returns,
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risk_metrics_refactor.algorithm_returns,
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rtol=0.001
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)
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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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if isinstance(risk_original_dict[measure], numbers.Real):
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np.testing.assert_allclose(
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risk_original_dict[measure],
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risk_refactor_dict[measure],
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rtol=0.001,
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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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)
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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=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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@@ -43,7 +43,7 @@ class TestMinuteRisk(unittest.TestCase):
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def test_minute_risk(self):
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risk_metrics = risk.RiskMetricsIterative(self.sim_params)
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risk_metrics = risk.RiskMetricsCumulative(self.sim_params)
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first_dt = self.sim_params.first_open
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second_dt = self.sim_params.first_open + datetime.timedelta(minutes=1)
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@@ -171,17 +171,17 @@ class PerformanceTracker(object):
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index=trading.environment.trading_days)
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self.intraday_risk_metrics = None
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self.cumulative_risk_metrics = \
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risk.RiskMetricsIterative(self.sim_params)
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risk.RiskMetricsCumulative(self.sim_params)
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elif self.emission_rate == 'minute':
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self.all_benchmark_returns = pd.Series(index=pd.date_range(
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self.sim_params.first_open, self.sim_params.last_close,
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freq='Min'))
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self.intraday_risk_metrics = \
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risk.RiskMetricsIterative(self.sim_params)
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risk.RiskMetricsCumulative(self.sim_params)
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self.cumulative_risk_metrics = \
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risk.RiskMetricsIterative(self.sim_params)
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risk.RiskMetricsCumulative(self.sim_params)
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self.cumulative_risk_metrics.initialize_daily_indices()
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self.minute_performance = PerformancePeriod(
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@@ -379,7 +379,7 @@ class PerformanceTracker(object):
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def handle_intraday_close(self):
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self.intraday_risk_metrics = \
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risk.RiskMetricsIterative(self.sim_params)
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risk.RiskMetricsCumulative(self.sim_params)
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# increment the day counter before we move markers forward.
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self.day_count += 1.0
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# move the market day markers forward
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@@ -292,7 +292,7 @@ that date doesn't exceed treasury history range."
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raise Exception(message)
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class RiskMetricsBase(object):
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class RiskMetricsPeriod(object):
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def __init__(self, start_date, end_date, returns,
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benchmark_returns=None):
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@@ -536,12 +536,10 @@ class RiskMetricsBase(object):
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return 1.0 - math.exp(max_drawdown)
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class RiskMetricsIterative(RiskMetricsBase):
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"""Iterative version of RiskMetrics.
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Should behave exaclty like RiskMetricsBatch.
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class RiskMetricsCumulative(object):
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"""
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:Usage:
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Instantiate RiskMetricsIterative once.
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Instantiate RiskMetricsCumulative once.
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Call update() method on each dt to update the metrics.
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"""
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@@ -814,9 +812,39 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
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self.benchmark_period_returns[-1],
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self.beta[-1])
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def calculate_volatility(self, daily_returns):
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return np.std(daily_returns, ddof=1) * math.sqrt(self.num_trading_days)
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class RiskMetricsBatch(RiskMetricsBase):
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pass
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def calculate_beta(self):
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"""
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.. math::
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\\beta_a = \\frac{\mathrm{Cov}(r_a,r_p)}{\mathrm{Var}(r_p)}
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http://en.wikipedia.org/wiki/Beta_(finance)
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"""
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#it doesn't make much sense to calculate beta for less than two days,
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#so return none.
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if len(self.algorithm_returns) < 2:
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return 0.0, 0.0, 0.0, 0.0, []
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returns_matrix = np.vstack([self.algorithm_returns,
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self.benchmark_returns])
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C = np.cov(returns_matrix, ddof=1)
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eigen_values = la.eigvals(C)
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condition_number = max(eigen_values) / min(eigen_values)
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algorithm_covariance = C[0][1]
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benchmark_variance = C[1][1]
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beta = algorithm_covariance / benchmark_variance
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return (
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beta,
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algorithm_covariance,
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benchmark_variance,
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condition_number,
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eigen_values
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)
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class RiskReport(object):
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@@ -889,7 +917,7 @@ class RiskReport(object):
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cur_end = cur_start + relativedelta(months=months_per) - one_day
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if(cur_end > the_end):
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break
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cur_period_metrics = RiskMetricsBatch(
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cur_period_metrics = RiskMetricsPeriod(
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start_date=cur_start,
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end_date=cur_end,
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returns=self.algorithm_returns,
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