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TST: Update to empyrical, increase test coverage
ENH: Resolve rebase conflict by using updated example_data.tar TST: Increase test coverage for risk portion of zipline
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Ana Ruelas committed
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@@ -1,4 +1,4 @@
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"%PYTHON%" setup.py install
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"%PYTHON%" setup.py install
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if errorlevel 1 exit 1
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:: Add more build steps here, if they are necessary.
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@@ -1,6 +1,6 @@
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#!/bin/bash
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$PYTHON setup.py install
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$PYTHON setup.py install
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# Add more build steps here, if they are necessary.
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@@ -0,0 +1,33 @@
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package:
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name: empyrical
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version: "0.1.9"
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source:
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fn: empyrical-0.1.9.tar.gz
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url: https://pypi.python.org/packages/15/6b/de1d277d4342d2cecc8134f4935248853f32299cdd9b01728b0ec420c350/empyrical-0.1.9.tar.gz
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md5: 2c8b928cae192fc9cb8b7104608eec56
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requirements:
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build:
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- python
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- setuptools
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- numpy x.x
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- pandas >=0.16.1
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- scipy >=0.15.1
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- bottleneck >=1.0.0
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run:
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- python
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- numpy x.x
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- pandas >=0.16.1
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- scipy >=0.15.1
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- bottleneck >=1.0.0
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about:
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home: https://github.com/quantopian/empyrical
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license: Apache Software License
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summary: 'empyrical is a Python library with performance and risk statistics\ncommonly used in quantitative finance'
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# See
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# http://docs.continuum.io/conda/build.html for
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# more information about meta.yaml
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@@ -1,27 +0,0 @@
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package:
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name: qrisk
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version: "0.1.4"
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source:
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fn: qrisk-0.1.4.tar.gz
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url: https://files.pythonhosted.org/packages/49/c9/81924d56e8173c4e92d60f5e59143dd345a0bf015508f85683ed9c2efd27/qrisk-0.1.4.tar.gz
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md5: 24a76bcb960e82ade86bf115e87c5f53
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requirements:
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build:
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- python
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- setuptools
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- numpy x.x
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- pandas >=0.16.1
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- scipy >=0.15.1
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run:
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- python
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- numpy x.x
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- pandas >=0.16.1
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- scipy >=0.15.1
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about:
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home: https://github.com/quantopian/qrisk
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license: Apache Software License
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summary: 'qrisk is a Python library with performance and risk statistics\ncommonly used in quantitative finance'
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@@ -64,4 +64,4 @@ intervaltree==2.1.0
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cachetools==1.1.5
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# For financial risk calculations
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qrisk==0.1.4
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empyrical>=0.1.9
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{"last_row": {"0": 2768, "5": 16338, "6": 19107, "4": 13569, "2": 8306, "8": 24645, "1": 5537, "3": 10800, "7": 21876}, "calendar": [631238400000000000, 631324800000000000, 631411200000000000, 631497600000000000, 631756800000000000, 631843200000000000, 631929600000000000, 632016000000000000, 632102400000000000, 632361600000000000, 632448000000000000, 632534400000000000, 632620800000000000, 632707200000000000, 632966400000000000, 633052800000000000, 633139200000000000, 633225600000000000, 633312000000000000, 633571200000000000, 633657600000000000, 633744000000000000, 633830400000000000, 633916800000000000, 634176000000000000, 634262400000000000, 634348800000000000, 634435200000000000, 634521600000000000, 634780800000000000, 634867200000000000, 634953600000000000, 635040000000000000, 635126400000000000, 635472000000000000, 635558400000000000, 635644800000000000, 635731200000000000, 635990400000000000, 636076800000000000, 636163200000000000, 636249600000000000, 636336000000000000, 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{"names": ["open", "high", "low", "close", "volume", "day", "id"]}
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{}
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{"market_closes": [10521900, 10523340, 10524780, 10526220, 10530540, 10531980, 10533420, 10534860, 10536300, 10540620, 10542060, 10543500, 10544940, 10546380, 10550700, 10552140, 10553580, 10555020, 10556460, 10560780, 10562220, 10563660, 10565100, 10566540, 10570860, 10572300, 10573740, 10575180, 10576620, 10580940, 10582380, 10583820, 10585260, 10586700, 10592460, 10593900, 10595340, 10596780, 10601100, 10602540, 10603980, 10605420, 10606860, 10611180, 10612620, 10614060, 10615500, 10616940, 10621260, 10622700, 10624140, 10625580, 10627020, 10631340, 10632780, 10634220, 10635660, 10637100, 10641420, 10642860, 10644300, 10645740, 10647180, 10651440, 10652880, 10654320, 10655760, 10657200, 10661520, 10662960, 10664400, 10665840, 10671600, 10673040, 10674480, 10675920, 10677360, 10681680, 10683120, 10684560, 10686000, 10687440, 10691760, 10693200, 10694640, 10696080, 10697520, 10701840, 10703280, 10704720, 10706160, 10707600, 10711920, 10713360, 10714800, 10716240, 10717680, 10722000, 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Whitespace-only changes.
@@ -17,15 +17,11 @@ 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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from zipline.utils import factory
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import pandas as pd
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from zipline.testing.fixtures import WithTradingEnvironment, ZiplineTestCase
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from zipline.finance.trading import SimulationParameters
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from . import answer_key
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ANSWER_KEY = answer_key.ANSWER_KEY
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RETURNS_BASE = 0.01
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RETURNS = [RETURNS_BASE] * 251
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@@ -38,16 +34,10 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
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def init_instance_fixtures(self):
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super(TestRisk, self).init_instance_fixtures()
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<<<<<<< 30f5a8fcfa4a194f05d58f50cf0a2b06dd8085cc
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start_session = pd.Timestamp("2006-01-01", tz='UTC')
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end_session = pd.Timestamp("2006-12-29", tz='UTC')
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=======
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start_date = pd.Timestamp('2006-01-01', tz=pytz.utc)
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end_date = pd.Timestamp('2006-12-29', tz=pytz.utc)
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>>>>>>> ENH: Change datetime.datetime to pd.Timestamp in tests
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self.sim_params = SimulationParameters(
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start_session=start_session,
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end_session=end_session,
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@@ -150,3 +140,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
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all(isinstance(x, float)
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for x in self.cumulative_metrics.max_drawdowns),
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True)
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def test_representation(self):
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assert all([metric in self.cumulative_metrics.__repr__() for metric in
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self.cumulative_metrics.METRIC_NAMES])
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+147
-72
@@ -17,8 +17,6 @@ import datetime
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import calendar
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import pandas as pd
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import numpy as np
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import pytz
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import pandas as pd
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import zipline.finance.risk as risk
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from zipline.utils import factory
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@@ -26,6 +24,8 @@ from zipline.utils import factory
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from zipline.finance.trading import SimulationParameters
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from zipline.testing.fixtures import WithTradingEnvironment, ZiplineTestCase
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from zipline.finance.risk.period import RiskMetricsPeriod
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RETURNS_BASE = 0.01
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RETURNS = [RETURNS_BASE] * 251
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@@ -38,17 +38,14 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
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def init_instance_fixtures(self):
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super(TestRisk, self).init_instance_fixtures()
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||||
|
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start_session = pd.Timestamp("2006-01-01", tz='UTC')
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|
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end_session = self.trading_calendar.minute_to_session_label(
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self.start_session = pd.Timestamp("2006-01-01", tz='UTC')
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self.end_session = self.trading_calendar.minute_to_session_label(
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pd.Timestamp("2006-12-31", tz='UTC'),
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direction="previous"
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)
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||||
|
||||
self.sim_params = SimulationParameters(
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start_session=start_session,
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end_session=end_session,
|
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start_session=self.start_session,
|
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end_session=self.end_session,
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trading_calendar=self.trading_calendar,
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||||
)
|
||||
self.algo_returns = factory.create_returns_from_list(
|
||||
@@ -63,16 +60,15 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
self.algo_returns,
|
||||
self.sim_params,
|
||||
benchmark_returns=self.benchmark_returns,
|
||||
trading_schedule=self.trading_schedule,
|
||||
trading_calendar=self.trading_calendar,
|
||||
treasury_curves=self.env.treasury_curves,
|
||||
)
|
||||
|
||||
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] <= self.end_date)
|
||||
self.assertTrue(r_objects.index[0] >= self.start_date)
|
||||
self.assertTrue(r_objects.sample().values[0] == 0.1)
|
||||
self.assertTrue(r_objects.index[-1] <=
|
||||
pd.Timestamp('2006-12-31', tz='UTC'))
|
||||
|
||||
def test_drawdown(self):
|
||||
np.testing.assert_equal(
|
||||
@@ -123,7 +119,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
[20, 19, 23, 19, 22, 22, 20, 23, 20, 22, 21, 20])
|
||||
|
||||
def test_benchmark_volatility(self):
|
||||
# Volatility is calculated by a qrisk function so testing
|
||||
# Volatility is calculated by a empyrical function so testing
|
||||
# of period volatility will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -170,7 +166,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
DECIMAL_PLACES)
|
||||
|
||||
def test_algorithm_volatility(self):
|
||||
# Volatility is calculated by a qrisk function so testing
|
||||
# Volatility is calculated by a empyrical function so testing
|
||||
# of period volatility will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -191,7 +187,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
True)
|
||||
|
||||
def test_algorithm_sharpe(self):
|
||||
# The sharpe ratio is calculated by a qrisk function so testing
|
||||
# The sharpe ratio is calculated by a empyrical function so testing
|
||||
# of period sharpe ratios will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -212,7 +208,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
True)
|
||||
|
||||
def test_algorithm_downside_risk(self):
|
||||
# Downside risk is calculated by a qrisk function so testing
|
||||
# Downside risk is calculated by a empyrical function so testing
|
||||
# of period downside risk will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -233,7 +229,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
True)
|
||||
|
||||
def test_algorithm_sortino(self):
|
||||
# The sortino ratio is calculated by a qrisk function so testing
|
||||
# The sortino ratio is calculated by a empyrical function so testing
|
||||
# of period sortino ratios will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -254,9 +250,9 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
True)
|
||||
|
||||
def test_algorithm_information(self):
|
||||
# The information ratio is calculated by a qrisk function so testing
|
||||
# of period information ratio will be limited to determine if the value
|
||||
# is numerical. This tests for its existence and format.
|
||||
# The information ratio is calculated by a empyrical function
|
||||
# testing of period information ratio will be limited to determine
|
||||
# if the value is numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
all(isinstance(x.information, float)
|
||||
for x in self.metrics.month_periods),
|
||||
@@ -275,7 +271,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
True)
|
||||
|
||||
def test_algorithm_beta(self):
|
||||
# Beta is calculated by a qrisk function so testing
|
||||
# Beta is calculated by a empyrical function so testing
|
||||
# of period beta will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -296,7 +292,7 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
True)
|
||||
|
||||
def test_algorithm_alpha(self):
|
||||
# Alpha is calculated by a qrisk function so testing
|
||||
# Alpha is calculated by a empyrical function so testing
|
||||
# of period alpha will be limited to determine if the value is
|
||||
# numerical. This tests for its existence and format.
|
||||
np.testing.assert_equal(
|
||||
@@ -316,49 +312,6 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
for x in self.metrics.year_periods),
|
||||
True)
|
||||
|
||||
def test_algorithm_covariance(self):
|
||||
np.testing.assert_almost_equal(
|
||||
[x.algorithm_covariance for x in self.metrics.month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
DECIMAL_PLACES)
|
||||
np.testing.assert_almost_equal(
|
||||
[x.algorithm_covariance
|
||||
for x in self.metrics.three_month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
DECIMAL_PLACES)
|
||||
np.testing.assert_almost_equal(
|
||||
[x.algorithm_covariance
|
||||
for x in self.metrics.six_month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
DECIMAL_PLACES)
|
||||
np.testing.assert_almost_equal(
|
||||
[x.algorithm_covariance
|
||||
for x in self.metrics.year_periods],
|
||||
[0.0],
|
||||
DECIMAL_PLACES)
|
||||
|
||||
def test_benchmark_variance(self):
|
||||
np.testing.assert_almost_equal(
|
||||
[x.benchmark_variance
|
||||
for x in self.metrics.month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
DECIMAL_PLACES)
|
||||
np.testing.assert_almost_equal(
|
||||
[x.benchmark_variance
|
||||
for x in self.metrics.three_month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
DECIMAL_PLACES)
|
||||
np.testing.assert_almost_equal(
|
||||
[x.benchmark_variance
|
||||
for x in self.metrics.six_month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
DECIMAL_PLACES)
|
||||
np.testing.assert_almost_equal(
|
||||
[x.benchmark_variance
|
||||
for x in self.metrics.year_periods],
|
||||
[0.0],
|
||||
DECIMAL_PLACES)
|
||||
|
||||
def test_treasury_returns(self):
|
||||
returns = factory.create_returns_from_range(self.sim_params)
|
||||
metrics = risk.RiskReport(returns, self.sim_params,
|
||||
@@ -407,13 +360,33 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
[0.0500])
|
||||
|
||||
def test_benchmarkrange(self):
|
||||
self.check_year_range(
|
||||
pd.Timestamp('2008-01-01', tz=pytz.utc),
|
||||
2)
|
||||
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)
|
||||
|
||||
def test_partial_month(self):
|
||||
|
||||
start = pd.Timestamp('1991-01-01', tz=pytz.utc)
|
||||
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
|
||||
@@ -504,6 +477,108 @@ class TestRisk(WithTradingEnvironment, ZiplineTestCase):
|
||||
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)
|
||||
|
||||
self.assert_month(start_date.month, col[-1].end_date.month)
|
||||
self.assert_last_day(col[-1].end_date)
|
||||
def test_algorithm_leverages(self):
|
||||
# Max leverage for an algorithm with 'None' as leverage is 0.
|
||||
np.testing.assert_equal(
|
||||
[x.max_leverage for x in self.metrics.month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
|
||||
np.testing.assert_equal(
|
||||
[x.max_leverage for x in self.metrics.three_month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
|
||||
np.testing.assert_equal(
|
||||
[x.max_leverage for x in self.metrics.six_month_periods],
|
||||
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
|
||||
np.testing.assert_equal(
|
||||
[x.max_leverage for x in self.metrics.year_periods],
|
||||
[0.0])
|
||||
|
||||
def test_returns_beyond_treasury(self):
|
||||
# The last treasury value is used when return dates go beyond
|
||||
# treasury curve data
|
||||
treasury_curves = self.env.treasury_curves
|
||||
treasury = treasury_curves[treasury_curves.index < self.start_session]
|
||||
|
||||
test_period = RiskMetricsPeriod(
|
||||
start_session=self.start_session,
|
||||
end_session=self.end_session,
|
||||
returns=self.algo_returns,
|
||||
benchmark_returns=self.benchmark_returns,
|
||||
trading_calendar=self.trading_calendar,
|
||||
treasury_curves=treasury,
|
||||
algorithm_leverages=[.01, .02, .03]
|
||||
)
|
||||
assert test_period.treasury_curves.equals(treasury[-1:])
|
||||
# This return period has a list instead of None for algorithm_leverages
|
||||
# Confirm that max_leverage is set to the max of those values
|
||||
assert test_period.max_leverage == .03
|
||||
|
||||
def test_index_mismatch_exception(self):
|
||||
# An exception is raised when returns and benchmark returns
|
||||
# have indexes that do not match
|
||||
bench_params = SimulationParameters(
|
||||
start_session=pd.Timestamp("2006-02-01", tz='UTC'),
|
||||
end_session=pd.Timestamp("2006-02-28", tz='UTC'),
|
||||
trading_calendar=self.trading_calendar,
|
||||
)
|
||||
benchmark = factory.create_returns_from_list(
|
||||
[BENCHMARK_BASE]*19,
|
||||
bench_params
|
||||
)
|
||||
with np.testing.assert_raises(Exception):
|
||||
RiskMetricsPeriod(
|
||||
start_session=self.start_session,
|
||||
end_session=self.end_session,
|
||||
returns=self.algo_returns,
|
||||
benchmark_returns=benchmark,
|
||||
trading_calendar=self.trading_calendar,
|
||||
treasury_curves=self.env.treasury_curves,
|
||||
)
|
||||
|
||||
def test_sharpe_value_when_null(self):
|
||||
# Sharpe is displayed as '0.0' instead of np.nan
|
||||
null_returns = factory.create_returns_from_list(
|
||||
[0.0]*251,
|
||||
self.sim_params
|
||||
)
|
||||
test_period = RiskMetricsPeriod(
|
||||
start_session=self.start_session,
|
||||
end_session=self.end_session,
|
||||
returns=null_returns,
|
||||
benchmark_returns=self.benchmark_returns,
|
||||
trading_calendar=self.trading_calendar,
|
||||
treasury_curves=self.env.treasury_curves,
|
||||
)
|
||||
assert test_period.sharpe == 0.0
|
||||
|
||||
def test_representation(self):
|
||||
test_period = RiskMetricsPeriod(
|
||||
start_session=self.start_session,
|
||||
end_session=self.end_session,
|
||||
returns=self.algo_returns,
|
||||
benchmark_returns=self.benchmark_returns,
|
||||
trading_calendar=self.trading_calendar,
|
||||
treasury_curves=self.env.treasury_curves,
|
||||
)
|
||||
metrics = [
|
||||
"algorithm_period_returns",
|
||||
"benchmark_period_returns",
|
||||
"excess_return",
|
||||
"num_trading_days",
|
||||
"benchmark_volatility",
|
||||
"algorithm_volatility",
|
||||
"sharpe",
|
||||
"sortino",
|
||||
"information",
|
||||
"beta",
|
||||
"alpha",
|
||||
"max_drawdown",
|
||||
"max_leverage",
|
||||
"algorithm_returns",
|
||||
"benchmark_returns",
|
||||
]
|
||||
representation = test_period.__repr__()
|
||||
|
||||
assert all([metric in representation for metric in metrics])
|
||||
@@ -873,10 +873,10 @@ def before_trading_start(context, data):
|
||||
|
||||
res2 = algo2.run(self.data_portal)
|
||||
|
||||
# FIXME I think we are getting Nans due to fixed benchmark,
|
||||
# so dropping them for now.
|
||||
res1 = res1.fillna(method='ffill')
|
||||
res2 = res2.fillna(method='ffill')
|
||||
# There are some np.NaN values in the first row because there is not
|
||||
# enough data to calculate the metric, e.g. beta.
|
||||
res1 = res1.fillna(value=0)
|
||||
res2 = res2.fillna(value=0)
|
||||
|
||||
np.testing.assert_array_equal(res1, res2)
|
||||
|
||||
|
||||
@@ -1191,9 +1191,9 @@ class DataPortal(object):
|
||||
|
||||
if data_frequency == "minute":
|
||||
freq_str = "1m"
|
||||
calculated_bar_count = self._get_minute_count_for_transform(
|
||||
calculated_bar_count = int(self._get_minute_count_for_transform(
|
||||
dt, bars
|
||||
)
|
||||
))
|
||||
else:
|
||||
freq_str = "1d"
|
||||
calculated_bar_count = bars
|
||||
|
||||
@@ -27,7 +27,7 @@ from . risk import (
|
||||
choose_treasury
|
||||
)
|
||||
|
||||
from qrisk import (
|
||||
from empyrical import (
|
||||
alpha,
|
||||
annual_volatility,
|
||||
beta,
|
||||
|
||||
@@ -16,8 +16,6 @@
|
||||
import functools
|
||||
|
||||
import logbook
|
||||
import numpy as np
|
||||
import numpy.linalg as la
|
||||
|
||||
from six import iteritems
|
||||
|
||||
@@ -26,10 +24,11 @@ import pandas as pd
|
||||
from . import risk
|
||||
from . risk import check_entry
|
||||
|
||||
from qrisk import (
|
||||
from empyrical import (
|
||||
alpha,
|
||||
annual_volatility,
|
||||
beta,
|
||||
cum_returns,
|
||||
downside_risk,
|
||||
information_ratio,
|
||||
max_drawdown,
|
||||
@@ -46,7 +45,6 @@ choose_treasury = functools.partial(risk.choose_treasury,
|
||||
class RiskMetricsPeriod(object):
|
||||
def __init__(self, start_session, end_session, returns, trading_calendar,
|
||||
treasury_curves, benchmark_returns, algorithm_leverages=None):
|
||||
|
||||
if treasury_curves.index[-1] >= start_session:
|
||||
mask = ((treasury_curves.index >= start_session) &
|
||||
(treasury_curves.index <= end_session))
|
||||
@@ -79,10 +77,10 @@ class RiskMetricsPeriod(object):
|
||||
|
||||
def calculate_metrics(self):
|
||||
self.benchmark_period_returns = \
|
||||
self.calculate_period_returns(self.benchmark_returns)
|
||||
cum_returns(self.benchmark_returns).iloc[-1]
|
||||
|
||||
self.algorithm_period_returns = \
|
||||
self.calculate_period_returns(self.algorithm_returns)
|
||||
cum_returns(self.algorithm_returns).iloc[-1]
|
||||
|
||||
if not self.algorithm_returns.index.equals(
|
||||
self.benchmark_returns.index
|
||||
@@ -115,7 +113,6 @@ class RiskMetricsPeriod(object):
|
||||
)
|
||||
self.sharpe = sharpe_ratio(
|
||||
self.algorithm_returns,
|
||||
self.benchmark_returns
|
||||
)
|
||||
# The consumer currently expects a 0.0 value for sharpe in period,
|
||||
# this differs from cumulative which was np.nan.
|
||||
@@ -138,9 +135,6 @@ class RiskMetricsPeriod(object):
|
||||
self.algorithm_returns,
|
||||
self.benchmark_returns
|
||||
)
|
||||
self.algorithm_covariance, self.benchmark_variance, \
|
||||
self.condition_number, self.eigen_values \
|
||||
= self.calculate_covariance()
|
||||
self.beta = beta(
|
||||
self.algorithm_returns,
|
||||
self.benchmark_returns
|
||||
@@ -193,16 +187,12 @@ class RiskMetricsPeriod(object):
|
||||
"sharpe",
|
||||
"sortino",
|
||||
"information",
|
||||
"algorithm_covariance",
|
||||
"benchmark_variance",
|
||||
"beta",
|
||||
"alpha",
|
||||
"max_drawdown",
|
||||
"max_leverage",
|
||||
"algorithm_returns",
|
||||
"benchmark_returns",
|
||||
"condition_number",
|
||||
"eigen_values"
|
||||
]
|
||||
|
||||
for metric in metrics:
|
||||
@@ -226,45 +216,6 @@ class RiskMetricsPeriod(object):
|
||||
returns = returns[mask]
|
||||
return returns
|
||||
|
||||
def calculate_period_returns(self, returns):
|
||||
period_returns = (1. + returns).prod() - 1
|
||||
return period_returns
|
||||
|
||||
def calculate_covariance(self):
|
||||
"""
|
||||
|
||||
.. math::
|
||||
|
||||
\\beta_a = \\frac{\mathrm{Cov}(r_a,r_p)}{\mathrm{Var}(r_p)}
|
||||
|
||||
http://en.wikipedia.org/wiki/Beta_(finance)
|
||||
"""
|
||||
# it doesn't make much sense to calculate beta for less than two days,
|
||||
# so return nan.
|
||||
if len(self.algorithm_returns) < 2:
|
||||
return np.nan, np.nan, np.nan, []
|
||||
|
||||
returns_matrix = np.vstack([self.algorithm_returns,
|
||||
self.benchmark_returns])
|
||||
C = np.cov(returns_matrix, ddof=1)
|
||||
|
||||
# If there are missing benchmark values, then we can't calculate the
|
||||
# beta.
|
||||
if not np.isfinite(C).all():
|
||||
return np.nan, np.nan, np.nan, []
|
||||
|
||||
eigen_values = la.eigvals(C)
|
||||
condition_number = max(eigen_values) / min(eigen_values)
|
||||
algorithm_covariance = C[0][1]
|
||||
benchmark_variance = C[1][1]
|
||||
|
||||
return (
|
||||
algorithm_covariance,
|
||||
benchmark_variance,
|
||||
condition_number,
|
||||
eigen_values
|
||||
)
|
||||
|
||||
def calculate_max_leverage(self):
|
||||
if self.algorithm_leverages is None:
|
||||
return 0.0
|
||||
|
||||
Reference in new issue
Block a user