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Merge pull request #382 from quantopian/talib_optional

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Make talib an optional dependency.
This commit is contained in:
Thomas Wiecki
2014-08-06 15:13:00 +02:00
parent 3c8363722e 3f1c3b39ad
commit bb6e9c26c0
6 changed files with 158 additions and 136 deletions
Download Patch File Download Diff File Expand all files Collapse all files
conda/zipline/meta.yaml
-1
View File
@@ -19,7 +19,6 @@ requirements:
- pandas
- scipy
- matplotlib
- ta-lib
- logbook
test:
etc/requirements.txt
-7
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@@ -17,12 +17,5 @@ statsmodels==0.5.0
python-dateutil==2.2
six==1.6.1
# Cython is required for TA-Lib
Cython==0.20.1
# This --allow syntax is for compatibility with pip >= 1.5
# However, this is backwards incompatible change, since previous
# versions of pip do not support that flag.
--allow-external TA-Lib --allow-unverified TA-Lib TA-Lib==0.4.8
# For fetching remote data
requests==2.3.0
etc/requirements_dev.txt
+7
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@@ -24,3 +24,10 @@ tornado==3.2.1
pyparsing==2.0.2
Markdown==2.4.1
# Cython is required for TA-Lib
Cython==0.20.1
# This --allow syntax is for compatibility with pip >= 1.5
# However, this is backwards incompatible change, since previous
# versions of pip do not support that flag.
--allow-external TA-Lib --allow-unverified TA-Lib TA-Lib==0.4.8
setup.py
+4 -1
View File
@@ -1,6 +1,6 @@
#!/usr/bin/env python
#
# Copyright 2013 Quantopian, Inc.
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -71,5 +71,8 @@ setup(
'pandas',
'six'
],
extras_require = {
'talib': ["talib"],
},
url="https://github.com/quantopian/zipline"
)
tests/test_transforms.py
+1 -127
View File
@@ -15,10 +15,9 @@
import pytz
import numpy as np
import pandas as pd
from datetime import timedelta, datetime
from unittest import TestCase, skip
from unittest import TestCase
from six.moves import range
@@ -33,8 +32,6 @@ from zipline.transforms import MovingStandardDev
from zipline.transforms import Returns
import zipline.utils.factory as factory
from zipline.test_algorithms import TALIBAlgorithm
def to_dt(msg):
return Event({'dt': msg})
@@ -276,126 +273,3 @@ class TestFinanceTransforms(TestCase):
self.assertIsNone(v2)
continue
self.assertEquals(round(v1, 5), round(v2, 5))
############################################################
# Test TALIB
import talib
import zipline.transforms.ta as ta
class TestTALIB(TestCase):
def setUp(self):
setup_logger(self)
sim_params = factory.create_simulation_parameters(
start=datetime(1990, 1, 1, tzinfo=pytz.utc),
end=datetime(1990, 3, 30, tzinfo=pytz.utc))
self.source, self.panel = \
factory.create_test_panel_ohlc_source(sim_params)
@skip
def test_talib_with_default_params(self):
BLACKLIST = ['make_transform', 'BatchTransform',
# TODO: Figure out why MAVP generates a KeyError
'MAVP']
names = [name for name in dir(ta) if name[0].isupper()
and name not in BLACKLIST]
for name in names:
print(name)
zipline_transform = getattr(ta, name)(sid=0)
talib_fn = getattr(talib.abstract, name)
start = datetime(1990, 1, 1, tzinfo=pytz.utc)
end = start + timedelta(days=zipline_transform.lookback + 10)
sim_params = factory.create_simulation_parameters(
start=start, end=end)
source, panel = \
factory.create_test_panel_ohlc_source(sim_params)
algo = TALIBAlgorithm(talib=zipline_transform)
algo.run(source)
zipline_result = np.array(
algo.talib_results[zipline_transform][-1])
talib_data = dict()
data = zipline_transform.window
# TODO: Figure out if we are clobbering the tests by this
# protection against empty windows
if not data:
continue
for key in ['open', 'high', 'low', 'volume']:
if key in data:
talib_data[key] = data[key][0].values
talib_data['close'] = data['price'][0].values
expected_result = talib_fn(talib_data)
if isinstance(expected_result, list):
expected_result = np.array([e[-1] for e in expected_result])
else:
expected_result = np.array(expected_result[-1])
if not (np.all(np.isnan(zipline_result))
and np.all(np.isnan(expected_result))):
self.assertTrue(np.allclose(zipline_result, expected_result))
else:
print('--- NAN')
# reset generator so next iteration has data
# self.source, self.panel = \
# factory.create_test_panel_ohlc_source(self.sim_params)
def test_multiple_talib_with_args(self):
zipline_transforms = [ta.MA(timeperiod=10),
ta.MA(timeperiod=25)]
talib_fn = talib.abstract.MA
algo = TALIBAlgorithm(talib=zipline_transforms)
algo.run(self.source)
# Test if computed values match those computed by pandas rolling mean.
sid = 0
talib_values = np.array([x[sid] for x in
algo.talib_results[zipline_transforms[0]]])
np.testing.assert_array_equal(talib_values,
pd.rolling_mean(self.panel[0]['price'],
10).values)
talib_values = np.array([x[sid] for x in
algo.talib_results[zipline_transforms[1]]])
np.testing.assert_array_equal(talib_values,
pd.rolling_mean(self.panel[0]['price'],
25).values)
for t in zipline_transforms:
talib_result = np.array(algo.talib_results[t][-1])
talib_data = dict()
data = t.window
# TODO: Figure out if we are clobbering the tests by this
# protection against empty windows
if not data:
continue
for key in ['open', 'high', 'low', 'volume']:
if key in data:
talib_data[key] = data[key][0].values
talib_data['close'] = data['price'][0].values
expected_result = talib_fn(talib_data, **t.call_kwargs)[-1]
np.testing.assert_allclose(talib_result, expected_result)
def test_talib_with_minute_data(self):
ma_one_day_minutes = ta.MA(timeperiod=10, bars='minute')
# Assert that the BatchTransform window length is enough to cover
# the amount of minutes in the timeperiod.
# Here, 10 minutes only needs a window length of 1.
self.assertEquals(1, ma_one_day_minutes.window_length)
# With minutes greater than the 390, i.e. one trading day, we should
# have a window_length of two days.
ma_two_day_minutes = ta.MA(timeperiod=490, bars='minute')
self.assertEquals(2, ma_two_day_minutes.window_length)
# TODO: Ensure that the lookback into the datapanel is returning
# expected results.
# Requires supplying minute instead of day data to the unit test.
# When adding test data, should add more minute events than the
# timeperiod to ensure that lookback is behaving properly.
tests/test_transforms_talib.py
+146
View File
@@ -0,0 +1,146 @@
#
# Copyright 2013 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 pytz
import numpy as np
import pandas as pd
from datetime import timedelta, datetime
from unittest import TestCase, skip
from zipline.utils.test_utils import setup_logger
import zipline.utils.factory as factory
from zipline.test_algorithms import TALIBAlgorithm
import talib
import zipline.transforms.ta as ta
class TestTALIB(TestCase):
def setUp(self):
setup_logger(self)
sim_params = factory.create_simulation_parameters(
start=datetime(1990, 1, 1, tzinfo=pytz.utc),
end=datetime(1990, 3, 30, tzinfo=pytz.utc))
self.source, self.panel = \
factory.create_test_panel_ohlc_source(sim_params)
@skip
def test_talib_with_default_params(self):
BLACKLIST = ['make_transform', 'BatchTransform',
# TODO: Figure out why MAVP generates a KeyError
'MAVP']
names = [name for name in dir(ta) if name[0].isupper()
and name not in BLACKLIST]
for name in names:
print(name)
zipline_transform = getattr(ta, name)(sid=0)
talib_fn = getattr(talib.abstract, name)
start = datetime(1990, 1, 1, tzinfo=pytz.utc)
end = start + timedelta(days=zipline_transform.lookback + 10)
sim_params = factory.create_simulation_parameters(
start=start, end=end)
source, panel = \
factory.create_test_panel_ohlc_source(sim_params)
algo = TALIBAlgorithm(talib=zipline_transform)
algo.run(source)
zipline_result = np.array(
algo.talib_results[zipline_transform][-1])
talib_data = dict()
data = zipline_transform.window
# TODO: Figure out if we are clobbering the tests by this
# protection against empty windows
if not data:
continue
for key in ['open', 'high', 'low', 'volume']:
if key in data:
talib_data[key] = data[key][0].values
talib_data['close'] = data['price'][0].values
expected_result = talib_fn(talib_data)
if isinstance(expected_result, list):
expected_result = np.array([e[-1] for e in expected_result])
else:
expected_result = np.array(expected_result[-1])
if not (np.all(np.isnan(zipline_result))
and np.all(np.isnan(expected_result))):
self.assertTrue(np.allclose(zipline_result, expected_result))
else:
print('--- NAN')
# reset generator so next iteration has data
# self.source, self.panel = \
# factory.create_test_panel_ohlc_source(self.sim_params)
def test_multiple_talib_with_args(self):
zipline_transforms = [ta.MA(timeperiod=10),
ta.MA(timeperiod=25)]
talib_fn = talib.abstract.MA
algo = TALIBAlgorithm(talib=zipline_transforms)
algo.run(self.source)
# Test if computed values match those computed by pandas rolling mean.
sid = 0
talib_values = np.array([x[sid] for x in
algo.talib_results[zipline_transforms[0]]])
np.testing.assert_array_equal(talib_values,
pd.rolling_mean(self.panel[0]['price'],
10).values)
talib_values = np.array([x[sid] for x in
algo.talib_results[zipline_transforms[1]]])
np.testing.assert_array_equal(talib_values,
pd.rolling_mean(self.panel[0]['price'],
25).values)
for t in zipline_transforms:
talib_result = np.array(algo.talib_results[t][-1])
talib_data = dict()
data = t.window
# TODO: Figure out if we are clobbering the tests by this
# protection against empty windows
if not data:
continue
for key in ['open', 'high', 'low', 'volume']:
if key in data:
talib_data[key] = data[key][0].values
talib_data['close'] = data['price'][0].values
expected_result = talib_fn(talib_data, **t.call_kwargs)[-1]
np.testing.assert_allclose(talib_result, expected_result)
def test_talib_with_minute_data(self):
ma_one_day_minutes = ta.MA(timeperiod=10, bars='minute')
# Assert that the BatchTransform window length is enough to cover
# the amount of minutes in the timeperiod.
# Here, 10 minutes only needs a window length of 1.
self.assertEquals(1, ma_one_day_minutes.window_length)
# With minutes greater than the 390, i.e. one trading day, we should
# have a window_length of two days.
ma_two_day_minutes = ta.MA(timeperiod=490, bars='minute')
self.assertEquals(2, ma_two_day_minutes.window_length)
# TODO: Ensure that the lookback into the datapanel is returning
# expected results.
# Requires supplying minute instead of day data to the unit test.
# When adding test data, should add more minute events than the
# timeperiod to ensure that lookback is behaving properly.