mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-01 16:52:26 +08:00
Eddie Hebert
64b2a7377c
The data zipline_transform.window is always evaluating to empty, thus the actual checks are not being used because of the `if not data` done before running the `asserts`. This behavior should be fixed, and we should either remove the `not data` check, or bubble up that the check is being hit too many times; but in the meantime, disabling this test which takes a non-trivial amount of time to run. When run as an algorithm, outside of unit tests the talib wrapper does work, so the cause of the window always being empty may be to due to the machinery of the unit test.
402 lines
14 KiB
Python
402 lines
14 KiB
Python
#
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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 pytz
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import numpy as np
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import pandas as pd
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from datetime import timedelta, datetime
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from unittest import TestCase, skip
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from six.moves import range
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from zipline.utils.test_utils import setup_logger
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from zipline.protocol import Event
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from zipline.sources import SpecificEquityTrades
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from zipline.transforms.utils import StatefulTransform, EventWindow
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from zipline.transforms import MovingVWAP
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from zipline.transforms import MovingAverage
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from zipline.transforms import MovingStandardDev
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from zipline.transforms import Returns
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import zipline.utils.factory as factory
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from zipline.test_algorithms import TALIBAlgorithm
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def to_dt(msg):
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return Event({'dt': msg})
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class NoopEventWindow(EventWindow):
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"""
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A no-op EventWindow subclass for testing the base EventWindow logic.
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Keeps lists of all added and dropped events.
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"""
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def __init__(self, market_aware, days, delta):
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EventWindow.__init__(self, market_aware, days, delta)
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self.added = []
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self.removed = []
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def handle_add(self, event):
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self.added.append(event)
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def handle_remove(self, event):
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self.removed.append(event)
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class TestEventWindow(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters()
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setup_logger(self)
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self.monday = datetime(2012, 7, 9, 16, tzinfo=pytz.utc)
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self.eleven_normal_days = [self.monday + i * timedelta(days=1)
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for i in range(11)]
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# Modify the end of the period slightly to exercise the
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# incomplete day logic.
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self.eleven_normal_days[-1] -= timedelta(minutes=1)
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self.eleven_normal_days.append(self.monday +
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timedelta(days=11, seconds=1))
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# Second set of dates to test holiday handling.
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self.jul4_monday = datetime(2012, 7, 2, 16, tzinfo=pytz.utc)
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self.week_of_jul4 = [self.jul4_monday + i * timedelta(days=1)
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for i in range(5)]
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def test_market_aware_window_normal_week(self):
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window = NoopEventWindow(
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market_aware=True,
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delta=None,
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days=3
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)
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events = [to_dt(date) for date in self.eleven_normal_days]
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lengths = []
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# Run the events.
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for event in events:
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window.update(event)
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# Record the length of the window after each event.
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lengths.append(len(window.ticks))
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# The window stretches out during the weekend because we wait
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# to drop events until the weekend ends. The last window is
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# briefly longer because it doesn't complete a full day. The
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# window then shrinks once the day completes
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self.assertEquals(lengths, [1, 2, 3, 3, 3, 4, 5, 5, 5, 3, 4, 3])
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self.assertEquals(window.added, events)
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self.assertEquals(window.removed, events[:-3])
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def test_market_aware_window_holiday(self):
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window = NoopEventWindow(
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market_aware=True,
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delta=None,
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days=2
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)
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events = [to_dt(date) for date in self.week_of_jul4]
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lengths = []
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# Run the events.
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for event in events:
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window.update(event)
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# Record the length of the window after each event.
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lengths.append(len(window.ticks))
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self.assertEquals(lengths, [1, 2, 3, 3, 2])
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self.assertEquals(window.added, events)
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self.assertEquals(window.removed, events[:-2])
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def tearDown(self):
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setup_logger(self)
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class TestFinanceTransforms(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters()
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setup_logger(self)
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trade_history = factory.create_trade_history(
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133,
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[10.0, 10.0, 11.0, 11.0],
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[100, 100, 100, 300],
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timedelta(days=1),
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self.sim_params
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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def tearDown(self):
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self.log_handler.pop_application()
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def test_vwap(self):
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vwap = MovingVWAP(
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market_aware=True,
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window_length=2
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)
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transformed = list(vwap.transform(self.source))
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# Output values
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tnfm_vals = [message[vwap.get_hash()] for message in transformed]
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# "Hand calculated" values.
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expected = [
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(10.0 * 100) / 100.0,
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((10.0 * 100) + (10.0 * 100)) / (200.0),
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# We should drop the first event here.
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((10.0 * 100) + (11.0 * 100)) / (200.0),
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# We should drop the second event here.
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((11.0 * 100) + (11.0 * 300)) / (400.0)
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]
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# Output should match the expected.
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self.assertEquals(tnfm_vals, expected)
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def test_returns(self):
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# Daily returns.
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returns = Returns(1)
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transformed = list(returns.transform(self.source))
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tnfm_vals = [message[returns.get_hash()] for message in transformed]
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# No returns for the first event because we don't have a
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# previous close.
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expected = [0.0, 0.0, 0.1, 0.0]
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self.assertEquals(tnfm_vals, expected)
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# Two-day returns. An extra kink here is that the
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# factory will automatically skip a weekend for the
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# last event. Results shouldn't notice this blip.
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trade_history = factory.create_trade_history(
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133,
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[10.0, 15.0, 13.0, 12.0, 13.0],
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[100, 100, 100, 300, 100],
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timedelta(days=1),
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self.sim_params
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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returns = StatefulTransform(Returns, 2)
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transformed = list(returns.transform(self.source))
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tnfm_vals = [message[returns.get_hash()] for message in transformed]
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expected = [
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0.0,
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0.0,
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(13.0 - 10.0) / 10.0,
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(12.0 - 15.0) / 15.0,
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(13.0 - 13.0) / 13.0
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]
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self.assertEquals(tnfm_vals, expected)
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def test_moving_average(self):
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mavg = MovingAverage(
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market_aware=True,
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fields=['price', 'volume'],
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window_length=2
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)
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transformed = list(mavg.transform(self.source))
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# Output values.
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tnfm_prices = [message[mavg.get_hash()].price
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for message in transformed]
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tnfm_volumes = [message[mavg.get_hash()].volume
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for message in transformed]
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# "Hand-calculated" values
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expected_prices = [
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((10.0) / 1.0),
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((10.0 + 10.0) / 2.0),
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# First event should get dropped here.
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((10.0 + 11.0) / 2.0),
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# Second event should get dropped here.
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((11.0 + 11.0) / 2.0)
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]
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expected_volumes = [
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((100.0) / 1.0),
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((100.0 + 100.0) / 2.0),
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# First event should get dropped here.
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((100.0 + 100.0) / 2.0),
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# Second event should get dropped here.
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((100.0 + 300.0) / 2.0)
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]
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self.assertEquals(tnfm_prices, expected_prices)
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self.assertEquals(tnfm_volumes, expected_volumes)
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def test_moving_stddev(self):
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trade_history = factory.create_trade_history(
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133,
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[10.0, 15.0, 13.0, 12.0],
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[100, 100, 100, 100],
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timedelta(days=1),
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self.sim_params
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)
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stddev = MovingStandardDev(
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market_aware=True,
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window_length=3,
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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transformed = list(stddev.transform(self.source))
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vals = [message[stddev.get_hash()] for message in transformed]
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expected = [
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None,
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np.std([10.0, 15.0], ddof=1),
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np.std([10.0, 15.0, 13.0], ddof=1),
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np.std([15.0, 13.0, 12.0], ddof=1),
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]
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# np has odd rounding behavior, cf.
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# http://docs.scipy.org/doc/np/reference/generated/np.std.html
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for v1, v2 in zip(vals, expected):
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if v1 is None:
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self.assertIsNone(v2)
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continue
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self.assertEquals(round(v1, 5), round(v2, 5))
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############################################################
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# Test TALIB
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import talib
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import zipline.transforms.ta as ta
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class TestTALIB(TestCase):
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def setUp(self):
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setup_logger(self)
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sim_params = factory.create_simulation_parameters(
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start=datetime(1990, 1, 1, tzinfo=pytz.utc),
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end=datetime(1990, 3, 30, tzinfo=pytz.utc))
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self.source, self.panel = \
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factory.create_test_panel_ohlc_source(sim_params)
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@skip
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def test_talib_with_default_params(self):
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BLACKLIST = ['make_transform', 'BatchTransform',
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# TODO: Figure out why MAVP generates a KeyError
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'MAVP']
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names = [n for n in dir(ta) if n[0].isupper()
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and n not in BLACKLIST]
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for name in names:
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print(name)
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zipline_transform = getattr(ta, name)(sid=0)
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talib_fn = getattr(talib.abstract, name)
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start = datetime(1990, 1, 1, tzinfo=pytz.utc)
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end = start + timedelta(days=zipline_transform.lookback + 10)
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sim_params = factory.create_simulation_parameters(
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start=start, end=end)
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source, panel = \
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factory.create_test_panel_ohlc_source(sim_params)
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algo = TALIBAlgorithm(talib=zipline_transform)
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algo.run(source)
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zipline_result = np.array(
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algo.talib_results[zipline_transform][-1])
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talib_data = dict()
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data = zipline_transform.window
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# TODO: Figure out if we are clobbering the tests by this
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# protection against empty windows
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if not data:
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continue
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for key in ['open', 'high', 'low', 'volume']:
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if key in data:
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talib_data[key] = data[key][0].values
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talib_data['close'] = data['price'][0].values
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expected_result = talib_fn(talib_data)
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if isinstance(expected_result, list):
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expected_result = np.array([e[-1] for e in expected_result])
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else:
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expected_result = np.array(expected_result[-1])
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if not (np.all(np.isnan(zipline_result))
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and np.all(np.isnan(expected_result))):
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self.assertTrue(np.allclose(zipline_result, expected_result))
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else:
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print('--- NAN')
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# reset generator so next iteration has data
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# self.source, self.panel = \
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# factory.create_test_panel_ohlc_source(self.sim_params)
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def test_multiple_talib_with_args(self):
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zipline_transforms = [ta.MA(timeperiod=10),
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ta.MA(timeperiod=25)]
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talib_fn = talib.abstract.MA
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algo = TALIBAlgorithm(talib=zipline_transforms)
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algo.run(self.source)
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# Test if computed values match those computed by pandas rolling mean.
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sid = 0
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talib_values = np.array([x[sid] for x in
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algo.talib_results[zipline_transforms[0]]])
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np.testing.assert_array_equal(talib_values,
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pd.rolling_mean(self.panel[0]['price'],
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10).values)
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talib_values = np.array([x[sid] for x in
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algo.talib_results[zipline_transforms[1]]])
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np.testing.assert_array_equal(talib_values,
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pd.rolling_mean(self.panel[0]['price'],
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25).values)
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for t in zipline_transforms:
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talib_result = np.array(algo.talib_results[t][-1])
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talib_data = dict()
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data = t.window
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# TODO: Figure out if we are clobbering the tests by this
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# protection against empty windows
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if not data:
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continue
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for key in ['open', 'high', 'low', 'volume']:
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if key in data:
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talib_data[key] = data[key][0].values
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talib_data['close'] = data['price'][0].values
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expected_result = talib_fn(talib_data, **t.call_kwargs)[-1]
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np.testing.assert_allclose(talib_result, expected_result)
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def test_talib_with_minute_data(self):
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ma_one_day_minutes = ta.MA(timeperiod=10, bars='minute')
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# Assert that the BatchTransform window length is enough to cover
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# the amount of minutes in the timeperiod.
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# Here, 10 minutes only needs a window length of 1.
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self.assertEquals(1, ma_one_day_minutes.window_length)
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# With minutes greater than the 390, i.e. one trading day, we should
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# have a window_length of two days.
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ma_two_day_minutes = ta.MA(timeperiod=490, bars='minute')
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self.assertEquals(2, ma_two_day_minutes.window_length)
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# TODO: Ensure that the lookback into the datapanel is returning
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# expected results.
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# Requires supplying minute instead of day data to the unit test.
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# When adding test data, should add more minute events than the
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# timeperiod to ensure that lookback is behaving properly.
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