mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 03:02:36 +08:00
Eddie Hebert
39f44a44f8
Though the addition of tracking mulitple values in the window is powerful, the changes broke behavior of existing algorithms by changing method signatures and names. So temporarily reverting these changes, to be pulled back in when a way to have the multiple fields tracked with the existing API is written, or a cutover of the API is figured out and determined.
387 lines
12 KiB
Python
387 lines
12 KiB
Python
#
|
|
# Copyright 2012 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.
|
|
|
|
from collections import deque
|
|
|
|
import pytz
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from datetime import timedelta, datetime
|
|
from unittest import TestCase
|
|
|
|
from zipline import ndict
|
|
|
|
from zipline.utils.test_utils import setup_logger
|
|
from zipline.utils.date_utils import utcnow
|
|
|
|
from zipline.sources import SpecificEquityTrades
|
|
from zipline.transforms.utils import StatefulTransform, EventWindow
|
|
from zipline.transforms import MovingVWAP
|
|
from zipline.transforms import MovingAverage
|
|
from zipline.transforms import MovingStandardDev
|
|
from zipline.transforms import Returns
|
|
import zipline.utils.factory as factory
|
|
|
|
from zipline.test_algorithms import BatchTransformAlgorithm
|
|
|
|
|
|
def to_dt(msg):
|
|
return ndict({'dt': msg})
|
|
|
|
|
|
class NoopEventWindow(EventWindow):
|
|
"""
|
|
A no-op EventWindow subclass for testing the base EventWindow logic.
|
|
Keeps lists of all added and dropped events.
|
|
"""
|
|
def __init__(self, market_aware, days, delta):
|
|
EventWindow.__init__(self, market_aware, days, delta)
|
|
|
|
self.added = []
|
|
self.removed = []
|
|
|
|
def handle_add(self, event):
|
|
self.added.append(event)
|
|
|
|
def handle_remove(self, event):
|
|
self.removed.append(event)
|
|
|
|
|
|
class TestEventWindow(TestCase):
|
|
def setUp(self):
|
|
setup_logger(self)
|
|
|
|
self.monday = datetime(2012, 7, 9, 16, tzinfo=pytz.utc)
|
|
self.eleven_normal_days = [self.monday + i * timedelta(days=1)
|
|
for i in xrange(11)]
|
|
|
|
# Modify the end of the period slightly to exercise the
|
|
# incomplete day logic.
|
|
self.eleven_normal_days[-1] -= timedelta(minutes=1)
|
|
self.eleven_normal_days.append(self.monday +
|
|
timedelta(days=11, seconds=1))
|
|
|
|
# Second set of dates to test holiday handling.
|
|
self.jul4_monday = datetime(2012, 7, 2, 16, tzinfo=pytz.utc)
|
|
self.week_of_jul4 = [self.jul4_monday + i * timedelta(days=1)
|
|
for i in xrange(5)]
|
|
|
|
def test_event_window_with_timedelta(self):
|
|
|
|
# Keep all events within a 5 minute window.
|
|
window = NoopEventWindow(
|
|
market_aware=False,
|
|
delta=timedelta(minutes=5),
|
|
days=None
|
|
)
|
|
|
|
now = utcnow()
|
|
|
|
# 15 dates, increasing in 1 minute increments.
|
|
dates = [now + i * timedelta(minutes=1)
|
|
for i in xrange(15)]
|
|
|
|
# Turn the dates into the format required by EventWindow.
|
|
dt_messages = [to_dt(date) for date in dates]
|
|
|
|
# Run all messages through the window and assert that we're adding
|
|
# and removing messages appropriately. We start the enumeration at 1
|
|
# for convenience.
|
|
for num, message in enumerate(dt_messages, 1):
|
|
window.update(message)
|
|
|
|
# Assert that we've added the correct number of events.
|
|
assert len(window.added) == num
|
|
|
|
# Assert that we removed only events that fall outside (or
|
|
# on the boundary of) the delta.
|
|
for dropped in window.removed:
|
|
assert message.dt - dropped.dt >= timedelta(minutes=5)
|
|
|
|
def test_market_aware_window_normal_week(self):
|
|
window = NoopEventWindow(
|
|
market_aware=True,
|
|
delta=None,
|
|
days=3
|
|
)
|
|
events = [to_dt(date) for date in self.eleven_normal_days]
|
|
lengths = []
|
|
# Run the events.
|
|
for event in events:
|
|
window.update(event)
|
|
# Record the length of the window after each event.
|
|
lengths.append(len(window.ticks))
|
|
|
|
# The window stretches out during the weekend because we wait
|
|
# to drop events until the weekend ends. The last window is
|
|
# briefly longer because it doesn't complete a full day. The
|
|
# window then shrinks once the day completes
|
|
assert lengths == [1, 2, 3, 3, 3, 4, 5, 5, 5, 3, 4, 3]
|
|
assert window.added == events
|
|
assert window.removed == events[:-3]
|
|
|
|
def test_market_aware_window_holiday(self):
|
|
window = NoopEventWindow(
|
|
market_aware=True,
|
|
delta=None,
|
|
days=2
|
|
)
|
|
events = [to_dt(date) for date in self.week_of_jul4]
|
|
lengths = []
|
|
|
|
# Run the events.
|
|
for event in events:
|
|
window.update(event)
|
|
# Record the length of the window after each event.
|
|
lengths.append(len(window.ticks))
|
|
|
|
assert lengths == [1, 2, 3, 3, 2]
|
|
assert window.added == events
|
|
assert window.removed == events[:-2]
|
|
|
|
def tearDown(self):
|
|
setup_logger(self)
|
|
|
|
|
|
class TestFinanceTransforms(TestCase):
|
|
|
|
def setUp(self):
|
|
self.trading_environment = factory.create_trading_environment()
|
|
setup_logger(self)
|
|
|
|
trade_history = factory.create_trade_history(
|
|
133,
|
|
[10.0, 10.0, 11.0, 11.0],
|
|
[100, 100, 100, 300],
|
|
timedelta(days=1),
|
|
self.trading_environment
|
|
)
|
|
self.source = SpecificEquityTrades(event_list=trade_history)
|
|
|
|
def tearDown(self):
|
|
self.log_handler.pop_application()
|
|
|
|
def test_vwap(self):
|
|
|
|
vwap = MovingVWAP(
|
|
market_aware=False,
|
|
delta=timedelta(days=2)
|
|
)
|
|
transformed = list(vwap.transform(self.source))
|
|
|
|
# Output values
|
|
tnfm_vals = [message.tnfm_value for message in transformed]
|
|
# "Hand calculated" values.
|
|
expected = [
|
|
(10.0 * 100) / 100.0,
|
|
((10.0 * 100) + (10.0 * 100)) / (200.0),
|
|
# We should drop the first event here.
|
|
((10.0 * 100) + (11.0 * 100)) / (200.0),
|
|
# We should drop the second event here.
|
|
((11.0 * 100) + (11.0 * 300)) / (400.0)
|
|
]
|
|
|
|
# Output should match the expected.
|
|
assert tnfm_vals == expected
|
|
|
|
def test_returns(self):
|
|
# Daily returns.
|
|
returns = Returns(1)
|
|
|
|
transformed = list(returns.transform(self.source))
|
|
tnfm_vals = [message.tnfm_value for message in transformed]
|
|
|
|
# No returns for the first event because we don't have a
|
|
# previous close.
|
|
expected = [0.0, 0.0, 0.1, 0.0]
|
|
|
|
assert tnfm_vals == expected
|
|
|
|
# Two-day returns. An extra kink here is that the
|
|
# factory will automatically skip a weekend for the
|
|
# last event. Results shouldn't notice this blip.
|
|
|
|
trade_history = factory.create_trade_history(
|
|
133,
|
|
[10.0, 15.0, 13.0, 12.0, 13.0],
|
|
[100, 100, 100, 300, 100],
|
|
timedelta(days=1),
|
|
self.trading_environment
|
|
)
|
|
self.source = SpecificEquityTrades(event_list=trade_history)
|
|
|
|
returns = StatefulTransform(Returns, 2)
|
|
|
|
transformed = list(returns.transform(self.source))
|
|
tnfm_vals = [message.tnfm_value for message in transformed]
|
|
|
|
expected = [
|
|
0.0,
|
|
0.0,
|
|
(13.0 - 10.0) / 10.0,
|
|
(12.0 - 15.0) / 15.0,
|
|
(13.0 - 13.0) / 13.0
|
|
]
|
|
|
|
assert tnfm_vals == expected
|
|
|
|
def test_moving_average(self):
|
|
|
|
mavg = MovingAverage(
|
|
market_aware=False,
|
|
fields=['price', 'volume'],
|
|
delta=timedelta(days=2),
|
|
)
|
|
|
|
transformed = list(mavg.transform(self.source))
|
|
# Output values.
|
|
tnfm_prices = [message.tnfm_value.price for message in transformed]
|
|
tnfm_volumes = [message.tnfm_value.volume for message in transformed]
|
|
|
|
# "Hand-calculated" values
|
|
expected_prices = [
|
|
((10.0) / 1.0),
|
|
((10.0 + 10.0) / 2.0),
|
|
# First event should get dropped here.
|
|
((10.0 + 11.0) / 2.0),
|
|
# Second event should get dropped here.
|
|
((11.0 + 11.0) / 2.0)
|
|
]
|
|
expected_volumes = [
|
|
((100.0) / 1.0),
|
|
((100.0 + 100.0) / 2.0),
|
|
# First event should get dropped here.
|
|
((100.0 + 100.0) / 2.0),
|
|
# Second event should get dropped here.
|
|
((100.0 + 300.0) / 2.0)
|
|
]
|
|
|
|
assert tnfm_prices == expected_prices
|
|
assert tnfm_volumes == expected_volumes
|
|
|
|
def test_moving_stddev(self):
|
|
trade_history = factory.create_trade_history(
|
|
133,
|
|
[10.0, 15.0, 13.0, 12.0],
|
|
[100, 100, 100, 100],
|
|
timedelta(hours=1),
|
|
self.trading_environment
|
|
)
|
|
|
|
stddev = MovingStandardDev(
|
|
market_aware=False,
|
|
delta=timedelta(minutes=150),
|
|
)
|
|
self.source = SpecificEquityTrades(event_list=trade_history)
|
|
|
|
transformed = list(stddev.transform(self.source))
|
|
|
|
vals = [message.tnfm_value for message in transformed]
|
|
|
|
expected = [
|
|
None,
|
|
np.std([10.0, 15.0], ddof=1),
|
|
np.std([10.0, 15.0, 13.0], ddof=1),
|
|
np.std([15.0, 13.0, 12.0], ddof=1),
|
|
]
|
|
|
|
# np has odd rounding behavior, cf.
|
|
# http://docs.scipy.org/doc/np/reference/generated/np.std.html
|
|
for v1, v2 in zip(vals, expected):
|
|
|
|
if v1 is None:
|
|
assert v2 is None
|
|
continue
|
|
assert round(v1, 5) == round(v2, 5)
|
|
|
|
|
|
############################################################
|
|
# Test BatchTransform
|
|
|
|
class TestBatchTransform(TestCase):
|
|
def setUp(self):
|
|
setup_logger(self)
|
|
self.source, self.df = factory.create_test_df_source()
|
|
|
|
def test_event_window(self):
|
|
algo = BatchTransformAlgorithm()
|
|
algo.run(self.source)
|
|
wl = algo.window_length
|
|
self.assertEqual(algo.history_return_price_class[:wl],
|
|
[None] * wl,
|
|
"First two iterations should return None")
|
|
self.assertEqual(algo.history_return_price_decorator[:wl],
|
|
[None] * wl,
|
|
"First two iterations should return None")
|
|
self.assertTrue(isinstance(
|
|
algo.history_return_price_class[wl + 1],
|
|
pd.DataFrame)
|
|
)
|
|
|
|
# Test whether arbitrary fields can be added to datapanel
|
|
field = algo.history_return_arbitrary_fields[-1]
|
|
self.assertTrue(
|
|
'arbitrary' in field.items,
|
|
'datapanel should contain column arbitrary'
|
|
)
|
|
|
|
self.assertTrue(all(
|
|
field['arbitrary'].values.flatten() ==
|
|
[123] * algo.window_length),
|
|
'arbitrary dataframe should contain only "test"'
|
|
)
|
|
|
|
for data in algo.history_return_sid_filter[wl:]:
|
|
self.assertIn(0, data.columns)
|
|
self.assertNotIn(1, data.columns)
|
|
|
|
for data in algo.history_return_field_filter[wl:]:
|
|
self.assertIn('price', data.items)
|
|
self.assertNotIn('ignore', data.items)
|
|
|
|
for data in algo.history_return_field_no_filter[wl:]:
|
|
self.assertIn('price', data.items)
|
|
self.assertIn('ignore', data.items)
|
|
|
|
for data in algo.history_return_ticks[wl:]:
|
|
self.assertTrue(isinstance(data, deque))
|
|
|
|
for data in algo.history_return_not_full:
|
|
self.assertIsNot(data, None)
|
|
|
|
# test overloaded class
|
|
for test_history in [algo.history_return_price_class,
|
|
algo.history_return_price_decorator]:
|
|
# starting at window length, the window should contain
|
|
# consecutive (of window length) numbers up till the end.
|
|
for i in range(algo.window_length, len(test_history)):
|
|
np.testing.assert_array_equal(
|
|
range(i - algo.window_length + 1, i + 1),
|
|
test_history[i].values.flatten()
|
|
)
|
|
|
|
def test_passing_of_args(self):
|
|
algo = BatchTransformAlgorithm(1, kwarg='str')
|
|
self.assertEqual(algo.args, (1,))
|
|
self.assertEqual(algo.kwargs, {'kwarg': 'str'})
|
|
|
|
algo.run(self.source)
|
|
expected_item = ((1, ), {'kwarg': 'str'})
|
|
self.assertEqual(
|
|
algo.history_return_args,
|
|
[None, None, None, expected_item, expected_item,
|
|
expected_item])
|