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fawce
2c7355a0dc
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.
Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.
================
Details:
- adding delorean to the requirements.
- made index symbol a parameter for loading the benchmark data. changed
messagepack storage to be symbol specific.
- ported risk, performance, algorithm, transforms, batch transforms
and associated tests to use simulation parameters and global environment
- factory and sim factory use global state and sim params
- factory method parameter names now reflect the class expected
382 lines
13 KiB
Python
382 lines
13 KiB
Python
#
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# Copyright 2012 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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from collections import deque
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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
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from zipline import ndict
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from zipline.utils.test_utils import setup_logger
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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 BatchTransformAlgorithm
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def to_dt(msg):
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return ndict({'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 xrange(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 xrange(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 BatchTransform
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class TestBatchTransform(TestCase):
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def setUp(self):
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self.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, 1, 8, tzinfo=pytz.utc)
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)
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setup_logger(self)
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self.source, self.df = \
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factory.create_test_df_source(self.sim_params)
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def test_event_window(self):
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algo = BatchTransformAlgorithm()
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algo.run(self.source)
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wl = algo.window_length
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# The following assertion depend on window length of 3
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self.assertEqual(wl, 3)
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self.assertEqual(algo.history_return_price_class[:wl],
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[None] * wl,
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"First three iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_class,))
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self.assertEqual(algo.history_return_price_decorator[:wl],
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[None] * wl,
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"First three iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_decorator,))
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# After three Nones, the next value should be a data frame
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self.assertTrue(isinstance(
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algo.history_return_price_class[wl],
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pd.DataFrame)
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)
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# Test whether arbitrary fields can be added to datapanel
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field = algo.history_return_arbitrary_fields[-1]
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self.assertTrue(
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'arbitrary' in field.items,
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'datapanel should contain column arbitrary'
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)
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self.assertTrue(all(
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field['arbitrary'].values.flatten() ==
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[123] * algo.window_length),
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'arbitrary dataframe should contain only "test"'
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)
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for data in algo.history_return_sid_filter[wl:]:
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self.assertIn(0, data.columns)
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self.assertNotIn(1, data.columns)
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for data in algo.history_return_field_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertNotIn('ignore', data.items)
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for data in algo.history_return_field_no_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertIn('ignore', data.items)
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for data in algo.history_return_ticks[wl:]:
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self.assertTrue(isinstance(data, deque))
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for data in algo.history_return_not_full:
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self.assertIsNot(data, None)
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# test overloaded class
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for test_history in [algo.history_return_price_class,
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algo.history_return_price_decorator]:
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# starting at window length, the window should contain
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# consecutive (of window length) numbers up till the end.
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for i in range(algo.window_length, len(test_history)):
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np.testing.assert_array_equal(
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range(i - algo.window_length + 1, i + 1),
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test_history[i].values.flatten()
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)
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def test_passing_of_args(self):
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algo = BatchTransformAlgorithm(1, kwarg='str')
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self.assertEqual(algo.args, (1,))
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self.assertEqual(algo.kwargs, {'kwarg': 'str'})
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algo.run(self.source)
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expected_item = ((1, ), {'kwarg': 'str'})
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self.assertEqual(
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algo.history_return_args,
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[
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# 1990-01-01 - market holiday, no event
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# 1990-01-02 - window not full
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None,
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# 1990-01-03 - window not full
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None,
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# 1990-01-04 - window not full, 3rd event
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None,
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# 1990-01-05 - window now full
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expected_item,
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# 1990-01-08 - window now full
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expected_item
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])
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