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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
126 lines
3.6 KiB
Python
126 lines
3.6 KiB
Python
#!/usr/bin/python
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#
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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 unittest import TestCase
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from nose.tools import timed
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from datetime import datetime
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import pytz
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from zipline.algorithm import TradingAlgorithm
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from zipline.finance import slippage
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from zipline.utils import factory
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from zipline.utils.test_utils import (
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setup_logger,
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teardown_logger
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)
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DEFAULT_TIMEOUT = 15 # seconds
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EXTENDED_TIMEOUT = 90
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class RecordDateSlippage(slippage.FixedSlippage):
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def __init__(self, spread):
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super(RecordDateSlippage, self).__init__(spread=spread)
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self.latest_date = None
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def simulate(self, event, open_orders):
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self.latest_date = event['datetime']
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result = super(RecordDateSlippage, self).simulate(event, open_orders)
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return result
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class TestAlgo(TradingAlgorithm):
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def __init__(self, asserter, *args, **kwargs):
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super(TestAlgo, self).__init__(*args, **kwargs)
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self.asserter = asserter
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def initialize(self, window_length=100):
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self.latest_date = None
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self.set_slippage(RecordDateSlippage(spread=0.05))
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self.stocks = [8229]
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self.ordered = False
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def handle_data(self, data):
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self.latest_date = self.get_datetime()
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if not self.ordered:
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for stock in self.stocks:
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self.order(stock, 100)
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self.ordered = True
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self.asserter.assertGreaterEqual(
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self.latest_date,
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self.slippage.latest_date
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)
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class AlgorithmGeneratorTestCase(TestCase):
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def setUp(self):
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setup_logger(self)
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def tearDown(self):
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teardown_logger(self)
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@timed(DEFAULT_TIMEOUT)
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def test_generator_dates(self):
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"""
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Ensure the pipeline of generators are in sync, at least as far as
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their current dates.
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"""
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sim_params = factory.create_simulation_parameters(
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start=datetime(2011, 7, 30, tzinfo=pytz.utc),
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end=datetime(2012, 7, 30, tzinfo=pytz.utc)
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)
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algo = TestAlgo(self, sim_params=sim_params)
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trade_source = factory.create_daily_trade_source(
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[8229],
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200,
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sim_params
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)
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algo.set_sources([trade_source])
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gen = algo.get_generator()
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self.assertTrue(list(gen))
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self.assertTrue(algo.slippage.latest_date)
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self.assertTrue(algo.latest_date)
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@timed(DEFAULT_TIMEOUT)
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def test_progress(self):
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"""
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Ensure the pipeline of generators are in sync, at least as far as
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their current dates.
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"""
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sim_params = factory.create_simulation_parameters(
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start=datetime(2008, 1, 1, tzinfo=pytz.utc),
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end=datetime(2008, 1, 5, tzinfo=pytz.utc)
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)
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algo = TestAlgo(self, sim_params=sim_params)
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trade_source = factory.create_daily_trade_source(
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[8229],
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3,
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sim_params
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)
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algo.set_sources([trade_source])
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gen = algo.get_generator()
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results = list(gen)
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self.assertEqual(results[-2]['progress'], 1.0)
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