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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
368 lines
12 KiB
Python
368 lines
12 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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"""
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Tests for the zipline.finance package
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"""
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import pytz
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from unittest import TestCase
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from datetime import datetime, timedelta
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import numpy as np
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from nose.tools import timed
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import zipline.utils.factory as factory
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import zipline.utils.simfactory as simfactory
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import zipline.finance.trading as trading
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from zipline.finance.trading import SimulationParameters
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from zipline.finance.performance import PerformanceTracker
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from zipline.utils.protocol_utils import ndict
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from zipline.finance.trading import TransactionSimulator
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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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assert_single_position
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)
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DEFAULT_TIMEOUT = 15 # seconds
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EXTENDED_TIMEOUT = 90
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class FinanceTestCase(TestCase):
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def setUp(self):
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self.zipline_test_config = {
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'sid': 133,
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}
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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_factory_daily(self):
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sim_params = factory.create_simulation_parameters()
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trade_source = factory.create_daily_trade_source(
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[133],
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200,
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sim_params
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)
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prev = None
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for trade in trade_source:
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if prev:
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self.assertTrue(trade.dt > prev.dt)
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prev = trade
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@timed(DEFAULT_TIMEOUT)
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def test_trading_environment(self):
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#holidays taken from: http://www.nyse.com/press/1191407641943.html
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new_years = datetime(2008, 1, 1, tzinfo=pytz.utc)
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mlk_day = datetime(2008, 1, 21, tzinfo=pytz.utc)
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presidents = datetime(2008, 2, 18, tzinfo=pytz.utc)
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good_friday = datetime(2008, 3, 21, tzinfo=pytz.utc)
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memorial_day = datetime(2008, 5, 26, tzinfo=pytz.utc)
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july_4th = datetime(2008, 7, 4, tzinfo=pytz.utc)
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labor_day = datetime(2008, 9, 1, tzinfo=pytz.utc)
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tgiving = datetime(2008, 11, 27, tzinfo=pytz.utc)
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christmas = datetime(2008, 5, 25, tzinfo=pytz.utc)
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a_saturday = datetime(2008, 8, 2, tzinfo=pytz.utc)
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a_sunday = datetime(2008, 10, 12, tzinfo=pytz.utc)
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holidays = [
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new_years,
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mlk_day,
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presidents,
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good_friday,
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memorial_day,
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july_4th,
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labor_day,
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tgiving,
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christmas,
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a_saturday,
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a_sunday
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]
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for holiday in holidays:
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self.assertTrue(not trading.environment.is_trading_day(holiday))
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first_trading_day = datetime(2008, 1, 2, tzinfo=pytz.utc)
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last_trading_day = datetime(2008, 12, 31, tzinfo=pytz.utc)
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workdays = [first_trading_day, last_trading_day]
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for workday in workdays:
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self.assertTrue(trading.environment.is_trading_day(workday))
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def test_simulation_parameters(self):
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env = SimulationParameters(
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period_start=datetime(2008, 1, 1, tzinfo=pytz.utc),
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period_end=datetime(2008, 12, 31, tzinfo=pytz.utc),
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capital_base=100000,
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)
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self.assertTrue(env.last_close.month == 12)
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self.assertTrue(env.last_close.day == 31)
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@timed(DEFAULT_TIMEOUT)
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def test_sim_params_days_in_period(self):
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# January 2008
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4 5
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# 6 7 8 9 10 11 12
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# 13 14 15 16 17 18 19
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# 20 21 22 23 24 25 26
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# 27 28 29 30 31
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env = SimulationParameters(
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period_start=datetime(2007, 12, 31, tzinfo=pytz.utc),
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period_end=datetime(2008, 1, 7, tzinfo=pytz.utc),
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capital_base=100000,
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)
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expected_trading_days = (
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datetime(2007, 12, 31, tzinfo=pytz.utc),
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# Skip new years
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#holidays taken from: http://www.nyse.com/press/1191407641943.html
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datetime(2008, 1, 2, tzinfo=pytz.utc),
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datetime(2008, 1, 3, tzinfo=pytz.utc),
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datetime(2008, 1, 4, tzinfo=pytz.utc),
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# Skip Saturday
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# Skip Sunday
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datetime(2008, 1, 7, tzinfo=pytz.utc)
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)
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num_expected_trading_days = 5
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self.assertEquals(num_expected_trading_days, env.days_in_period)
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np.testing.assert_array_equal(expected_trading_days,
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env.trading_days.tolist())
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@timed(EXTENDED_TIMEOUT)
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def test_full_zipline(self):
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#provide enough trades to ensure all orders are filled.
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self.zipline_test_config['order_count'] = 100
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self.zipline_test_config['trade_count'] = 200
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zipline = simfactory.create_test_zipline(**self.zipline_test_config)
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assert_single_position(self, zipline)
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# TODO: write tests for short sales
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# TODO: write a test to do massive buying or shorting.
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@timed(DEFAULT_TIMEOUT)
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def test_partially_filled_orders(self):
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# create a scenario where order size and trade size are equal
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# so that orders must be spread out over several trades.
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params = {
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'trade_count': 360,
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'trade_amount': 100,
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'trade_interval': timedelta(minutes=1),
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'order_count': 2,
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'order_amount': 100,
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'order_interval': timedelta(minutes=1),
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# because we placed an order for 100 shares, and the volume
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# of each trade is 100, the simulator should spread the order
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# into 4 trades of 25 shares per order.
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'expected_txn_count': 8,
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'expected_txn_volume': 2 * 100
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}
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self.transaction_sim(**params)
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# same scenario, but with short sales
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params2 = {
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'trade_count': 360,
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'trade_amount': 100,
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'trade_interval': timedelta(minutes=1),
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'order_count': 2,
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'order_amount': -100,
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'order_interval': timedelta(minutes=1),
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'expected_txn_count': 8,
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'expected_txn_volume': 2 * -100
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}
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self.transaction_sim(**params2)
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@timed(DEFAULT_TIMEOUT)
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def test_collapsing_orders(self):
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# create a scenario where order.amount <<< trade.volume
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# to test that several orders can be covered properly by one trade.
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params1 = {
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'trade_count': 6,
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'trade_amount': 100,
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'trade_interval': timedelta(hours=1),
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'order_count': 24,
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'order_amount': 1,
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'order_interval': timedelta(minutes=1),
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# because we placed an orders totaling less than 25% of one trade
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# the simulator should produce just one transaction.
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'expected_txn_count': 1,
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'expected_txn_volume': 24 * 1
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}
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self.transaction_sim(**params1)
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# second verse, same as the first. except short!
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params2 = {
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'trade_count': 6,
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'trade_amount': 100,
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'trade_interval': timedelta(hours=1),
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'order_count': 24,
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'order_amount': -1,
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'order_interval': timedelta(minutes=1),
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'expected_txn_count': 1,
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'expected_txn_volume': 24 * -1
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}
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self.transaction_sim(**params2)
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# Runs the collapsed trades over daily trade intervals.
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# Ensuring that our delay works for daily intervals as well.
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params3 = {
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'trade_count': 6,
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'trade_amount': 100,
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'trade_interval': timedelta(days=1),
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'order_count': 24,
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'order_amount': 1,
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'order_interval': timedelta(minutes=1),
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'expected_txn_count': 1,
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'expected_txn_volume': 24 * 1
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}
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self.transaction_sim(**params3)
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@timed(DEFAULT_TIMEOUT)
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def test_alternating_long_short(self):
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# create a scenario where we alternate buys and sells
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params1 = {
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'trade_count': int(6.5 * 60 * 4),
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'trade_amount': 100,
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'trade_interval': timedelta(minutes=1),
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'order_count': 4,
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'order_amount': 10,
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'order_interval': timedelta(hours=24),
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'alternate': True,
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'complete_fill': True,
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'expected_txn_count': 4,
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'expected_txn_volume': 0 # equal buys and sells
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}
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self.transaction_sim(**params1)
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def transaction_sim(self, **params):
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""" This is a utility method that asserts expected
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results for conversion of orders to transactions given a
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trade history"""
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trade_count = params['trade_count']
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trade_interval = params['trade_interval']
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trade_delay = params.get('trade_delay')
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order_count = params['order_count']
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order_amount = params['order_amount']
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order_interval = params['order_interval']
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expected_txn_count = params['expected_txn_count']
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expected_txn_volume = params['expected_txn_volume']
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# optional parameters
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# ---------------------
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# if present, alternate between long and short sales
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alternate = params.get('alternate')
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# if present, expect transaction amounts to match orders exactly.
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complete_fill = params.get('complete_fill')
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sid = 1
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sim_params = factory.create_simulation_parameters()
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trade_sim = TransactionSimulator()
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price = [10.1] * trade_count
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volume = [100] * trade_count
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start_date = sim_params.first_open
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generated_trades = factory.create_trade_history(
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sid,
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price,
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volume,
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trade_interval,
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sim_params
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)
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if alternate:
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alternator = -1
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else:
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alternator = 1
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order_date = start_date
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for i in xrange(order_count):
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order = ndict({
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'sid': sid,
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'amount': order_amount * alternator ** i,
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'dt': order_date
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})
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trade_sim.place_order(order)
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order_date = order_date + order_interval
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# move after market orders to just after market next
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# market open.
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if order_date.hour >= 21:
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if order_date.minute >= 00:
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order_date = order_date + timedelta(days=1)
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order_date = order_date.replace(hour=14, minute=30)
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# there should now be one open order list stored under the sid
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oo = trade_sim.open_orders
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self.assertEqual(len(oo), 1)
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self.assertTrue(sid in oo)
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order_list = oo[sid]
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self.assertEqual(order_count, len(order_list))
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for i in xrange(order_count):
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order = order_list[i]
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self.assertEqual(order.sid, sid)
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self.assertEqual(order.amount, order_amount * alternator ** i)
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tracker = PerformanceTracker(sim_params)
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# this approximates the loop inside TradingSimulationClient
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transactions = []
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for trade in generated_trades:
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if trade_delay:
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trade.dt = trade.dt + trade_delay
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trade_sim.update(trade)
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if trade.TRANSACTION:
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transactions.append(trade.TRANSACTION)
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tracker.process_event(trade)
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if complete_fill:
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self.assertEqual(len(transactions), len(order_list))
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total_volume = 0
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for i in xrange(len(transactions)):
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txn = transactions[i]
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total_volume += txn.amount
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if complete_fill:
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order = order_list[i]
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self.assertEqual(order.amount, txn.amount)
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self.assertEqual(total_volume, expected_txn_volume)
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self.assertEqual(len(transactions), expected_txn_count)
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cumulative_pos = tracker.cumulative_performance.positions[sid]
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self.assertEqual(total_volume, cumulative_pos.amount)
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# the open orders should now be empty
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oo = trade_sim.open_orders
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self.assertTrue(sid in oo)
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order_list = oo[sid]
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self.assertEqual(0, len(order_list))
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