mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 15:04:32 +08:00
Jean Bredeche
fbd3774278
BarData now takes the trading calendar as a parameter. can_trade now checks if the asset’s exchange is open at the current or next market minute (defined by the given trading calendar).
450 lines
16 KiB
Python
450 lines
16 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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"""
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Tests for the zipline.finance package
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"""
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from datetime import datetime, timedelta
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import os
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from nose.tools import timed
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import numpy as np
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import pandas as pd
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import pytz
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from six import iteritems
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from six.moves import range
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from testfixtures import TempDirectory
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from zipline.assets.synthetic import make_simple_equity_info
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from zipline.finance.blotter import Blotter
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from zipline.finance.execution import MarketOrder, LimitOrder
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from zipline.finance.performance import PerformanceTracker
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from zipline.finance.trading import SimulationParameters
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from zipline.data.us_equity_pricing import BcolzDailyBarReader
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from zipline.data.minute_bars import BcolzMinuteBarReader
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from zipline.data.data_portal import DataPortal
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from zipline.data.us_equity_pricing import BcolzDailyBarWriter
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from zipline.finance.slippage import FixedSlippage
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from zipline.protocol import BarData
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from zipline.testing import (
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tmp_trading_env,
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write_bcolz_minute_data,
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)
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from zipline.testing.fixtures import (
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WithLogger,
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WithTradingEnvironment,
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ZiplineTestCase,
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)
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import zipline.utils.factory as factory
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DEFAULT_TIMEOUT = 15 # seconds
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EXTENDED_TIMEOUT = 90
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_multiprocess_can_split_ = False
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class FinanceTestCase(WithLogger,
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WithTradingEnvironment,
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ZiplineTestCase):
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ASSET_FINDER_EQUITY_SIDS = 1, 2, 133
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start = START_DATE = pd.Timestamp('2006-01-01', tz='utc')
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end = END_DATE = pd.Timestamp('2006-12-31', tz='utc')
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def init_instance_fixtures(self):
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super(FinanceTestCase, self).init_instance_fixtures()
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self.zipline_test_config = {'sid': 133}
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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_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 two orders for 100 shares each, and the volume
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# of each trade is 100, and by default you can take up 2.5% of the
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# bar's volume, the simulator should spread the order into 100
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# trades of 2 shares per order.
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'expected_txn_count': 100,
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'expected_txn_volume': 2 * 100,
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'default_slippage': True
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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_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': 100,
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'expected_txn_volume': 2 * -100,
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'default_slippage': True
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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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# but are represented by multiple transactions.
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params1 = {
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'trade_count': 6,
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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': 24,
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'expected_txn_volume': 24
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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_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': 24,
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'expected_txn_volume': -24
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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_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': 24,
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'expected_txn_volume': 24
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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_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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"""
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trade_count = params['trade_count']
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trade_interval = params['trade_interval']
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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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metadata = make_simple_equity_info([sid], self.start, self.end)
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with TempDirectory() as tempdir, \
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tmp_trading_env(equities=metadata) as env:
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if trade_interval < timedelta(days=1):
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sim_params = factory.create_simulation_parameters(
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start=self.start,
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end=self.end,
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data_frequency="minute"
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)
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minutes = self.trading_calendar.minutes_window(
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sim_params.first_open,
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int((trade_interval.total_seconds() / 60) * trade_count)
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+ 100)
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price_data = np.array([10.1] * len(minutes))
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assets = {
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sid: pd.DataFrame({
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"open": price_data,
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"high": price_data,
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"low": price_data,
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"close": price_data,
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"volume": np.array([100] * len(minutes)),
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"dt": minutes
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}).set_index("dt")
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}
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write_bcolz_minute_data(
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self.trading_calendar,
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self.trading_calendar.sessions_in_range(
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self.trading_calendar.minute_to_session_label(
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minutes[0]
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),
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self.trading_calendar.minute_to_session_label(
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minutes[-1]
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)
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),
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tempdir.path,
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iteritems(assets),
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)
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equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
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data_portal = DataPortal(
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env.asset_finder, self.trading_calendar,
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first_trading_day=equity_minute_reader.first_trading_day,
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equity_minute_reader=equity_minute_reader,
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)
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else:
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sim_params = factory.create_simulation_parameters(
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data_frequency="daily"
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)
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days = sim_params.sessions
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assets = {
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1: pd.DataFrame({
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"open": [10.1] * len(days),
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"high": [10.1] * len(days),
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"low": [10.1] * len(days),
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"close": [10.1] * len(days),
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"volume": [100] * len(days),
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"day": [day.value for day in days]
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}, index=days)
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}
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path = os.path.join(tempdir.path, "testdata.bcolz")
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BcolzDailyBarWriter(path, self.trading_calendar, days[0],
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days[-1]).write(
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assets.items()
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)
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equity_daily_reader = BcolzDailyBarReader(path)
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data_portal = DataPortal(
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env.asset_finder, self.trading_calendar,
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first_trading_day=equity_daily_reader.first_trading_day,
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equity_daily_reader=equity_daily_reader,
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)
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if "default_slippage" not in params or \
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not params["default_slippage"]:
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slippage_func = FixedSlippage()
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else:
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slippage_func = None
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blotter = Blotter(sim_params.data_frequency, self.env.asset_finder,
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slippage_func)
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start_date = sim_params.first_open
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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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tracker = PerformanceTracker(sim_params, self.trading_calendar,
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self.env)
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# replicate what tradesim does by going through every minute or day
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# of the simulation and processing open orders each time
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if sim_params.data_frequency == "minute":
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ticks = minutes
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else:
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ticks = days
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transactions = []
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order_list = []
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order_date = start_date
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for tick in ticks:
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blotter.current_dt = tick
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if tick >= order_date and len(order_list) < order_count:
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# place an order
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direction = alternator ** len(order_list)
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order_id = blotter.order(
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blotter.asset_finder.retrieve_asset(sid),
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order_amount * direction,
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MarketOrder())
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order_list.append(blotter.orders[order_id])
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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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else:
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bar_data = BarData(
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data_portal,
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lambda: tick,
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sim_params.data_frequency,
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self.trading_calendar
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)
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txns, _, closed_orders = blotter.get_transactions(bar_data)
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for txn in txns:
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tracker.process_transaction(txn)
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transactions.append(txn)
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blotter.prune_orders(closed_orders)
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for i in range(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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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 range(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.position_tracker.positions[sid]
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if total_volume == 0:
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self.assertIsNone(cumulative_pos)
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else:
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self.assertEqual(total_volume, cumulative_pos.amount)
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# the open orders should not contain sid.
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oo = blotter.open_orders
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self.assertNotIn(sid, oo, "Entry is removed when no open orders")
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def test_blotter_processes_splits(self):
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blotter = Blotter('daily', self.env.asset_finder,
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slippage_func=FixedSlippage())
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# set up two open limit orders with very low limit prices,
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# one for sid 1 and one for sid 2
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blotter.order(
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blotter.asset_finder.retrieve_asset(1), 100, LimitOrder(10))
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blotter.order(
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blotter.asset_finder.retrieve_asset(2), 100, LimitOrder(10))
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# send in a split for sid 2
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blotter.process_splits([(2, 0.3333)])
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for sid in [1, 2]:
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order_lists = blotter.open_orders[sid]
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self.assertIsNotNone(order_lists)
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self.assertEqual(1, len(order_lists))
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aapl_order = blotter.open_orders[1][0].to_dict()
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fls_order = blotter.open_orders[2][0].to_dict()
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# make sure the aapl order didn't change
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self.assertEqual(100, aapl_order['amount'])
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self.assertEqual(10, aapl_order['limit'])
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self.assertEqual(1, aapl_order['sid'])
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# make sure the fls order did change
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# to 300 shares at 3.33
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self.assertEqual(300, fls_order['amount'])
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self.assertEqual(3.33, fls_order['limit'])
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self.assertEqual(2, fls_order['sid'])
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class TradingEnvironmentTestCase(WithLogger,
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WithTradingEnvironment,
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ZiplineTestCase):
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"""
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Tests for date management utilities in zipline.finance.trading.
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"""
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def test_simulation_parameters(self):
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sp = SimulationParameters(
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start_session=pd.Timestamp("2008-01-01", tz='UTC'),
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end_session=pd.Timestamp("2008-12-31", tz='UTC'),
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capital_base=100000,
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trading_calendar=self.trading_calendar,
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)
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self.assertTrue(sp.last_close.month == 12)
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self.assertTrue(sp.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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params = SimulationParameters(
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start_session=pd.Timestamp("2007-12-31", tz='UTC'),
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end_session=pd.Timestamp("2008-01-07", tz='UTC'),
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capital_base=100000,
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trading_calendar=self.trading_calendar,
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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(
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num_expected_trading_days,
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len(params.sessions)
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)
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np.testing.assert_array_equal(expected_trading_days,
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params.sessions.tolist())
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