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zipline/tests/test_finance.py
Scott Sanderson e491ebef53 ENH: Initial support for international pipelines. 
This commit adds initial support for international markets in the Pipeline API
via addition of the concept of "domains".

Significant changes on this branch include:

- Added `zipline.pipeline.domain` and the `Domain` interface.
  - Added several canonical domain definitions:
    - GENERIC
    - US_EQUITIES
    - CA_EQUITIES
    - GB_EQUITIES

- Added `zipline.country` as a place to define canonical country code names.

- Made `PipelineLoader` an interface and added new `domain` parameter to
  signature of `load_adjusted_array`.

- Added a new system for "generic" and "specialized" DataSet and BoundColumn
  objects.

- Added a new system for inferring a domain from a pipeline that contains a mix
  of generic and non-generic terms.

- Reworked the built-in pricing dataset. USEquityPricing dataset is now a
  specialized version of a generic EquityPricing dataset, and most built-in
  factors are now implemented in terms of EquityPricing rather than
  USEquityPricing.

- Removed `zipline.data.us_equity_pricing`.
  - Moved BcolzDailyBar{Reader,Writer} to `zipline.data.bcolz_daily_bars`.
  - Moved SQLiteAdjustment{Reader,Writer} to `zipline.data.adjustments`.

New dependencies added as part of this work:

- iso3166 (for canonical country code definitions)
- python-interface (for strict interface definitions).

See https://github.com/quantopian/zipline/issues/2265 for a full description of
the design for domains.
2018-09-10 10:05:39 -05:00

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#
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Tests for the zipline.finance package
"""
from datetime import datetime, timedelta
import os
from nose.tools import timed
import numpy as np
import pandas as pd
import pytz
from six import iteritems
from six.moves import range
from testfixtures import TempDirectory
from zipline.finance.blotter.simulation_blotter import SimulationBlotter
from zipline.finance.execution import MarketOrder, LimitOrder
from zipline.finance.metrics import MetricsTracker, load as load_metrics_set
from zipline.finance.trading import SimulationParameters
from zipline.data.bcolz_daily_bars import (
BcolzDailyBarReader,
BcolzDailyBarWriter,
)
from zipline.data.minute_bars import BcolzMinuteBarReader
from zipline.data.data_portal import DataPortal
from zipline.finance.slippage import FixedSlippage, FixedBasisPointsSlippage
from zipline.finance.asset_restrictions import NoRestrictions
from zipline.protocol import BarData
from zipline.testing import write_bcolz_minute_data
import zipline.testing.fixtures as zf
import zipline.utils.factory as factory
DEFAULT_TIMEOUT = 15 # seconds
EXTENDED_TIMEOUT = 90
_multiprocess_can_split_ = False
class FinanceTestCase(zf.WithAssetFinder,
zf.WithTradingCalendars,
zf.ZiplineTestCase):
ASSET_FINDER_EQUITY_SIDS = 1, 2, 133
start = START_DATE = pd.Timestamp('2006-01-01', tz='utc')
end = END_DATE = pd.Timestamp('2006-12-31', tz='utc')
def init_instance_fixtures(self):
super(FinanceTestCase, self).init_instance_fixtures()
self.zipline_test_config = {'sid': 133}
# TODO: write tests for short sales
# TODO: write a test to do massive buying or shorting.
@timed(DEFAULT_TIMEOUT)
def test_partially_filled_orders(self):
# create a scenario where order size and trade size are equal
# so that orders must be spread out over several trades.
params = {
'trade_count': 360,
'trade_interval': timedelta(minutes=1),
'order_count': 2,
'order_amount': 100,
'order_interval': timedelta(minutes=1),
# because we placed two orders for 100 shares each, and the volume
# of each trade is 100, and by default you can take up 10% of the
# bar's volume (per FixedBasisPointsSlippage, the default slippage
# model), the simulator should spread the order into 20 trades of
# 10 shares per order.
'expected_txn_count': 20,
'expected_txn_volume': 2 * 100,
'default_slippage': True
}
self.transaction_sim(**params)
# same scenario, but with short sales
params2 = {
'trade_count': 360,
'trade_interval': timedelta(minutes=1),
'order_count': 2,
'order_amount': -100,
'order_interval': timedelta(minutes=1),
'expected_txn_count': 20,
'expected_txn_volume': 2 * -100,
'default_slippage': True
}
self.transaction_sim(**params2)
@timed(DEFAULT_TIMEOUT)
def test_collapsing_orders(self):
# create a scenario where order.amount <<< trade.volume
# to test that several orders can be covered properly by one trade,
# but are represented by multiple transactions.
params1 = {
'trade_count': 6,
'trade_interval': timedelta(hours=1),
'order_count': 24,
'order_amount': 1,
'order_interval': timedelta(minutes=1),
# because we placed an orders totaling less than 25% of one trade
# the simulator should produce just one transaction.
'expected_txn_count': 24,
'expected_txn_volume': 24
}
self.transaction_sim(**params1)
# second verse, same as the first. except short!
params2 = {
'trade_count': 6,
'trade_interval': timedelta(hours=1),
'order_count': 24,
'order_amount': -1,
'order_interval': timedelta(minutes=1),
'expected_txn_count': 24,
'expected_txn_volume': -24
}
self.transaction_sim(**params2)
# Runs the collapsed trades over daily trade intervals.
# Ensuring that our delay works for daily intervals as well.
params3 = {
'trade_count': 6,
'trade_interval': timedelta(days=1),
'order_count': 24,
'order_amount': 1,
'order_interval': timedelta(minutes=1),
'expected_txn_count': 24,
'expected_txn_volume': 24
}
self.transaction_sim(**params3)
@timed(DEFAULT_TIMEOUT)
def test_alternating_long_short(self):
# create a scenario where we alternate buys and sells
params1 = {
'trade_count': int(6.5 * 60 * 4),
'trade_interval': timedelta(minutes=1),
'order_count': 4,
'order_amount': 10,
'order_interval': timedelta(hours=24),
'alternate': True,
'complete_fill': True,
'expected_txn_count': 4,
'expected_txn_volume': 0 # equal buys and sells
}
self.transaction_sim(**params1)
def transaction_sim(self, **params):
"""This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history
"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
asset1 = self.asset_finder.retrieve_asset(1)
with TempDirectory() as tempdir:
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
start=self.start,
end=self.end,
data_frequency="minute"
)
minutes = self.trading_calendar.minutes_window(
sim_params.first_open,
int((trade_interval.total_seconds() / 60) * trade_count)
+ 100)
price_data = np.array([10.1] * len(minutes))
assets = {
asset1.sid: pd.DataFrame({
"open": price_data,
"high": price_data,
"low": price_data,
"close": price_data,
"volume": np.array([100] * len(minutes)),
"dt": minutes
}).set_index("dt")
}
write_bcolz_minute_data(
self.trading_calendar,
self.trading_calendar.sessions_in_range(
self.trading_calendar.minute_to_session_label(
minutes[0]
),
self.trading_calendar.minute_to_session_label(
minutes[-1]
)
),
tempdir.path,
iteritems(assets),
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
self.asset_finder, self.trading_calendar,
first_trading_day=equity_minute_reader.first_trading_day,
equity_minute_reader=equity_minute_reader,
)
else:
sim_params = factory.create_simulation_parameters(
data_frequency="daily"
)
days = sim_params.sessions
assets = {
1: pd.DataFrame({
"open": [10.1] * len(days),
"high": [10.1] * len(days),
"low": [10.1] * len(days),
"close": [10.1] * len(days),
"volume": [100] * len(days),
"day": [day.value for day in days]
}, index=days)
}
path = os.path.join(tempdir.path, "testdata.bcolz")
BcolzDailyBarWriter(path, self.trading_calendar, days[0],
days[-1]).write(
assets.items()
)
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
self.asset_finder, self.trading_calendar,
first_trading_day=equity_daily_reader.first_trading_day,
equity_daily_reader=equity_daily_reader,
)
if "default_slippage" not in params or \
not params["default_slippage"]:
slippage_func = FixedBasisPointsSlippage()
else:
slippage_func = None
blotter = SimulationBlotter(slippage_func)
start_date = sim_params.first_open
if alternate:
alternator = -1
else:
alternator = 1
tracker = MetricsTracker(
trading_calendar=self.trading_calendar,
first_session=sim_params.start_session,
last_session=sim_params.end_session,
capital_base=sim_params.capital_base,
emission_rate=sim_params.emission_rate,
data_frequency=sim_params.data_frequency,
asset_finder=self.asset_finder,
metrics=load_metrics_set('none'),
)
# replicate what tradesim does by going through every minute or day
# of the simulation and processing open orders each time
if sim_params.data_frequency == "minute":
ticks = minutes
else:
ticks = days
transactions = []
order_list = []
order_date = start_date
for tick in ticks:
blotter.current_dt = tick
if tick >= order_date and len(order_list) < order_count:
# place an order
direction = alternator ** len(order_list)
order_id = blotter.order(
asset1,
order_amount * direction,
MarketOrder(),
)
order_list.append(blotter.orders[order_id])
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
else:
bar_data = BarData(
data_portal=data_portal,
simulation_dt_func=lambda: tick,
data_frequency=sim_params.data_frequency,
trading_calendar=self.trading_calendar,
restrictions=NoRestrictions(),
)
txns, _, closed_orders = blotter.get_transactions(bar_data)
for txn in txns:
tracker.process_transaction(txn)
transactions.append(txn)
blotter.prune_orders(closed_orders)
for i in range(order_count):
order = order_list[i]
self.assertEqual(order.asset, asset1)
self.assertEqual(order.amount, order_amount * alternator ** i)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in range(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
if total_volume == 0:
self.assertRaises(KeyError, lambda: tracker.positions[asset1])
else:
cumulative_pos = tracker.positions[asset1]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain the asset.
oo = blotter.open_orders
self.assertNotIn(
asset1,
oo,
"Entry is removed when no open orders"
)
def test_blotter_processes_splits(self):
blotter = SimulationBlotter(equity_slippage=FixedSlippage())
# set up two open limit orders with very low limit prices,
# one for sid 1 and one for sid 2
asset1 = self.asset_finder.retrieve_asset(1)
asset2 = self.asset_finder.retrieve_asset(2)
asset133 = self.asset_finder.retrieve_asset(133)
blotter.order(asset1, 100, LimitOrder(10, asset=asset1))
blotter.order(asset2, 100, LimitOrder(10, asset=asset2))
# send in splits for assets 133 and 2. We have no open orders for
# asset 133 so it should be ignored.
blotter.process_splits([(asset133, 0.5), (asset2, 0.3333)])
for asset in [asset1, asset2]:
order_lists = blotter.open_orders[asset]
self.assertIsNotNone(order_lists)
self.assertEqual(1, len(order_lists))
asset1_order = blotter.open_orders[1][0]
asset2_order = blotter.open_orders[2][0]
# make sure the asset1 order didn't change
self.assertEqual(100, asset1_order.amount)
self.assertEqual(10, asset1_order.limit)
self.assertEqual(1, asset1_order.asset)
# make sure the asset2 order did change
# to 300 shares at 3.33
self.assertEqual(300, asset2_order.amount)
self.assertEqual(3.33, asset2_order.limit)
self.assertEqual(2, asset2_order.asset)
class SimParamsTestCase(zf.WithTradingCalendars, zf.ZiplineTestCase):
"""
Tests for date management utilities in zipline.finance.trading.
"""
def test_simulation_parameters(self):
sp = SimulationParameters(
start_session=pd.Timestamp("2008-01-01", tz='UTC'),
end_session=pd.Timestamp("2008-12-31", tz='UTC'),
capital_base=100000,
trading_calendar=self.trading_calendar,
)
self.assertTrue(sp.last_close.month == 12)
self.assertTrue(sp.last_close.day == 31)
@timed(DEFAULT_TIMEOUT)
def test_sim_params_days_in_period(self):
# January 2008
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5
# 6 7 8 9 10 11 12
# 13 14 15 16 17 18 19
# 20 21 22 23 24 25 26
# 27 28 29 30 31
params = SimulationParameters(
start_session=pd.Timestamp("2007-12-31", tz='UTC'),
end_session=pd.Timestamp("2008-01-07", tz='UTC'),
capital_base=100000,
trading_calendar=self.trading_calendar,
)
expected_trading_days = (
datetime(2007, 12, 31, tzinfo=pytz.utc),
# Skip new years
# holidays taken from: http://www.nyse.com/press/1191407641943.html
datetime(2008, 1, 2, tzinfo=pytz.utc),
datetime(2008, 1, 3, tzinfo=pytz.utc),
datetime(2008, 1, 4, tzinfo=pytz.utc),
# Skip Saturday
# Skip Sunday
datetime(2008, 1, 7, tzinfo=pytz.utc)
)
num_expected_trading_days = 5
self.assertEquals(
num_expected_trading_days,
len(params.sessions)
)
np.testing.assert_array_equal(expected_trading_days,
params.sessions.tolist())
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