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catalyst/tests/test_finance.py
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Joe Jevnik 721dd36116 TST: move test_utils and adds test fixture classes 
Renames zipline.utils.test_utils to zipline.testing

Adds zipline.testing.fixtures.ZiplineTestCase to manage setup and
teardown and adds mixins to define fixtures like an asset finder or
trading calendar.
2016-03-10 15:39:52 -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 itertools
import operator
from unittest import TestCase
from nose.tools import timed
import numpy as np
import pandas as pd
import pytz
from six.moves import range
from zipline.finance.blotter import Blotter
from zipline.finance.execution import MarketOrder, LimitOrder
from zipline.finance.trading import TradingEnvironment
from zipline.finance.performance import PerformanceTracker
from zipline.finance.trading import SimulationParameters
from zipline.gens.composites import date_sorted_sources
import zipline.protocol
from zipline.protocol import Event, DATASOURCE_TYPE
from zipline.testing import(
setup_logger,
teardown_logger,
assert_single_position
)
import zipline.utils.factory as factory
import zipline.utils.simfactory as simfactory
DEFAULT_TIMEOUT = 15 # seconds
EXTENDED_TIMEOUT = 90
_multiprocess_can_split_ = False
class FinanceTestCase(TestCase):
@classmethod
def setUpClass(cls):
cls.env = TradingEnvironment()
cls.env.write_data(equities_identifiers=[1, 133])
@classmethod
def tearDownClass(cls):
del cls.env
def setUp(self):
self.zipline_test_config = {
'sid': 133,
}
setup_logger(self)
def tearDown(self):
teardown_logger(self)
@timed(DEFAULT_TIMEOUT)
def test_factory_daily(self):
sim_params = factory.create_simulation_parameters()
trade_source = factory.create_daily_trade_source(
[133],
sim_params,
env=self.env,
)
prev = None
for trade in trade_source:
if prev:
self.assertTrue(trade.dt > prev.dt)
prev = trade
@timed(EXTENDED_TIMEOUT)
def test_full_zipline(self):
# provide enough trades to ensure all orders are filled.
self.zipline_test_config['order_count'] = 100
# making a small order amount, so that each order is filled
# in a single transaction, and txn_count == order_count.
self.zipline_test_config['order_amount'] = 25
# No transactions can be filled on the first trade, so
# we have one extra trade to ensure all orders are filled.
self.zipline_test_config['trade_count'] = 101
full_zipline = simfactory.create_test_zipline(
**self.zipline_test_config)
assert_single_position(self, full_zipline)
# 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_amount': 100,
'trade_interval': timedelta(minutes=1),
'order_count': 2,
'order_amount': 100,
'order_interval': timedelta(minutes=1),
# because we placed an order for 100 shares, and the volume
# of each trade is 100, the simulator should spread the order
# into 4 trades of 25 shares per order.
'expected_txn_count': 8,
'expected_txn_volume': 2 * 100
}
self.transaction_sim(**params)
# same scenario, but with short sales
params2 = {
'trade_count': 360,
'trade_amount': 100,
'trade_interval': timedelta(minutes=1),
'order_count': 2,
'order_amount': -100,
'order_interval': timedelta(minutes=1),
'expected_txn_count': 8,
'expected_txn_volume': 2 * -100
}
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_amount': 100,
'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_amount': 100,
'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_amount': 100,
'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_amount': 100,
'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')
sid = 1
sim_params = factory.create_simulation_parameters()
blotter = Blotter()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = sim_params.first_open
generated_trades = factory.create_trade_history(
sid,
price,
volume,
trade_interval,
sim_params,
env=self.env,
)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in range(order_count):
blotter.set_date(order_date)
blotter.order(sid, order_amount * alternator ** i, MarketOrder())
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)
# there should now be one open order list stored under the sid
oo = blotter.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid][:] # make copy
self.assertEqual(order_count, len(order_list))
for i in range(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator ** i)
tracker = PerformanceTracker(sim_params, env=self.env)
benchmark_returns = [
Event({'dt': dt,
'returns': ret,
'type':
zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in self.env.benchmark_returns.iteritems()
if dt.date() >= sim_params.period_start.date() and
dt.date() <= sim_params.period_end.date()
]
generated_events = date_sorted_sources(generated_trades,
benchmark_returns)
# this approximates the loop inside TradingSimulationClient
transactions = []
for dt, events in itertools.groupby(generated_events,
operator.attrgetter('dt')):
for event in events:
if event.type == DATASOURCE_TYPE.TRADE:
for txn, order in blotter.process_trade(event):
transactions.append(txn)
tracker.process_transaction(txn)
elif event.type == DATASOURCE_TYPE.BENCHMARK:
tracker.process_benchmark(event)
elif event.type == DATASOURCE_TYPE.TRADE:
tracker.process_trade(event)
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)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain sid.
oo = blotter.open_orders
self.assertNotIn(sid, oo, "Entry is removed when no open orders")
def test_blotter_processes_splits(self):
sim_params = factory.create_simulation_parameters()
blotter = Blotter()
blotter.set_date(sim_params.period_start)
# set up two open limit orders with very low limit prices,
# one for sid 1 and one for sid 2
blotter.order(1, 100, LimitOrder(10))
blotter.order(2, 100, LimitOrder(10))
# send in a split for sid 2
split_event = factory.create_split(2, 0.33333,
sim_params.period_start +
timedelta(days=1))
blotter.process_split(split_event)
for sid in [1, 2]:
order_lists = blotter.open_orders[sid]
self.assertIsNotNone(order_lists)
self.assertEqual(1, len(order_lists))
aapl_order = blotter.open_orders[1][0].to_dict()
fls_order = blotter.open_orders[2][0].to_dict()
# make sure the aapl order didn't change
self.assertEqual(100, aapl_order['amount'])
self.assertEqual(10, aapl_order['limit'])
self.assertEqual(1, aapl_order['sid'])
# make sure the fls order did change
# to 300 shares at 3.33
self.assertEqual(300, fls_order['amount'])
self.assertEqual(3.33, fls_order['limit'])
self.assertEqual(2, fls_order['sid'])
class TradingEnvironmentTestCase(TestCase):
"""
Tests for date management utilities in zipline.finance.trading.
"""
def setUp(self):
setup_logger(self)
def tearDown(self):
teardown_logger(self)
@classmethod
def setUpClass(cls):
cls.env = TradingEnvironment()
@classmethod
def tearDownClass(cls):
del cls.env
@timed(DEFAULT_TIMEOUT)
def test_is_trading_day(self):
# holidays taken from: http://www.nyse.com/press/1191407641943.html
new_years = datetime(2008, 1, 1, tzinfo=pytz.utc)
mlk_day = datetime(2008, 1, 21, tzinfo=pytz.utc)
presidents = datetime(2008, 2, 18, tzinfo=pytz.utc)
good_friday = datetime(2008, 3, 21, tzinfo=pytz.utc)
memorial_day = datetime(2008, 5, 26, tzinfo=pytz.utc)
july_4th = datetime(2008, 7, 4, tzinfo=pytz.utc)
labor_day = datetime(2008, 9, 1, tzinfo=pytz.utc)
tgiving = datetime(2008, 11, 27, tzinfo=pytz.utc)
christmas = datetime(2008, 5, 25, tzinfo=pytz.utc)
a_saturday = datetime(2008, 8, 2, tzinfo=pytz.utc)
a_sunday = datetime(2008, 10, 12, tzinfo=pytz.utc)
holidays = [
new_years,
mlk_day,
presidents,
good_friday,
memorial_day,
july_4th,
labor_day,
tgiving,
christmas,
a_saturday,
a_sunday
]
for holiday in holidays:
self.assertTrue(not self.env.is_trading_day(holiday))
first_trading_day = datetime(2008, 1, 2, tzinfo=pytz.utc)
last_trading_day = datetime(2008, 12, 31, tzinfo=pytz.utc)
workdays = [first_trading_day, last_trading_day]
for workday in workdays:
self.assertTrue(self.env.is_trading_day(workday))
def test_simulation_parameters(self):
env = SimulationParameters(
period_start=datetime(2008, 1, 1, tzinfo=pytz.utc),
period_end=datetime(2008, 12, 31, tzinfo=pytz.utc),
capital_base=100000,
env=self.env,
)
self.assertTrue(env.last_close.month == 12)
self.assertTrue(env.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(
period_start=datetime(2007, 12, 31, tzinfo=pytz.utc),
period_end=datetime(2008, 1, 7, tzinfo=pytz.utc),
capital_base=100000,
env=self.env,
)
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, params.days_in_period)
np.testing.assert_array_equal(expected_trading_days,
params.trading_days.tolist())
@timed(DEFAULT_TIMEOUT)
def test_market_minute_window(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
us_east = pytz.timezone('US/Eastern')
utc = pytz.utc
# 10:01 AM Eastern on January 7th..
start = us_east.localize(datetime(2008, 1, 7, 10, 1))
utc_start = start.astimezone(utc)
# Get the next 10 minutes
minutes = self.env.market_minute_window(
utc_start, 10,
)
self.assertEqual(len(minutes), 10)
for i in range(10):
self.assertEqual(minutes[i], utc_start + timedelta(minutes=i))
# Get the previous 10 minutes.
minutes = self.env.market_minute_window(
utc_start, 10, step=-1,
)
self.assertEqual(len(minutes), 10)
for i in range(10):
self.assertEqual(minutes[i], utc_start + timedelta(minutes=-i))
# Get the next 900 minutes, including utc_start, rolling over into the
# next two days.
# Should include:
# Today: 10:01 AM -> 4:00 PM (360 minutes)
# Tomorrow: 9:31 AM -> 4:00 PM (390 minutes, 750 total)
# Last Day: 9:31 AM -> 12:00 PM (150 minutes, 900 total)
minutes = self.env.market_minute_window(
utc_start, 900,
)
today = self.env.market_minutes_for_day(start)[30:]
tomorrow = self.env.market_minutes_for_day(
start + timedelta(days=1)
)
last_day = self.env.market_minutes_for_day(
start + timedelta(days=2))[:150]
self.assertEqual(len(minutes), 900)
self.assertEqual(minutes[0], utc_start)
self.assertTrue(all(today == minutes[:360]))
self.assertTrue(all(tomorrow == minutes[360:750]))
self.assertTrue(all(last_day == minutes[750:]))
# Get the previous 801 minutes, including utc_start, rolling over into
# Friday the 4th and Thursday the 3rd.
# Should include:
# Today: 10:01 AM -> 9:31 AM (31 minutes)
# Friday: 4:00 PM -> 9:31 AM (390 minutes, 421 total)
# Thursday: 4:00 PM -> 9:41 AM (380 minutes, 801 total)
minutes = self.env.market_minute_window(
utc_start, 801, step=-1,
)
today = self.env.market_minutes_for_day(start)[30::-1]
# minus an extra two days from each of these to account for the two
# weekend days we skipped
friday = self.env.market_minutes_for_day(
start + timedelta(days=-3),
)[::-1]
thursday = self.env.market_minutes_for_day(
start + timedelta(days=-4),
)[:9:-1]
self.assertEqual(len(minutes), 801)
self.assertEqual(minutes[0], utc_start)
self.assertTrue(all(today == minutes[:31]))
self.assertTrue(all(friday == minutes[31:421]))
self.assertTrue(all(thursday == minutes[421:]))
def test_min_date(self):
min_date = pd.Timestamp('2016-03-04', tz='UTC')
env = TradingEnvironment(min_date=min_date)
self.assertGreaterEqual(env.first_trading_day, min_date)
self.assertGreaterEqual(env.treasury_curves.index[0],
min_date)
def test_max_date(self):
max_date = pd.Timestamp('2008-08-01', tz='UTC')
env = TradingEnvironment(max_date=max_date)
self.assertLessEqual(env.last_trading_day, max_date)
self.assertLessEqual(env.treasury_curves.index[-1],
max_date)
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