mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-04 20:27:34 +08:00
Eddie Hebert
7904773d00
The latest flake8 release in now 1.5, which pulls in pep8: 1.3.4a0 The upgrade pep8 has changes to what it picks up as lint. Making code base compatible, so that new devs can install pep8 from PyPI and not have friction over the version difference. Currently using these ignores in the config file: ``` [pep8] ignore = E124,E125,E126 ``` Ignoring these since they are difficult to squash while maintaining an 80 char line length, and appear spurious. Should address later. Updates Travis config, README, and pip requirements to reflect change.
336 lines
11 KiB
Python
336 lines
11 KiB
Python
#
|
|
# Copyright 2012 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
|
|
"""
|
|
import pytz
|
|
|
|
from unittest2 import TestCase
|
|
from datetime import datetime, timedelta
|
|
from collections import defaultdict
|
|
|
|
from nose.tools import timed
|
|
|
|
import zipline.utils.factory as factory
|
|
import zipline.utils.simfactory as simfactory
|
|
|
|
from zipline.finance.trading import TradingEnvironment
|
|
from zipline.finance.performance import PerformanceTracker
|
|
from zipline.utils.protocol_utils import ndict
|
|
from zipline.finance.trading import TransactionSimulator
|
|
from zipline.utils.test_utils import(
|
|
setup_logger,
|
|
teardown_logger,
|
|
assert_single_position
|
|
)
|
|
|
|
DEFAULT_TIMEOUT = 15 # seconds
|
|
EXTENDED_TIMEOUT = 90
|
|
|
|
|
|
class FinanceTestCase(TestCase):
|
|
|
|
leased_sockets = defaultdict(list)
|
|
|
|
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):
|
|
trading_environment = factory.create_trading_environment()
|
|
trade_source = factory.create_daily_trade_source(
|
|
[133],
|
|
200,
|
|
trading_environment
|
|
)
|
|
prev = None
|
|
for trade in trade_source:
|
|
if prev:
|
|
self.assertTrue(trade.dt > prev.dt)
|
|
prev = trade
|
|
|
|
@timed(DEFAULT_TIMEOUT)
|
|
def test_trading_environment(self):
|
|
benchmark_returns, treasury_curves = \
|
|
factory.load_market_data()
|
|
|
|
env = TradingEnvironment(
|
|
benchmark_returns,
|
|
treasury_curves,
|
|
period_start=datetime(2008, 1, 1, tzinfo=pytz.utc),
|
|
period_end=datetime(2008, 12, 31, tzinfo=pytz.utc),
|
|
capital_base=100000,
|
|
)
|
|
#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 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(env.is_trading_day(workday))
|
|
|
|
self.assertTrue(env.last_close.month == 12)
|
|
self.assertTrue(env.last_close.day == 31)
|
|
|
|
@timed(EXTENDED_TIMEOUT)
|
|
def test_full_zipline(self):
|
|
#provide enough trades to ensure all orders are filled.
|
|
self.zipline_test_config['order_count'] = 100
|
|
self.zipline_test_config['trade_count'] = 200
|
|
zipline = simfactory.create_test_zipline(**self.zipline_test_config)
|
|
assert_single_position(self, 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.
|
|
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': 1,
|
|
'expected_txn_volume': 24 * 1
|
|
}
|
|
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': 1,
|
|
'expected_txn_volume': 24 * -1
|
|
}
|
|
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': 1,
|
|
'expected_txn_volume': 24 * 1
|
|
}
|
|
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']
|
|
trade_delay = params.get('trade_delay')
|
|
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
|
|
trading_environment = factory.create_trading_environment()
|
|
trade_sim = TransactionSimulator()
|
|
price = [10.1] * trade_count
|
|
volume = [100] * trade_count
|
|
start_date = trading_environment.first_open
|
|
|
|
generated_trades = factory.create_trade_history(
|
|
sid,
|
|
price,
|
|
volume,
|
|
trade_interval,
|
|
trading_environment
|
|
)
|
|
|
|
if alternate:
|
|
alternator = -1
|
|
else:
|
|
alternator = 1
|
|
|
|
order_date = start_date
|
|
for i in xrange(order_count):
|
|
order = ndict({
|
|
'sid': sid,
|
|
'amount': order_amount * alternator ** i,
|
|
'dt': order_date
|
|
})
|
|
|
|
trade_sim.place_order(order)
|
|
|
|
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 = trade_sim.open_orders
|
|
self.assertEqual(len(oo), 1)
|
|
self.assertTrue(sid in oo)
|
|
order_list = oo[sid]
|
|
self.assertEqual(order_count, len(order_list))
|
|
|
|
for i in xrange(order_count):
|
|
order = order_list[i]
|
|
self.assertEqual(order.sid, sid)
|
|
self.assertEqual(order.amount, order_amount * alternator ** i)
|
|
|
|
tracker = PerformanceTracker(trading_environment)
|
|
|
|
# this approximates the loop inside TradingSimulationClient
|
|
transactions = []
|
|
for trade in generated_trades:
|
|
if trade_delay:
|
|
trade.dt = trade.dt + trade_delay
|
|
trade_sim.update(trade)
|
|
if trade.TRANSACTION:
|
|
transactions.append(trade.TRANSACTION)
|
|
|
|
tracker.process_event(trade)
|
|
|
|
if complete_fill:
|
|
self.assertEqual(len(transactions), len(order_list))
|
|
|
|
total_volume = 0
|
|
for i in xrange(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 now be empty
|
|
oo = trade_sim.open_orders
|
|
self.assertTrue(sid in oo)
|
|
order_list = oo[sid]
|
|
self.assertEqual(0, len(order_list))
|