# # 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 """ import itertools import operator import pytz from unittest import TestCase from datetime import datetime, timedelta import numpy as np from nose.tools import timed import zipline.protocol from zipline.protocol import Event, DATASOURCE_TYPE import zipline.utils.factory as factory import zipline.utils.simfactory as simfactory from zipline.finance.blotter import Blotter from zipline.gens.composites import date_sorted_sources import zipline.finance.trading as trading from zipline.finance.trading import SimulationParameters from zipline.finance.performance import PerformanceTracker from zipline.utils.test_utils import( setup_logger, teardown_logger, assert_single_position ) DEFAULT_TIMEOUT = 15 # seconds EXTENDED_TIMEOUT = 90 class FinanceTestCase(TestCase): 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], 200, sim_params ) 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): #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 trading.environment.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(trading.environment.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, ) 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 env = SimulationParameters( period_start=datetime(2007, 12, 31, tzinfo=pytz.utc), period_end=datetime(2008, 1, 7, tzinfo=pytz.utc), capital_base=100000, ) 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, env.days_in_period) np.testing.assert_array_equal(expected_trading_days, env.trading_days.tolist()) @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 ) if alternate: alternator = -1 else: alternator = 1 order_date = start_date for i in xrange(order_count): blotter.set_date(order_date) blotter.order(sid, order_amount * alternator ** i, None, None) 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] 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(sim_params) benchmark_returns = [ Event({'dt': ret.date, 'returns': ret.returns, 'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK, 'source_id': 'benchmarks'}) for ret in trading.environment.benchmark_returns if ret.date.date() >= sim_params.period_start.date() and ret.date.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_event(txn) tracker.process_event(event) 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 = blotter.open_orders self.assertTrue(sid in oo) order_list = oo[sid] self.assertEqual(0, len(order_list)) 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, 10, None, None) blotter.order(2, 100, 10, None, None) # 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'])