""" Tests for the zipline.finance package """ import pytz import zmq from unittest2 import TestCase from datetime import datetime, timedelta from collections import defaultdict from nose.tools import timed import zipline.utils.factory as factory from zipline.test_algorithms import TestAlgorithm from zipline.finance.trading import TradingEnvironment from zipline.core.devsimulator import AddressAllocator from zipline.lines import SimulatedTrading 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 \ drain_zipline, \ setup_logger, \ teardown_logger,\ assert_single_position DEFAULT_TIMEOUT = 15 # seconds EXTENDED_TIMEOUT = 90 allocator = AddressAllocator(1000) class FinanceTestCase(TestCase): leased_sockets = defaultdict(list) def setUp(self): self.zipline_test_config = { 'sid' : 133, 'results_socket_uri' : allocator.lease(1)[0] } self.ctx = zmq.Context() 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, max_drawdown = 0.50 ) #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 = SimulatedTrading.create_test_zipline(**self.zipline_test_config) assert_single_position(self, zipline) #@timed(DEFAULT_TIMEOUT) def test_sid_filter(self): # Ensure the algorithm's filter prevents events from arriving. # create a test algorithm whose filter will not match any of the # trade events sourced inside the zipline. order_amount = 100 order_count = 100 no_match_sid = 222 test_algo = TestAlgorithm( no_match_sid, order_amount, order_count ) self.zipline_test_config['trade_count'] = 200 self.zipline_test_config['algorithm'] = test_algo zipline = SimulatedTrading.create_test_zipline( **self.zipline_test_config ) output, transaction_count = drain_zipline(self, zipline) #check that the algorithm received no events self.assertEqual( 0, transaction_count, "The algorithm should not receive any events due to filtering." ) # 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) @timed(DEFAULT_TIMEOUT) def test_partial_expiration_orders(self): # create a scenario where orders expire without being filled # entirely params1 = { 'trade_count':100, 'trade_amount':100, 'trade_delay': timedelta(minutes=5), 'trade_interval': timedelta(days=1), 'order_count':3, 'order_amount':1000, 'order_interval': timedelta(minutes=30), # 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' : 25 } self.transaction_sim(**params1) # same scenario, but short sales. params2 = { 'trade_count' : 100, 'trade_amount' : 100, 'trade_delay' : timedelta(minutes=5), 'trade_interval' : timedelta(days=1), 'order_count' : 3, 'order_amount' :-1000, 'order_interval' : timedelta(minutes=30), # 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' : -25 } self.transaction_sim(**params2) @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_amount = params['trade_amount'] 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([sid]) 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(oo.has_key(sid)) 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, [sid]) # this approximates the loop inside TradingSimulationClient transactions = [] for trade in generated_trades: if trade_delay: trade.dt = trade.dt + trade_delay txn = trade_sim.apply_trade_to_open_orders(trade) if txn: transactions.append(txn) trade.TRANSACTION = txn else: trade.TRANSACTION = None 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(oo.has_key(sid)) order_list = oo[sid] self.assertEqual(0, len(order_list))