catalyst/tests/test_finance.py

"""
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))