code: #
  # 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 os
  
  from nose.tools import timed
  from nose.tools import nottest
  
  import numpy as np
  import pandas as pd
  import pytz
  from six import iteritems
  from six.moves import range
  from testfixtures import TempDirectory
  
  from zipline.finance.blotter.simulation_blotter import SimulationBlotter
  from zipline.finance.execution import MarketOrder, LimitOrder
  from zipline.finance.metrics import MetricsTracker, load as load_metrics_set
  from zipline.finance.trading import SimulationParameters
  from zipline.data.us_equity_pricing import BcolzDailyBarReader
  from zipline.data.minute_bars import BcolzMinuteBarReader
  from zipline.data.data_portal import DataPortal
  from zipline.data.us_equity_pricing import BcolzDailyBarWriter
  from zipline.finance.slippage import FixedSlippage, FixedBasisPointsSlippage
  from zipline.finance.asset_restrictions import NoRestrictions
  from zipline.protocol import BarData
  from zipline.testing import write_bcolz_minute_data
  import zipline.testing.fixtures as zf
  import zipline.utils.factory as factory
  
  DEFAULT_TIMEOUT = 15  # seconds
  EXTENDED_TIMEOUT = 90
  
  _multiprocess_can_split_ = False
  
  
  class FinanceTestCase(zf.WithAssetFinder,
                        zf.WithTradingCalendars,
                        zf.ZiplineTestCase):
      ASSET_FINDER_EQUITY_SIDS = 1, 2, 133
      start = START_DATE = pd.Timestamp('2006-01-01', tz='utc')
      end = END_DATE = pd.Timestamp('2006-12-31', tz='utc')
  
      def init_instance_fixtures(self):
          super(FinanceTestCase, self).init_instance_fixtures()
          self.zipline_test_config = {'sid': 133}
  
      # TODO: write tests for short sales
      # TODO: write a test to do massive buying or shorting.
  
      @timed(DEFAULT_TIMEOUT)
      @nottest
      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_interval': timedelta(minutes=1),
              'order_count': 2,
              'order_amount': 100,
              'order_interval': timedelta(minutes=1),
              # because we placed two orders for 100 shares each, and the volume
              # of each trade is 100, and by default you can take up 10% of the
              # bar's volume (per FixedBasisPointsSlippage, the default slippage
              # model), the simulator should spread the order into 20 trades of
              # 10 shares per order.
              'expected_txn_count': 20,
              'expected_txn_volume': 2 * 100,
              'default_slippage': True
          }
  
          self.transaction_sim(**params)
  
          # same scenario, but with short sales
          params2 = {
              'trade_count': 360,
              'trade_interval': timedelta(minutes=1),
              'order_count': 2,
              'order_amount': -100,
              'order_interval': timedelta(minutes=1),
              'expected_txn_count': 20,
              'expected_txn_volume': 2 * -100,
              'default_slippage': True
          }
  
          self.transaction_sim(**params2)
  
      @timed(DEFAULT_TIMEOUT)
      @nottest
      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_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_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_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)
      @nottest
      def test_alternating_long_short(self):
          # create a scenario where we alternate buys and sells
          params1 = {
              'trade_count': int(6.5 * 60 * 4),
              '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')
  
          asset1 = self.asset_finder.retrieve_asset(1)
          with TempDirectory() as tempdir:
  
              if trade_interval < timedelta(days=1):
                  sim_params = factory.create_simulation_parameters(
                      start=self.start,
                      end=self.end,
                      data_frequency="minute"
                  )
  
                  minutes = self.trading_calendar.minutes_window(
                      sim_params.first_open,
                      int((trade_interval.total_seconds() / 60) * trade_count)
                      + 100)
  
                  price_data = np.array([10.1] * len(minutes))
                  assets = {
                      asset1.sid: pd.DataFrame({
                          "open": price_data,
                          "high": price_data,
                          "low": price_data,
                          "close": price_data,
                          "volume": np.array([100] * len(minutes)),
                          "dt": minutes
                      }).set_index("dt")
                  }
  
                  write_bcolz_minute_data(
                      self.trading_calendar,
                      self.trading_calendar.sessions_in_range(
                          self.trading_calendar.minute_to_session_label(
                              minutes[0]
                          ),
                          self.trading_calendar.minute_to_session_label(
                              minutes[-1]
                          )
                      ),
                      tempdir.path,
                      iteritems(assets),
                  )
  
                  equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
  
                  data_portal = DataPortal(
                      self.asset_finder, self.trading_calendar,
                      first_trading_day=equity_minute_reader.first_trading_day,
                      equity_minute_reader=equity_minute_reader,
                  )
              else:
                  sim_params = factory.create_simulation_parameters(
                      data_frequency="daily"
                  )
  
                  days = sim_params.sessions
  
                  assets = {
                      1: pd.DataFrame({
                          "open": [10.1] * len(days),
                          "high": [10.1] * len(days),
                          "low": [10.1] * len(days),
                          "close": [10.1] * len(days),
                          "volume": [100] * len(days),
                          "day": [day.value for day in days]
                      }, index=days)
                  }
  
                  path = os.path.join(tempdir.path, "testdata.bcolz")
                  BcolzDailyBarWriter(path, self.trading_calendar, days[0],
                                      days[-1]).write(
                      assets.items()
                  )
  
                  equity_daily_reader = BcolzDailyBarReader(path)
  
                  data_portal = DataPortal(
                      self.asset_finder, self.trading_calendar,
                      first_trading_day=equity_daily_reader.first_trading_day,
                      equity_daily_reader=equity_daily_reader,
                  )
  
              if "default_slippage" not in params or \
                 not params["default_slippage"]:
                  slippage_func = FixedBasisPointsSlippage()
              else:
                  slippage_func = None
  
              blotter = SimulationBlotter(slippage_func)
  
              start_date = sim_params.first_open
  
              if alternate:
                  alternator = -1
              else:
                  alternator = 1
  
              tracker = MetricsTracker(
                  trading_calendar=self.trading_calendar,
                  first_session=sim_params.start_session,
                  last_session=sim_params.end_session,
                  capital_base=sim_params.capital_base,
                  emission_rate=sim_params.emission_rate,
                  data_frequency=sim_params.data_frequency,
                  asset_finder=self.asset_finder,
                  metrics=load_metrics_set('none'),
              )
  
              # replicate what tradesim does by going through every minute or day
              # of the simulation and processing open orders each time
              if sim_params.data_frequency == "minute":
                  ticks = minutes
              else:
                  ticks = days
  
              transactions = []
  
              order_list = []
              order_date = start_date
              for tick in ticks:
                  blotter.current_dt = tick
                  if tick >= order_date and len(order_list) < order_count:
                      # place an order
                      direction = alternator ** len(order_list)
                      order_id = blotter.order(
                          asset1,
                          order_amount * direction,
                          MarketOrder(),
                      )
                      order_list.append(blotter.orders[order_id])
                      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)
                  else:
                      bar_data = BarData(
                          data_portal=data_portal,
                          simulation_dt_func=lambda: tick,
                          data_frequency=sim_params.data_frequency,
                          trading_calendar=self.trading_calendar,
                          restrictions=NoRestrictions(),
                      )
                      txns, _, closed_orders = blotter.get_transactions(bar_data)
                      for txn in txns:
                          tracker.process_transaction(txn)
                          transactions.append(txn)
  
                      blotter.prune_orders(closed_orders)
  
              for i in range(order_count):
                  order = order_list[i]
                  self.assertEqual(order.asset, asset1)
                  self.assertEqual(order.amount, order_amount * alternator ** i)
  
              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)
  
              if total_volume == 0:
                  self.assertRaises(KeyError, lambda: tracker.positions[asset1])
              else:
                  cumulative_pos = tracker.positions[asset1]
                  self.assertEqual(total_volume, cumulative_pos.amount)
  
              # the open orders should not contain the asset.
              oo = blotter.open_orders
              self.assertNotIn(
                  asset1,
                  oo,
                  "Entry is removed when no open orders"
              )
  
      def test_blotter_processes_splits(self):
          blotter = SimulationBlotter(equity_slippage=FixedSlippage())
  
          # set up two open limit orders with very low limit prices,
          # one for sid 1 and one for sid 2
          asset1 = self.asset_finder.retrieve_asset(1)
          asset2 = self.asset_finder.retrieve_asset(2)
          asset133 = self.asset_finder.retrieve_asset(133)
  
          blotter.order(asset1, 100, LimitOrder(10, asset=asset1))
          blotter.order(asset2, 100, LimitOrder(10, asset=asset2))
  
          # send in splits for assets 133 and 2.  We have no open orders for
          # asset 133 so it should be ignored.
          blotter.process_splits([(asset133, 0.5), (asset2, 0.3333)])
  
          for asset in [asset1, asset2]:
              order_lists = blotter.open_orders[asset]
              self.assertIsNotNone(order_lists)
              self.assertEqual(1, len(order_lists))
  
          asset1_order = blotter.open_orders[1][0]
          asset2_order = blotter.open_orders[2][0]
  
          # make sure the asset1 order didn't change
          self.assertEqual(100, asset1_order.amount)
          self.assertEqual(10, asset1_order.limit)
          self.assertEqual(1, asset1_order.asset)
  
          # make sure the asset2 order did change
          # to 300 shares at 3.33
          self.assertEqual(300, asset2_order.amount)
          self.assertEqual(3.33, asset2_order.limit)
          self.assertEqual(2, asset2_order.asset)
  
  
  class SimParamsTestCase(zf.WithTradingCalendars, zf.ZiplineTestCase):
      """
      Tests for date management utilities in zipline.finance.trading.
      """
      def test_simulation_parameters(self):
          sp = SimulationParameters(
              start_session=pd.Timestamp("2008-01-01", tz='UTC'),
              end_session=pd.Timestamp("2008-12-31", tz='UTC'),
              capital_base=100000,
              trading_calendar=self.trading_calendar,
          )
  
          self.assertTrue(sp.last_close.month == 12)
          self.assertTrue(sp.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(
              start_session=pd.Timestamp("2007-12-31", tz='UTC'),
              end_session=pd.Timestamp("2008-01-07", tz='UTC'),
              capital_base=100000,
              trading_calendar=self.trading_calendar,
          )
  
          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,
              len(params.sessions)
          )
          np.testing.assert_array_equal(expected_trading_days,
                                        params.sessions.tolist())
  
apis: pandas.Timestamp