def test_blotter_processes_splits(self):
blotter = Blotter('daily', 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))
blotter.order(asset2, 100, LimitOrder(10))
# 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)
def test_limit_order_prices(self,
price,
expected_limit_buy_or_stop_sell,
expected_limit_sell_or_stop_buy):
"""
Test price getters for the LimitOrder class.
"""
style = LimitOrder(price)
self.assertEqual(expected_limit_buy_or_stop_sell,
style.get_limit_price(True))
self.assertEqual(expected_limit_sell_or_stop_buy,
style.get_limit_price(False))
self.assertEqual(None, style.get_stop_price(True))
self.assertEqual(None, style.get_stop_price(False))
def test_batch_order_matches_multiple_orders(self):
"""
Ensure the effect of order_batch is the same as multiple calls to
order.
"""
blotter1 = Blotter(self.sim_params.data_frequency)
blotter2 = Blotter(self.sim_params.data_frequency)
for i in range(1, 4):
order_arg_lists = [
(self.asset_24, i * 100, MarketOrder()),
(self.asset_25, i * 100, LimitOrder(i * 100 + 1)),
]
order_batch_ids = blotter1.batch_order(order_arg_lists)
order_ids = []
for order_args in order_arg_lists:
order_ids.append(blotter2.order(*order_args))
self.assertEqual(len(order_batch_ids), len(order_ids))
self.assertEqual(len(blotter1.open_orders),
len(blotter2.open_orders))
for (asset, _, _), order_batch_id, order_id in zip(
order_arg_lists, order_batch_ids, order_ids
):
self.assertEqual(len(blotter1.open_orders[asset]),
len(blotter2.open_orders[asset]))
self.assertEqual(order_batch_id,
blotter1.open_orders[asset][i-1].id)
self.assertEqual(order_id,
blotter2.open_orders[asset][i-1].id)
def test_order(self):
log.info('creating order')
asset = self.exchange.get_asset('eth_usd')
order_id = self.exchange.order(asset=asset,
style=LimitOrder(limit_price=200),
limit_price=200,
amount=0.5,
stop_price=None)
log.info('order created {}'.format(order_id))
pass
def handle_data(self, data):
from catalyst.api import (
order_percent,
order_target,
order_target_percent,
order_target_value,
order_value,
)
for style in [MarketOrder(), LimitOrder(10),
StopOrder(10), StopLimitOrder(10, 10)]:
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100,
stop_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset, .2,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset, .2,
stop_price=10,
style=style)
def test_invalid_prices(self, price):
"""
Test that execution styles throw appropriate exceptions upon receipt
of an invalid price field.
"""
with self.assertRaises(BadOrderParameters):
LimitOrder(price)
with self.assertRaises(BadOrderParameters):
StopOrder(price)
for lmt, stp in [(price, 1), (1, price), (price, price)]:
with self.assertRaises(BadOrderParameters):
StopLimitOrder(lmt, stp)
def test_limit_order_prices(self, price, expected_limit_buy_or_stop_sell,
expected_limit_sell_or_stop_buy):
"""
Test price getters for the LimitOrder class.
"""
style = LimitOrder(price)
self.assertEqual(expected_limit_buy_or_stop_sell,
style.get_limit_price(True))
self.assertEqual(expected_limit_sell_or_stop_buy,
style.get_limit_price(False))
self.assertEqual(None, style.get_stop_price(True))
self.assertEqual(None, style.get_stop_price(False))
class BlotterTestCase(WithCreateBarData,
WithLogger,
WithDataPortal,
WithSimParams,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-05', tz='utc')
END_DATE = pd.Timestamp('2006-01-06', tz='utc')
ASSET_FINDER_EQUITY_SIDS = 24, 25
@classmethod
def init_class_fixtures(cls):
super(BlotterTestCase, cls).init_class_fixtures()
cls.asset_24 = cls.asset_finder.retrieve_asset(24)
cls.asset_25 = cls.asset_finder.retrieve_asset(25)
cls.future_cl = cls.asset_finder.retrieve_asset(1000)
@classmethod
def make_equity_daily_bar_data(cls):
yield 24, pd.DataFrame(
{
'open': [50, 50],
'high': [50, 50],
'low': [50, 50],
'close': [50, 50],
'volume': [100, 400],
},
index=cls.sim_params.sessions,
)
yield 25, pd.DataFrame(
{
'open': [50, 50],
'high': [50, 50],
'low': [50, 50],
'close': [50, 50],
'volume': [100, 400],
},
index=cls.sim_params.sessions,
)
@classmethod
def make_futures_info(cls):
return pd.DataFrame.from_dict(
{
1000: {
'symbol': 'CLF06',
'root_symbol': 'CL',
'start_date': cls.START_DATE,
'end_date': cls.END_DATE,
'expiration_date': cls.END_DATE,
'auto_close_date': cls.END_DATE,
'exchange': 'CME',
},
},
orient='index',
)
@classproperty
def CREATE_BARDATA_DATA_FREQUENCY(cls):
return cls.sim_params.data_frequency
@parameterized.expand([(MarketOrder(), None, None),
(LimitOrder(10), 10, None),
(StopOrder(10), None, 10),
(StopLimitOrder(10, 20), 10, 20)])
def test_blotter_order_types(self, style_obj, expected_lmt, expected_stp):
blotter = Blotter('daily')
blotter.order(self.asset_24, 100, style_obj)
result = blotter.open_orders[self.asset_24][0]
self.assertEqual(result.limit, expected_lmt)
self.assertEqual(result.stop, expected_stp)
def test_cancel(self):
blotter = Blotter('daily')
oid_1 = blotter.order(self.asset_24, 100, MarketOrder())
oid_2 = blotter.order(self.asset_24, 200, MarketOrder())
oid_3 = blotter.order(self.asset_24, 300, MarketOrder())
# Create an order for another asset to verify that we don't remove it
# when we do cancel_all on 24.
blotter.order(self.asset_25, 150, MarketOrder())
self.assertEqual(len(blotter.open_orders), 2)
self.assertEqual(len(blotter.open_orders[self.asset_24]), 3)
self.assertEqual(
[o.amount for o in blotter.open_orders[self.asset_24]],
[100, 200, 300],
)
blotter.cancel(oid_2)
self.assertEqual(len(blotter.open_orders), 2)
self.assertEqual(len(blotter.open_orders[self.asset_24]), 2)
self.assertEqual(
[o.amount for o in blotter.open_orders[self.asset_24]],
[100, 300],
)
self.assertEqual(
[o.id for o in blotter.open_orders[self.asset_24]],
[oid_1, oid_3],
)
blotter.cancel_all_orders_for_asset(self.asset_24)
self.assertEqual(len(blotter.open_orders), 1)
self.assertEqual(list(blotter.open_orders), [self.asset_25])
def test_blotter_eod_cancellation(self):
blotter = Blotter('minute', cancel_policy=EODCancel())
# Make two orders for the same asset, so we can test that we are not
# mutating the orders list as we are cancelling orders
blotter.order(self.asset_24, 100, MarketOrder())
blotter.order(self.asset_24, -100, MarketOrder())
self.assertEqual(len(blotter.new_orders), 2)
order_ids = [order.id for order in blotter.open_orders[self.asset_24]]
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
self.assertEqual(blotter.new_orders[1].status, ORDER_STATUS.OPEN)
blotter.execute_cancel_policy(BAR)
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
self.assertEqual(blotter.new_orders[1].status, ORDER_STATUS.OPEN)
blotter.execute_cancel_policy(SESSION_END)
for order_id in order_ids:
order = blotter.orders[order_id]
self.assertEqual(order.status, ORDER_STATUS.CANCELLED)
def test_blotter_never_cancel(self):
blotter = Blotter('minute', cancel_policy=NeverCancel())
blotter.order(self.asset_24, 100, MarketOrder())
self.assertEqual(len(blotter.new_orders), 1)
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
blotter.execute_cancel_policy(BAR)
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
blotter.execute_cancel_policy(SESSION_END)
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
def test_order_rejection(self):
blotter = Blotter(self.sim_params.data_frequency)
# Reject a nonexistent order -> no order appears in new_order,
# no exceptions raised out
blotter.reject(56)
self.assertEqual(blotter.new_orders, [])
# Basic tests of open order behavior
open_order_id = blotter.order(self.asset_24, 100, MarketOrder())
second_order_id = blotter.order(self.asset_24, 50, MarketOrder())
self.assertEqual(len(blotter.open_orders[self.asset_24]), 2)
open_order = blotter.open_orders[self.asset_24][0]
self.assertEqual(open_order.status, ORDER_STATUS.OPEN)
self.assertEqual(open_order.id, open_order_id)
self.assertIn(open_order, blotter.new_orders)
# Reject that order immediately (same bar, i.e. still in new_orders)
blotter.reject(open_order_id)
self.assertEqual(len(blotter.new_orders), 2)
self.assertEqual(len(blotter.open_orders[self.asset_24]), 1)
still_open_order = blotter.new_orders[0]
self.assertEqual(still_open_order.id, second_order_id)
self.assertEqual(still_open_order.status, ORDER_STATUS.OPEN)
rejected_order = blotter.new_orders[1]
self.assertEqual(rejected_order.status, ORDER_STATUS.REJECTED)
self.assertEqual(rejected_order.reason, '')
# Do it again, but reject it at a later time (after tradesimulation
# pulls it from new_orders)
blotter = Blotter(self.sim_params.data_frequency)
new_open_id = blotter.order(self.asset_24, 10, MarketOrder())
new_open_order = blotter.open_orders[self.asset_24][0]
self.assertEqual(new_open_id, new_open_order.id)
# Pretend that the trade simulation did this.
blotter.new_orders = []
rejection_reason = "Not enough cash on hand."
blotter.reject(new_open_id, reason=rejection_reason)
rejected_order = blotter.new_orders[0]
self.assertEqual(rejected_order.id, new_open_id)
self.assertEqual(rejected_order.status, ORDER_STATUS.REJECTED)
self.assertEqual(rejected_order.reason, rejection_reason)
# You can't reject a filled order.
# Reset for paranoia
blotter = Blotter(self.sim_params.data_frequency)
blotter.slippage_models[Equity] = FixedSlippage()
filled_id = blotter.order(self.asset_24, 100, MarketOrder())
filled_order = None
blotter.current_dt = self.sim_params.sessions[-1]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.sim_params.sessions[-1],
)
txns, _, closed_orders = blotter.get_transactions(bar_data)
for txn in txns:
filled_order = blotter.orders[txn.order_id]
blotter.prune_orders(closed_orders)
self.assertEqual(filled_order.id, filled_id)
self.assertIn(filled_order, blotter.new_orders)
self.assertEqual(filled_order.status, ORDER_STATUS.FILLED)
self.assertNotIn(filled_order, blotter.open_orders[self.asset_24])
blotter.reject(filled_id)
updated_order = blotter.orders[filled_id]
self.assertEqual(updated_order.status, ORDER_STATUS.FILLED)
def test_order_hold(self):
"""
Held orders act almost identically to open orders, except for the
status indication. When a fill happens, the order should switch
status to OPEN/FILLED as necessary
"""
blotter = Blotter(self.sim_params.data_frequency)
# Nothing happens on held of a non-existent order
blotter.hold(56)
self.assertEqual(blotter.new_orders, [])
open_id = blotter.order(self.asset_24, 100, MarketOrder())
open_order = blotter.open_orders[self.asset_24][0]
self.assertEqual(open_order.id, open_id)
blotter.hold(open_id)
self.assertEqual(len(blotter.new_orders), 1)
self.assertEqual(len(blotter.open_orders[self.asset_24]), 1)
held_order = blotter.new_orders[0]
self.assertEqual(held_order.status, ORDER_STATUS.HELD)
self.assertEqual(held_order.reason, '')
blotter.cancel(held_order.id)
self.assertEqual(len(blotter.new_orders), 1)
self.assertEqual(len(blotter.open_orders[self.asset_24]), 0)
cancelled_order = blotter.new_orders[0]
self.assertEqual(cancelled_order.id, held_order.id)
self.assertEqual(cancelled_order.status, ORDER_STATUS.CANCELLED)
for data in ([100, self.sim_params.sessions[0]],
[400, self.sim_params.sessions[1]]):
# Verify that incoming fills will change the order status.
trade_amt = data[0]
dt = data[1]
order_size = 100
expected_filled = int(trade_amt *
DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT)
expected_open = order_size - expected_filled
expected_status = ORDER_STATUS.OPEN if expected_open else \
ORDER_STATUS.FILLED
blotter = Blotter(self.sim_params.data_frequency)
open_id = blotter.order(self.asset_24, order_size, MarketOrder())
open_order = blotter.open_orders[self.asset_24][0]
self.assertEqual(open_id, open_order.id)
blotter.hold(open_id)
held_order = blotter.new_orders[0]
filled_order = None
blotter.current_dt = dt
bar_data = self.create_bardata(
simulation_dt_func=lambda: dt,
)
txns, _, _ = blotter.get_transactions(bar_data)
for txn in txns:
filled_order = blotter.orders[txn.order_id]
self.assertEqual(filled_order.id, held_order.id)
self.assertEqual(filled_order.status, expected_status)
self.assertEqual(filled_order.filled, expected_filled)
self.assertEqual(filled_order.open_amount, expected_open)
def test_prune_orders(self):
blotter = Blotter(self.sim_params.data_frequency)
blotter.order(self.asset_24, 100, MarketOrder())
open_order = blotter.open_orders[self.asset_24][0]
blotter.prune_orders([])
self.assertEqual(1, len(blotter.open_orders[self.asset_24]))
blotter.prune_orders([open_order])
self.assertEqual(0, len(blotter.open_orders[self.asset_24]))
# prune an order that isn't in our our open orders list, make sure
# nothing blows up
other_order = Order(
dt=blotter.current_dt,
asset=self.asset_25,
amount=1
)
blotter.prune_orders([other_order])
def test_batch_order_matches_multiple_orders(self):
"""
Ensure the effect of order_batch is the same as multiple calls to
order.
"""
blotter1 = Blotter(self.sim_params.data_frequency)
blotter2 = Blotter(self.sim_params.data_frequency)
for i in range(1, 4):
order_arg_lists = [
(self.asset_24, i * 100, MarketOrder()),
(self.asset_25, i * 100, LimitOrder(i * 100 + 1)),
]
order_batch_ids = blotter1.batch_order(order_arg_lists)
order_ids = []
for order_args in order_arg_lists:
order_ids.append(blotter2.order(*order_args))
self.assertEqual(len(order_batch_ids), len(order_ids))
self.assertEqual(len(blotter1.open_orders),
len(blotter2.open_orders))
for (asset, _, _), order_batch_id, order_id in zip(
order_arg_lists, order_batch_ids, order_ids
):
self.assertEqual(len(blotter1.open_orders[asset]),
len(blotter2.open_orders[asset]))
self.assertEqual(order_batch_id,
blotter1.open_orders[asset][i-1].id)
self.assertEqual(order_id,
blotter2.open_orders[asset][i-1].id)
def test_slippage_and_commission_dispatching(self):
blotter = Blotter(
self.sim_params.data_frequency,
equity_slippage=FixedSlippage(spread=0.0),
future_slippage=FixedSlippage(spread=2.0),
equity_commission=PerTrade(cost=1.0),
future_commission=PerTrade(cost=2.0),
)
blotter.order(self.asset_24, 1, MarketOrder())
blotter.order(self.future_cl, 1, MarketOrder())
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.sim_params.sessions[-1],
)
txns, commissions, _ = blotter.get_transactions(bar_data)
# The equity transaction should have the same price as its current
# price because the slippage spread is zero. Its commission should be
# $1.00.
equity_txn = txns[0]
self.assertEqual(
equity_txn.price,
bar_data.current(equity_txn.asset, 'price'),
)
self.assertEqual(commissions[0]['cost'], 1.0)
# The future transaction price should be 1.0 more than its current
# price because half of the 'future_slippage' spread is added. Its
# commission should be $2.00.
future_txn = txns[1]
self.assertEqual(
future_txn.price,
bar_data.current(future_txn.asset, 'price') + 1.0,
)
self.assertEqual(commissions[1]['cost'], 2.0)