def test_market_order_prices(self):
"""
Basic unit tests for the MarketOrder class.
"""
style = MarketOrder()
self.assertEqual(style.get_limit_price(True), None)
self.assertEqual(style.get_limit_price(False), None)
self.assertEqual(style.get_stop_price(True), None)
self.assertEqual(style.get_stop_price(False), None)
def test_blotter_eod_cancellation(self):
blotter = SimulationBlotter(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_blotter_never_cancel(self):
blotter = SimulationBlotter(cancel_policy=NeverCancel())
blotter.order(self.asset_24, 100, MarketOrder())
assert len(blotter.new_orders) == 1
assert blotter.new_orders[0].status == ORDER_STATUS.OPEN
blotter.execute_cancel_policy(BAR)
assert blotter.new_orders[0].status == ORDER_STATUS.OPEN
blotter.execute_cancel_policy(SESSION_END)
assert blotter.new_orders[0].status == ORDER_STATUS.OPEN
def handle_data(self, data):
from zipline.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.sid, 10, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order(self.sid, 10, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.sid, 300, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.sid, 300, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.sid, .1, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.sid, .1, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.sid, 100, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.sid, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.sid, 100, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.sid, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.sid, .2, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.sid, .2, stop_price=10, style=style)
def test_transactions_created_for_complete_orders(self, symbol_lookup):
with patch('zipline.gens.brokers.ib_broker.TWSConnection.connect'):
broker = IBBroker("localhost:9999:1111", account_id='TEST-123')
broker._tws.nextValidId(0)
asset = self.asset_finder.retrieve_asset(1)
symbol_lookup.return_value = asset
order_count = 0
for amount, order_style in [(-112,
StopLimitOrder(limit_price=9,
stop_price=1)),
(43, LimitOrder(limit_price=10)),
(-99, StopOrder(stop_price=8)),
(-32, MarketOrder())]:
order = broker.order(asset, amount, order_style)
broker._tws.orderStatus(order.broker_order_id,
'Filled',
filled=int(fabs(amount)),
remaining=0,
avg_fill_price=111,
perm_id=0,
parent_id=1,
last_fill_price=112,
client_id=1111,
why_held='')
contract = self._create_contract(str(asset.symbol))
(shares, cum_qty, price, avg_price, exec_time, exec_id) = \
(int(fabs(amount)), int(fabs(amount)), 12.3, 12.31,
pd.to_datetime('now', utc=True), order_count)
exec_detail = self._create_exec_detail(order.broker_order_id,
shares, cum_qty, price,
avg_price, exec_time,
exec_id)
broker._tws.execDetails(0, contract, exec_detail)
order_count += 1
assert len(broker.transactions) == order_count
transactions = [
tx for tx in broker.transactions.values()
if tx.order_id == order.id
]
assert len(transactions) == 1
assert broker.transactions[exec_id].asset == asset
assert broker.transactions[exec_id].amount == order.amount
assert (broker.transactions[exec_id].dt -
pd.to_datetime('now', utc=True) < pd.Timedelta('10s'))
assert broker.transactions[exec_id].price == price
assert broker.orders[order.id].commission == 0
def test_factory_some_orders_and_no_fills(self):
matcher = mmm_Matcher()
fills = {}
MID_DATE_0 = pd.Timestamp('2013-01-07 17:00', tz='utc')
orders = {
1: {
1: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
2: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
3: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
}
}
assets = {1: a1}
all_closed, all_txns, open_orders, unused, all_minutes = mmm_factory(
matcher, fills, orders, assets)
self.assertEqual(0, len(all_closed))
self.assertEqual(0, len(all_txns))
self.assertEqual(1, len(open_orders))
a1a = matcher.env.asset_finder.retrieve_asset(sid=1)
self.assertEqual(3, len(open_orders[a1a]))
def test_blotter_never_cancel(self):
blotter = Blotter('minute', self.env.asset_finder,
cancel_policy=NeverCancel())
blotter.order(blotter.asset_finder.retrieve_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(DAY_END)
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
def test_blotter_eod_cancellation(self):
blotter = Blotter('minute', self.env.asset_finder,
cancel_policy=EODCancel())
asset_24 = blotter.asset_finder.retrieve_asset(24)
# Make two orders for the same sid, so we can test that we are not
# mutating the orders list as we are cancelling orders
blotter.order(asset_24, 100, MarketOrder())
blotter.order(asset_24, -100, MarketOrder())
self.assertEqual(len(blotter.new_orders), 2)
order_ids = [order.id for order in blotter.open_orders[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(DAY_END)
for order_id in order_ids:
order = blotter.orders[order_id]
self.assertEqual(order.status, ORDER_STATUS.CANCELLED)
def test_cancel(self):
blotter = Blotter('daily', self.env.asset_finder)
asset_24 = blotter.asset_finder.retrieve_asset(24)
asset_25 = blotter.asset_finder.retrieve_asset(25)
oid_1 = blotter.order(asset_24, 100, MarketOrder())
oid_2 = blotter.order(asset_24, 200, MarketOrder())
oid_3 = blotter.order(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(asset_25, 150, MarketOrder())
self.assertEqual(len(blotter.open_orders), 2)
self.assertEqual(len(blotter.open_orders[asset_24]), 3)
self.assertEqual(
[o.amount for o in blotter.open_orders[asset_24]],
[100, 200, 300],
)
blotter.cancel(oid_2)
self.assertEqual(len(blotter.open_orders), 2)
self.assertEqual(len(blotter.open_orders[asset_24]), 2)
self.assertEqual(
[o.amount for o in blotter.open_orders[asset_24]],
[100, 300],
)
self.assertEqual(
[o.id for o in blotter.open_orders[asset_24]],
[oid_1, oid_3],
)
blotter.cancel_all_orders_for_asset(asset_24)
self.assertEqual(len(blotter.open_orders), 1)
self.assertEqual(list(blotter.open_orders), [asset_25])
def test_slippage_and_commission_dispatching(self):
blotter = Blotter(
self.sim_params.data_frequency,
self.asset_finder,
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.sid, '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.sid, 'price') + 1.0,
)
self.assertEqual(commissions[1]['cost'], 2.0)
def test_market_order_prices(self):
"""
Basic unit tests for the MarketOrder class.
"""
style = MarketOrder()
assert_equal(style.get_limit_price(_is_buy=True), None)
assert_equal(style.get_limit_price(_is_buy=False), None)
assert_equal(style.get_stop_price(_is_buy=True), None)
assert_equal(style.get_stop_price(_is_buy=False), None)
def test_market_order_prices(self):
"""
Basic unit tests for the MarketOrder class.
"""
style = MarketOrder()
self.assertEqual(style.get_limit_price(True), None)
self.assertEqual(style.get_limit_price(False), None)
self.assertEqual(style.get_stop_price(True), None)
self.assertEqual(style.get_stop_price(False), None)
def test_prune_orders(self):
blotter = Blotter(self.sim_params.data_frequency, self.asset_finder)
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, sid=self.asset_25, amount=1)
blotter.prune_orders([other_order])
def test_factory_fills_before_orders(self):
matcher = mmm_Matcher()
# Use same sid as for assets above
# NOT Multiplying by 1000 as documented in zipline/data/minute_bars.py#L419
MID_DATE_1 = pd.Timestamp('2013-01-07 17:01', tz='utc')
MID_DATE_2 = pd.Timestamp('2013-01-07 17:03', tz='utc')
fills = {
1:
pd.DataFrame({
"close": [1, 2],
"volume": [5, 5],
"dt": [MID_DATE_1, MID_DATE_2]
}).set_index("dt"),
}
MID_DATE_0 = pd.Timestamp('2013-01-07 17:02', tz='utc')
orders = {
1: {
1: {
"dt": MID_DATE_0,
"amount": 5,
"style": MarketOrder()
},
}
}
assets = {1: a1}
all_closed, all_txns, open_orders, unused, all_minutes = mmm_factory(
matcher, fills, orders, assets)
self.assertEqual(1, len(all_closed))
self.assertEqual(1, len(all_txns))
self.assertEqual(0, len(open_orders))
txn = all_txns[0].to_dict()
self.assertEqual(
txn["price"], 1
) # this is 1 if fills are allowed to match with later orders, and 2 if not. The latter is the case ATM
a1a = matcher.env.asset_finder.retrieve_asset(sid=1)
self.assertEqual(unused, {a1a: [5]})
class BlotterTestCase(TestCase):
def setUp(self):
setup_logger(self)
def tearDown(self):
teardown_logger(self)
@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()
blotter.order(24, 100, style_obj)
result = blotter.open_orders[24][0]
self.assertEqual(result.limit, expected_lmt)
self.assertEqual(result.stop, expected_stp)
def __convert_order_params_for_blotter(limit_price, stop_price, style):
"""
Helper method for converting deprecated limit_price and stop_price
arguments into ExecutionStyle instances.
This function assumes that either style == None or (limit_price,
stop_price) == (None, None).
"""
# TODO_SS: DeprecationWarning for usage of limit_price and stop_price.
if style:
assert (limit_price, stop_price) == (None, None)
return style
if limit_price and stop_price:
return StopLimitOrder(limit_price, stop_price)
if limit_price:
return LimitOrder(limit_price)
if stop_price:
return StopOrder(stop_price)
else:
return MarketOrder()
def test_prune_orders(self):
blotter = SimulationBlotter()
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_multiple_orders(self, symbol_lookup):
with patch('zipline.gens.brokers.ib_broker.TWSConnection.connect'):
broker = IBBroker("localhost:9999:1111", account_id='TEST-123')
broker._tws.nextValidId(0)
asset = self.asset_finder.retrieve_asset(1)
symbol_lookup.return_value = asset
order_count = 0
for amount, order_style in [(-112,
StopLimitOrder(limit_price=9,
stop_price=1)),
(43, LimitOrder(limit_price=10)),
(-99, StopOrder(stop_price=8)),
(-32, MarketOrder())]:
order = broker.order(asset, amount, order_style)
order_count += 1
assert order_count == len(broker.orders)
assert broker.orders[order.id] == order
is_buy = amount > 0
assert order.stop == order_style.get_stop_price(is_buy)
assert order.limit == order_style.get_limit_price(is_buy)
def test_order_rejection(self):
blotter = SimulationBlotter(self.sim_params)
# 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 = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(self.sim_params)
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 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 range(order_count):
blotter.set_date(order_date)
blotter.order(sid, order_amount * alternator**i, MarketOrder())
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][:] # make copy
self.assertEqual(order_count, len(order_list))
for i in range(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': dt,
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'
})
for dt, ret in trading.environment.benchmark_returns.iteritems()
if dt.date() >= sim_params.period_start.date()
and dt.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_transaction(txn)
elif event.type == DATASOURCE_TYPE.BENCHMARK:
tracker.process_benchmark(event)
elif event.type == DATASOURCE_TYPE.TRADE:
tracker.process_trade(event)
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)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain sid.
oo = blotter.open_orders
self.assertNotIn(sid, oo, "Entry is removed when no open orders")
def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
tempdir = TempDirectory()
try:
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')
env = TradingEnvironment()
sid = 1
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
data_frequency="minute")
minutes = env.market_minute_window(
sim_params.first_open,
int((trade_interval.total_seconds() / 60) * trade_count) +
100)
price_data = np.array([10.1] * len(minutes))
assets = {
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(
env, env.days_in_range(minutes[0], minutes[-1]),
tempdir.path, assets)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
env,
equity_minute_reader=equity_minute_reader,
)
else:
sim_params = factory.create_simulation_parameters(
data_frequency="daily")
days = sim_params.trading_days
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")
DailyBarWriterFromDataFrames(assets).write(path, days, assets)
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
env,
equity_daily_reader=equity_daily_reader,
)
if "default_slippage" not in params or \
not params["default_slippage"]:
slippage_func = FixedSlippage()
else:
slippage_func = None
blotter = Blotter(sim_params.data_frequency, self.env.asset_finder,
slippage_func)
env.write_data(
equities_data={
sid: {
"start_date": sim_params.trading_days[0],
"end_date": sim_params.trading_days[-1]
}
})
start_date = sim_params.first_open
if alternate:
alternator = -1
else:
alternator = 1
tracker = PerformanceTracker(sim_params, self.env)
# 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(
blotter.asset_finder.retrieve_asset(sid),
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, lambda: tick,
sim_params.data_frequency)
txns, _ = blotter.get_transactions(bar_data)
for txn in txns:
tracker.process_transaction(txn)
transactions.append(txn)
for i in range(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
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)
cumulative_pos = tracker.position_tracker.positions[sid]
if total_volume == 0:
self.assertIsNone(cumulative_pos)
else:
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain sid.
oo = blotter.open_orders
self.assertNotIn(sid, oo, "Entry is removed when no open orders")
finally:
tempdir.cleanup()
def test_order(self, tradeapi, symbol_lookup):
api = tradeapi.REST()
asset = self.asset_finder.retrieve_asset(1)
symbol_lookup.return_value = asset
broker = ALPACABroker('')
amount = 10
submitted_orders = []
def submit_order(symbol, qty, side, type, time_in_force, limit_price,
stop_price, client_order_id):
o = apca.Order({
'symbol': symbol,
'qty': str(qty),
'side': side,
'type': type,
'time_in_force': time_in_force,
'limit_price': limit_price,
'stop_price': stop_price,
'client_order_id': client_order_id,
'submitted_at': '2017-06-01T10:30:00-0400',
'filled_at': None,
'filled_qty': None,
'canceled_at': None,
'failed_at': None,
})
submitted_orders.append(o)
return o
api.submit_order = submit_order
order = broker.order(asset, amount, MarketOrder())
assert order.limit is None
assert submitted_orders[-1].side == 'buy'
assert submitted_orders[-1].type == 'market'
order = broker.order(asset, -amount, LimitOrder(210.00))
assert order.limit == 210.00
assert order.amount == -amount
assert submitted_orders[-1].side == 'sell'
assert submitted_orders[-1].type == 'limit'
assert submitted_orders[-1].limit_price is not None
order = broker.order(asset, amount, StopOrder(211))
assert order.stop == 211.00
assert submitted_orders[-1].side == 'buy'
assert submitted_orders[-1].type == 'stop'
assert submitted_orders[-1].stop_price is not None
order = broker.order(asset, -amount, StopLimitOrder(210, 211))
assert order.limit == 210.00
assert order.stop == 211.00
assert submitted_orders[-1].side == 'sell'
assert submitted_orders[-1].type == 'stop_limit'
assert submitted_orders[-1].limit_price is not None
assert submitted_orders[-1].stop_price is not None
api.get_order_by_client_order_id.return_value = submitted_orders[-1]
def cancel_order(self, order_id):
assert order.id == order_id
broker.cancel_order(order.id)
def close_positions(context, data):
"""
Closes all positions.
"""
for asset, position in context.portfolio.positions.items():
algo.order(asset, -position.amount, style=MarketOrder())
class BlotterTestCase(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 make_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.trading_days,
)
yield 25, pd.DataFrame(
{
'open': [50, 50],
'high': [50, 50],
'low': [50, 50],
'close': [50, 50],
'volume': [100, 400],
},
index=cls.sim_params.trading_days,
)
@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', self.env.asset_finder)
asset_24 = blotter.asset_finder.retrieve_asset(24)
blotter.order(asset_24, 100, style_obj)
result = blotter.open_orders[asset_24][0]
self.assertEqual(result.limit, expected_lmt)
self.assertEqual(result.stop, expected_stp)
def test_cancel(self):
blotter = Blotter('daily', self.env.asset_finder)
asset_24 = blotter.asset_finder.retrieve_asset(24)
asset_25 = blotter.asset_finder.retrieve_asset(25)
oid_1 = blotter.order(asset_24, 100, MarketOrder())
oid_2 = blotter.order(asset_24, 200, MarketOrder())
oid_3 = blotter.order(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(asset_25, 150, MarketOrder())
self.assertEqual(len(blotter.open_orders), 2)
self.assertEqual(len(blotter.open_orders[asset_24]), 3)
self.assertEqual(
[o.amount for o in blotter.open_orders[asset_24]],
[100, 200, 300],
)
blotter.cancel(oid_2)
self.assertEqual(len(blotter.open_orders), 2)
self.assertEqual(len(blotter.open_orders[asset_24]), 2)
self.assertEqual(
[o.amount for o in blotter.open_orders[asset_24]],
[100, 300],
)
self.assertEqual(
[o.id for o in blotter.open_orders[asset_24]],
[oid_1, oid_3],
)
blotter.cancel_all_orders_for_asset(asset_24)
self.assertEqual(len(blotter.open_orders), 1)
self.assertEqual(list(blotter.open_orders), [asset_25])
def test_blotter_eod_cancellation(self):
blotter = Blotter('minute',
self.env.asset_finder,
cancel_policy=EODCancel())
asset_24 = blotter.asset_finder.retrieve_asset(24)
# Make two orders for the same sid, so we can test that we are not
# mutating the orders list as we are cancelling orders
blotter.order(asset_24, 100, MarketOrder())
blotter.order(asset_24, -100, MarketOrder())
self.assertEqual(len(blotter.new_orders), 2)
order_ids = [order.id for order in blotter.open_orders[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(DAY_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',
self.env.asset_finder,
cancel_policy=NeverCancel())
blotter.order(blotter.asset_finder.retrieve_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(DAY_END)
self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN)
def test_order_rejection(self):
blotter = Blotter(self.sim_params.data_frequency,
self.env.asset_finder)
asset_24 = blotter.asset_finder.retrieve_asset(24)
# 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(asset_24, 100, MarketOrder())
second_order_id = blotter.order(asset_24, 50, MarketOrder())
self.assertEqual(len(blotter.open_orders[asset_24]), 2)
open_order = blotter.open_orders[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[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,
self.env.asset_finder)
new_open_id = blotter.order(asset_24, 10, MarketOrder())
new_open_order = blotter.open_orders[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,
self.env.asset_finder)
blotter.slippage_func = FixedSlippage()
filled_id = blotter.order(asset_24, 100, MarketOrder())
filled_order = None
blotter.current_dt = self.sim_params.trading_days[-1]
bar_data = BarData(
self.data_portal,
lambda: self.sim_params.trading_days[-1],
self.sim_params.data_frequency,
)
txns, _ = blotter.get_transactions(bar_data)
for txn in txns:
filled_order = blotter.orders[txn.order_id]
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[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,
self.env.asset_finder)
# Nothing happens on held of a non-existent order
blotter.hold(56)
self.assertEqual(blotter.new_orders, [])
asset_24 = blotter.asset_finder.retrieve_asset(24)
open_id = blotter.order(asset_24, 100, MarketOrder())
open_order = blotter.open_orders[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[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[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.trading_days[0]],
[400, self.sim_params.trading_days[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_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,
self.env.asset_finder)
open_id = blotter.order(blotter.asset_finder.retrieve_asset(24),
order_size, MarketOrder())
open_order = blotter.open_orders[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 = BarData(
self.data_portal,
lambda: dt,
self.sim_params.data_frequency,
)
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_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,
self.env.asset_finder)
# Nothing happens on held of a non-existent order
blotter.hold(56)
self.assertEqual(blotter.new_orders, [])
asset_24 = blotter.asset_finder.retrieve_asset(24)
open_id = blotter.order(asset_24, 100, MarketOrder())
open_order = blotter.open_orders[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[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[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.trading_days[0]],
[400, self.sim_params.trading_days[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_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,
self.env.asset_finder)
open_id = blotter.order(blotter.asset_finder.retrieve_asset(24),
order_size, MarketOrder())
open_order = blotter.open_orders[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 = BarData(
self.data_portal,
lambda: dt,
self.sim_params.data_frequency,
)
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_factory_orders_with_validity(self):
matcher = mmm_Matcher()
# Use same sid as for assets above
# NOT Multiplying by 1000 as documented in zipline/data/minute_bars.py#L419
MID_DATE_1 = pd.Timestamp('2013-01-07 17:01', tz='utc')
MID_DATE_2 = pd.Timestamp('2013-01-07 17:02', tz='utc')
MID_DATE_3 = pd.Timestamp('2013-01-07 17:03', tz='utc')
# seconds in below timestamp will be floored
MID_DATE_4 = pd.Timestamp('2017-02-13 05:13:23', tz='utc')
fills = {
1:
pd.DataFrame({
"close": [3.5, 4.5, 4, 2.4],
"volume": [10, 5, 7, 1],
"dt": [MID_DATE_1, MID_DATE_2, MID_DATE_3, MID_DATE_4]
}).set_index("dt"),
2:
pd.DataFrame({
"close": [3.5],
"volume": [1],
"dt": [MID_DATE_4]
}).set_index("dt")
}
#print("data: %s" % (fills))
MID_DATE_0 = pd.Timestamp('2013-01-07 17:00', tz='utc')
orders = {
1: {
1: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder(),
"validity": None
},
2: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder(),
"validity": {
"type": ORDER_VALIDITY.GTC,
"date": None
}
},
3: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder(),
"validity": {
"type": ORDER_VALIDITY.GTD,
"date": MID_DATE_3
}
},
},
2: {
4: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder(),
"validity": {
"type": ORDER_VALIDITY.DAY,
"date": None
}
},
}
}
assets = {1: a1, 2: a2}
all_closed, all_txns, open_orders, unused, all_minutes = mmm_factory(
matcher, fills, orders, assets)
self.assertEqual(2, len(all_closed))
self.assertEqual(4, len(all_txns))
self.assertEqual(0, len(open_orders))
# remaining order on asset 1 gets filled on MID_DATE_3 but not on MID_DATE_4 due to validity
sub = [txn['amount'] for txn in all_txns if txn['order_id'] == 3]
self.assertEqual(1, len(sub))
self.assertEqual(2, sub[0])
# remaining order on asset 2 does not get filled at all due to validity
sub = [txn['amount'] for txn in all_txns if txn['order_id'] == 4]
self.assertEqual(0, len(sub))
def test_factory_some_orders_and_some_fills(self):
matcher = mmm_Matcher()
# Use same sid as for assets above
# NOT Multiplying by 1000 as documented in zipline/data/minute_bars.py#L419
MID_DATE_1 = pd.Timestamp('2013-01-07 17:01', tz='utc')
MID_DATE_2 = pd.Timestamp('2013-01-07 17:02', tz='utc')
MID_DATE_3 = pd.Timestamp('2013-01-07 17:03', tz='utc')
# seconds in below timestamp will be floored
MID_DATE_4 = pd.Timestamp('2017-02-13 05:13:23', tz='utc')
fills = {
1:
pd.DataFrame({
"close": [3.5, 4.5, 4, 2.4],
"volume": [10, 5, 7, 1],
"dt": [MID_DATE_1, MID_DATE_2, MID_DATE_3, MID_DATE_4]
}).set_index("dt"),
2:
pd.DataFrame({
"close": [3.5],
"volume": [1],
"dt": [MID_DATE_4]
}).set_index("dt")
}
#print("data: %s" % (fills))
MID_DATE_0 = pd.Timestamp('2013-01-07 17:00', tz='utc')
orders = {
1: {
1: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
2: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
3: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
},
2: {
4: {
"dt": MID_DATE_0,
"amount": 10,
"style": MarketOrder()
},
}
}
assets = {1: a1, 2: a2}
all_closed, all_txns, open_orders, unused, all_minutes = mmm_factory(
matcher, fills, orders, assets)
self.assertEqual(2, len(all_closed))
self.assertEqual(6, len(all_txns))
self.assertEqual(2, len(open_orders))
a1a = matcher.env.asset_finder.retrieve_asset(sid=1)
self.assertEqual(1, len(open_orders[a1a]))
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 = SimulationBlotter(self.sim_params,
equity_slippage=VolumeShareSlippage())
# 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 = SimulationBlotter(self.sim_params,
equity_slippage=VolumeShareSlippage())
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)
class BlotterTestCase(WithCreateBarData, 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, country_code, sids):
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',
'real_sid': '1000',
'currency': 'USD',
'start_date': cls.START_DATE,
'end_date': cls.END_DATE,
'expiration_date': cls.END_DATE,
'auto_close_date': cls.END_DATE,
'exchange': 'CMES',
},
},
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):
style_obj.asset = self.asset_24
blotter = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(self.sim_params, 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 = SimulationBlotter(self.sim_params,
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 = SimulationBlotter(self.sim_params)
# 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 = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(self.sim_params,
equity_slippage=VolumeShareSlippage())
# 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 = SimulationBlotter(self.sim_params,
equity_slippage=VolumeShareSlippage())
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 = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(self.sim_params)
blotter2 = SimulationBlotter(self.sim_params)
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 = SimulationBlotter(
self.sim_params,
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)
def test_filterBySign(self):
MID_DATE_1 = pd.Timestamp('2013-01-07 17:01', tz='utc')
fills_all = {
1:
pd.DataFrame({
"close": [3.5, 3.5],
"volume": [-3, 4],
"dt": [MID_DATE_1, MID_DATE_1]
}).set_index("dt"),
}
MID_DATE_0 = pd.Timestamp('2013-01-07 17:00', tz='utc')
orders_all = {
1: {
1: {
"dt": MID_DATE_0,
"amount": -10,
"style": MarketOrder()
},
2: {
"dt": MID_DATE_0,
"amount": 15,
"style": MarketOrder()
},
},
}
act_fills_pos, act_orders_pos = mmm_Matcher.filterBySign(
+1, fills_all, orders_all)
act_fills_neg, act_orders_neg = mmm_Matcher.filterBySign(
-1, fills_all, orders_all)
exp_fills_pos = {
1:
pd.DataFrame({
"close": [3.5],
"volume": [4],
"dt": [MID_DATE_1]
}).set_index("dt"),
}
exp_fills_neg = {
1:
pd.DataFrame({
"close": [3.5],
"volume": [-3],
"dt": [MID_DATE_1]
}).set_index("dt"),
}
exp_orders_pos = {
1: {
2: {
"dt": MID_DATE_0,
"amount": 15,
"style": orders_all[1][2]['style']
},
},
}
exp_orders_neg = {
1: {
1: {
"dt": MID_DATE_0,
"amount": -10,
"style": orders_all[1][1]['style']
},
},
}
self.assertEqual(act_fills_pos[1].equals(exp_fills_pos[1]), True)
self.assertEqual(act_fills_neg[1].equals(exp_fills_neg[1]), True)
self.assertEqual(act_orders_pos, exp_orders_pos)
self.assertEqual(act_orders_neg, exp_orders_neg)
