def test_prune_orders(self):
blotter = Blotter(self.sim_params.data_frequency,
self.env.asset_finder)
asset_24 = blotter.asset_finder.retrieve_asset(24)
asset_25 = blotter.asset_finder.retrieve_asset(25)
blotter.order(asset_24, 100, MarketOrder())
open_order = blotter.open_orders[asset_24][0]
blotter.prune_orders([])
self.assertEqual(1, len(blotter.open_orders[asset_24]))
blotter.prune_orders([open_order])
self.assertEqual(0, len(blotter.open_orders[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=asset_25,
amount=1
)
blotter.prune_orders([other_order])
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_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 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
metadata = make_simple_equity_info([sid], self.start, self.end)
with TempDirectory() as tempdir, tmp_trading_env(equities=metadata) as env:
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
start=self.start, end=self.end, 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, iteritems(assets)
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
env,
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.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")
BcolzDailyBarWriter(path, days).write(assets.items())
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
env,
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 = FixedSlippage()
else:
slippage_func = None
blotter = Blotter(sim_params.data_frequency, self.env.asset_finder, slippage_func)
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, _, 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.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")
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_rejection(self):
blotter = Blotter(self.sim_params.data_frequency,
self.asset_finder)
# 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,
self.asset_finder)
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,
self.asset_finder)
blotter.slippage_func = 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
metadata = make_simple_equity_info([sid], self.start, self.end)
with TempDirectory() as tempdir, \
tmp_trading_env(equities=metadata) as env:
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 = {
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(
env.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(
env.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 = FixedSlippage()
else:
slippage_func = None
blotter = Blotter(sim_params.data_frequency, self.env.asset_finder,
slippage_func)
start_date = sim_params.first_open
if alternate:
alternator = -1
else:
alternator = 1
tracker = PerformanceTracker(sim_params, self.trading_calendar,
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, _, 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.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")
