def test_volume_limit(self, name,
first_order_amount,
second_order_amount,
first_order_fill_amount,
second_order_fill_amount):
slippage_model = FixedBasisPointsSlippage(basis_points=5,
volume_limit=0.1)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=order_amount,
filled=0,
asset=self.ASSET133
)
for order_amount in [first_order_amount, second_order_amount]
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_minute,
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 2)
_, first_txn = orders_txns[0]
_, second_txn = orders_txns[1]
self.assertEquals(first_txn['amount'], first_order_fill_amount)
self.assertEquals(second_txn['amount'], second_order_fill_amount)
def test_orders_stop(self, name, order_data, event_data, expected):
data = order_data
data['asset'] = self.ASSET133
order = Order(**data)
if expected['transaction']:
expected['transaction']['asset'] = self.ASSET133
event_data['asset'] = self.ASSET133
assets = (
(133, pd.DataFrame(
{
'open': [event_data['open']],
'high': [event_data['high']],
'low': [event_data['low']],
'close': [event_data['close']],
'volume': [event_data['volume']],
},
index=[pd.Timestamp('2006-01-05 14:31', tz='UTC')],
)),
)
days = pd.date_range(
start=normalize_date(self.minutes[0]),
end=normalize_date(self.minutes[-1])
)
with tmp_bcolz_equity_minute_bar_reader(
self.trading_calendar, days, assets) as reader:
data_portal = DataPortal(
self.asset_finder, self.trading_calendar,
first_trading_day=reader.first_trading_day,
equity_minute_reader=reader,
)
slippage_model = VolumeShareSlippage()
try:
dt = pd.Timestamp('2006-01-05 14:31', tz='UTC')
bar_data = BarData(
data_portal,
lambda: dt,
self.sim_params.data_frequency,
self.trading_calendar,
NoRestrictions(),
)
_, txn = next(slippage_model.simulate(
bar_data,
self.ASSET133,
[order],
))
except StopIteration:
txn = None
if expected['transaction'] is None:
self.assertIsNone(txn)
else:
self.assertIsNotNone(txn)
for key, value in expected['transaction'].items():
self.assertEquals(value, txn[key])
def generate_order_and_txns(self, sid, order_amount, fill_amounts):
asset1 = self.asset_finder.retrieve_asset(sid)
# one order
order = Order(dt=None, asset=asset1, amount=order_amount)
# three fills
txn1 = Transaction(asset=asset1,
amount=fill_amounts[0],
dt=None,
price=100,
order_id=order.id)
txn2 = Transaction(asset=asset1,
amount=fill_amounts[1],
dt=None,
price=101,
order_id=order.id)
txn3 = Transaction(asset=asset1,
amount=fill_amounts[2],
dt=None,
price=102,
order_id=order.id)
return order, [txn1, txn2, txn3]
def order(self, sid, amount, style, order_id=None):
"""Place an order.
Parameters
----------
asset : zipline.assets.Asset
The asset that this order is for.
amount : int
The amount of shares to order. If ``amount`` is positive, this is
the number of shares to buy or cover. If ``amount`` is negative,
this is the number of shares to sell or short.
style : zipline.finance.execution.ExecutionStyle
The execution style for the order.
order_id : str, optional
The unique identifier for this order.
Returns
-------
order_id : str or None
The unique identifier for this order, or None if no order was
placed.
Notes
-----
amount > 0 :: Buy/Cover
amount < 0 :: Sell/Short
Market order: order(sid, amount)
Limit order: order(sid, amount, style=LimitOrder(limit_price))
Stop order: order(sid, amount, style=StopOrder(stop_price))
StopLimit order: order(sid, amount, style=StopLimitOrder(limit_price,
stop_price))
"""
# something could be done with amount to further divide
# between buy by share count OR buy shares up to a dollar amount
# numeric == share count AND "$dollar.cents" == cost amount
if amount == 0:
# Don't bother placing orders for 0 shares.
return None
elif amount > self.max_shares:
# Arbitrary limit of 100 billion (US) shares will never be
# exceeded except by a buggy algorithm.
raise OverflowError("Can't order more than %d shares" %
self.max_shares)
is_buy = (amount > 0)
order = Order(dt=self.current_dt,
sid=sid,
amount=amount,
stop=style.get_stop_price(is_buy),
limit=style.get_limit_price(is_buy),
id=order_id)
self.open_orders[order.sid].append(order)
self.orders[order.id] = order
self.new_orders.append(order)
return order.id
def test_fill_zero_shares(self):
slippage_model = FixedBasisPointsSlippage(basis_points=5,
volume_limit=0.1)
# since the volume limit for the bar is 20, the first order will be
# filled and there will be a transaction for it, and the second order
# will order zero shares so there should not be a transaction for it.
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=20,
filled=0,
asset=self.ASSET133,
)
] * 2
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_minute)
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert 1 == len(orders_txns)
# ordering zero shares should result in zero transactions
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=0,
filled=0,
asset=self.ASSET133,
)
]
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert 0 == len(orders_txns)
def order(self, asset, amount, limit_price, stop_price, style):
is_buy = (amount > 0)
zp_order = ZPOrder(
dt=pd.to_datetime('now', utc=True),
asset=asset,
amount=amount,
stop=style.get_stop_price(is_buy),
limit=style.get_limit_price(is_buy))
contract = Contract()
contract.m_symbol = str(asset.symbol)
contract.m_currency = self.currency
contract.m_exchange = 'SMART'
contract.m_secType = 'STK'
order = Order()
order.m_totalQuantity = int(fabs(amount))
order.m_action = "BUY" if amount > 0 else "SELL"
order.m_lmtPrice = 0
order.m_auxPrice = 0
if isinstance(style, MarketOrder):
order.m_orderType = "MKT"
elif isinstance(style, LimitOrder):
order.m_orderType = "LMT"
order.m_lmtPrice = limit_price
elif isinstance(style, StopOrder):
order.m_orderType = "STP"
order.m_auxPrice = stop_price
elif isinstance(style, StopLimitOrder):
order.m_orderType = "STP LMT"
order.m_auxPrice = stop_price
order.m_lmtPrice = limit_price
order.m_tif = "DAY"
ib_order_id = self._tws.next_order_id
zp_order.broker_order_id = ib_order_id
self.orders[zp_order.id] = zp_order
self._tws.placeOrder(ib_order_id, contract, order)
return zp_order.id
def order(
self,
sid,
amount,
style,
commission,
order_id=None,
give_price=None,
):
# something could be done with amount to further divide
# between buy by share count OR buy shares up to a dollar amount
# numeric == share count AND "$dollar.cents" == cost amount
# sid 为stock_id 股票代码，他么的。
"""
amount > 0 :: Buy/Cover
amount < 0 :: Sell/Short
Market order: order(sid, amount)
Limit order: order(sid, amount, style=LimitOrder(limit_price))
Stop order: order(sid, amount, style=StopOrder(stop_price))
StopLimit order: order(sid, amount, style=StopLimitOrder(limit_price,
stop_price))
"""
if amount == 0:
# Don't bother placing orders for 0 shares.
return
elif amount > self.max_shares:
# Arbitrary limit of 100 billion (US) shares will never be
# exceeded except by a buggy algorithm.
raise OverflowError("Can't order more than %d shares" %
self.max_shares)
'''
elif amount%100 !=0:
original=amount
n=amount%100
amount=amount-n
print (u"%s:买入股票数目应该为100倍数, 从 %d 调整为 %d"%(self.current_dt,original,amount))
'''
is_buy = (amount > 0)
order = Order(dt=self.current_dt,
sid=sid,
amount=amount,
give_price=give_price,
stop=style.get_stop_price(is_buy),
limit=style.get_limit_price(is_buy),
id=order_id)
#print u'这里的情况啊啊啊啊啊'
self.open_orders[order.sid].append(order)
self.orders[order.id] = order
self.new_orders.append(order)
return order.id
def tdx_order_to_zipline_order(self, order):
"""
status
0 已报
1 部分成交
2 全部成交
3 部分撤单
4 全部撤单
5 交易所拒单
6 柜台未接受
:param order:
:return:
"""
if order.status == "3" or '4' == order.status:
zp_status = ZP_ORDER_STATUS.CANCELLED
elif order.status == "0":
zp_status = ZP_ORDER_STATUS.OPEN
elif order.status == "1":
zp_status = ZP_ORDER_STATUS.FILLED
elif order.status == "2":
zp_status = ZP_ORDER_STATUS.HELD
elif order.status == "5" or order.status == "6":
zp_status = ZP_ORDER_STATUS.REJECTED
zp_order_id = self._tdx_to_zp_order_id(order.order_id)
od = ZPOrder(
dt=order.dt,
asset=symbol(order.symbol),
amount=order.amount,
filled=order.filled,
stop=None,
limit=order.price, # TODO 市价单和限价单
id=zp_order_id,
)
od.broker_order_id = order.order_id
od.status = zp_status
return od
def test_calculate_impact_sell(self):
answer_key = [
# We ordered -10 contracts, but are capped at -(100 * 0.05) = -5
(91485.499914831875, -5),
(91486.499914830943, -5),
(None, None),
]
order = Order(
dt=pd.Timestamp.now(tz='utc').round('min'),
asset=self.ASSET,
amount=-10,
)
self._calculate_impact(order, answer_key)
def test_calculate_impact_buy(self):
answer_key = [
# We ordered 10 contracts, but are capped at 100 * 0.05 = 5
(91485.500085168125, 5),
(91486.500085169057, 5),
(None, None),
]
order = Order(
dt=pd.Timestamp.now(tz='utc').round('min'),
asset=self.ASSET,
amount=10,
)
self._calculate_impact(order, answer_key)
def test_low_transaction_volume(self):
# With a volume limit of 0.001, and a bar volume of 100, we should
# compute a transaction volume of 100 * 0.001 = 0.1, which gets rounded
# down to zero. In this case we expect no amount to be transacted.
model = VolatilityVolumeShare(volume_limit=0.001)
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=self.ASSET, amount=10)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def tdx_order_to_zipline_order(self, order):
if order.status is not None and 'CANCEL' == order.status:
zp_status = ZP_ORDER_STATUS.CANCELLED
elif order.filled == 0:
zp_status = ZP_ORDER_STATUS.OPEN
else:
zp_status = ZP_ORDER_STATUS.FILLED
zp_order_id = self._tdx_to_zp_order_id(order.order_id)
od = ZPOrder(
dt=pd.to_datetime(order.dt),
asset=symbol(order.symbol),
amount=order.amount,
filled=order.filled,
stop=None,
limit=order.price, # TODO 市价单和限价单
id=zp_order_id,
)
od.broker_order_id = order.order_id
od.status = zp_status
return od
def order(self,
sid,
amount,
style,
order_id=None,
base_price=None,
open_price=None,
close_price=None,
when=None,
current_volume=None):
# something could be done with amount to further divide
# between buy by share count OR buy shares up to a dollar amount
# numeric == share count AND "$dollar.cents" == cost amount
"""
amount > 0 :: Buy/Cover
amount < 0 :: Sell/Short
Market order: order(sid, amount)
Limit order: order(sid, amount, style=LimitOrder(limit_price))
Stop order: order(sid, amount, style=StopOrder(stop_price))
StopLimit order: order(sid, amount, style=StopLimitOrder(limit_price,
stop_price))
"""
if amount == 0:
# Don't bother placing orders for 0 shares.
return
elif amount > self.max_shares:
# Arbitrary limit of 100 billion (US) shares will never be
# exceeded except by a buggy algorithm.
raise OverflowError("Can't order more than %d shares" %
self.max_shares)
is_buy = (amount > 0)
order = Order(dt=self.current_dt,
sid=sid,
amount=amount,
stop=style.get_stop_price(is_buy),
limit=style.get_limit_price(is_buy),
id=order_id,
base_price=base_price,
close_price=close_price,
open_price=open_price,
when=when,
current_volume=current_volume)
self.open_orders[order.sid].append(order)
self.orders[order.id] = order
self.new_orders.append(order)
return order.id
def tdx_order_to_zipline_order(self, order):
if order.status == '已撤':
zp_status = ZP_ORDER_STATUS.CANCELLED
elif order.filled == 0:
zp_status = ZP_ORDER_STATUS.OPEN
else:
zp_status = ZP_ORDER_STATUS.FILLED
zp_order_id = self._tdx_to_zp_order_id(order.order_id)
od = ZPOrder(
dt=pd.to_datetime(order.dt),
asset=symbol(order.symbol),
amount=order.amount,
filled=order.filled,
stop=None,
limit=order.price, # TODO 市价单和限价单
id=zp_order_id,
)
od.broker_order_id = order.order_id
od.status = zp_status
return od
def test_impacted_price_worse_than_limit(self):
model = VolatilityVolumeShare(volume_limit=0.05)
# Use all the same numbers from the 'calculate_impact' tests. Since the
# impacted price is 59805.5, which is worse than the limit price of
# 59800, the model should return None.
minute = pd.Timestamp('2006-03-01 11:35AM', tz='UTC')
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(
dt=data.current_dt, asset=self.ASSET, amount=10, limit=59800,
)
price, amount = model.process_order(data, order)
self.assertIsNone(price)
self.assertIsNone(amount)
def generate_order_and_txns(self):
asset1 = self.asset_finder.retrieve_asset(1)
# one order
order = Order(dt=None, sid=asset1, amount=500)
# three fills
txn1 = Transaction(sid=asset1, amount=230, dt=None,
price=100, order_id=order.id)
txn2 = Transaction(sid=asset1, amount=170, dt=None,
price=101, order_id=order.id)
txn3 = Transaction(sid=asset1, amount=100, dt=None,
price=102, order_id=order.id)
return order, [txn1, txn2, txn3]
def test_fixed_bps_slippage(
self,
name,
basis_points,
volume_limit,
order_amount,
expected_price,
expected_amount,
):
slippage_model = FixedBasisPointsSlippage(basis_points=basis_points,
volume_limit=volume_limit)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=order_amount,
filled=0,
asset=self.ASSET133,
)
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_minute)
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 1
_, txn = orders_txns[0]
expected_txn = {
"price": expected_price,
"dt": datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
"amount": expected_amount,
"asset": self.ASSET133,
"type": DATASOURCE_TYPE.TRANSACTION,
"order_id": open_orders[0].id,
}
assert txn is not None
assert expected_txn == txn.__dict__
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_fixed_bps_slippage(self,
name,
basis_points,
volume_limit,
order_amount,
expected_price,
expected_amount):
slippage_model = FixedBasisPointsSlippage(basis_points=basis_points,
volume_limit=volume_limit)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=order_amount,
filled=0,
asset=self.ASSET133
)
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.first_minute
)
orders_txns = list(slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': expected_price,
'dt': datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': expected_amount,
'asset': self.ASSET133,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
self.assertEquals(expected_txn, txn.__dict__)
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_volume_share_slippage_with_future(self):
slippage_model = VolumeShareSlippage(volume_limit=1, price_impact=0.3)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=10,
filled=0,
asset=self.ASSET1000,
),
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0],
)
orders_txns = list(
slippage_model.simulate(bar_data, self.ASSET1000, open_orders)
)
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
# We expect to fill the order for all 10 contracts. The volume for the
# futures contract in this bar is 100, so our volume share is:
# 10.0 / 100 = 0.1
# The current price is 5.0 and the price impact is 0.3, so the expected
# impacted price is:
# 5.0 + (5.0 * (0.1 ** 2) * 0.3) = 5.015
expected_txn = {
'price': 5.015,
'dt': datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': 10,
'asset': self.ASSET1000,
'commission': None,
'type': DATASOURCE_TYPE.TRANSACTION,
'order_id': open_orders[0].id,
}
self.assertIsNotNone(txn)
self.assertEquals(expected_txn, txn.__dict__)
def _calculate_impact(self, test_order, answer_key):
model = VolatilityVolumeShare(volume_limit=0.05)
first_minute = pd.Timestamp('2006-03-31 11:35AM', tz='UTC')
next_3_minutes = self.trading_calendar.minutes_window(first_minute, 3)
remaining_shares = test_order.open_amount
for i, minute in enumerate(next_3_minutes):
data = self.create_bardata(simulation_dt_func=lambda: minute)
new_order = Order(
dt=data.current_dt, asset=self.ASSET, amount=remaining_shares,
)
price, amount = model.process_order(data, new_order)
self.assertEqual(price, answer_key[i][0])
self.assertEqual(amount, answer_key[i][1])
amount = amount or 0
if remaining_shares < 0:
remaining_shares = min(0, remaining_shares - amount)
else:
remaining_shares = max(0, remaining_shares - amount)
def test_calculate_impact_without_history(self):
model = VolatilityVolumeShare(volume_limit=1)
minutes = [
# Start day of the futures contract; no history yet.
pd.Timestamp('2006-02-10 11:35AM', tz='UTC'),
# Only a week's worth of history data.
pd.Timestamp('2006-02-17 11:35AM', tz='UTC'),
]
for minute in minutes:
data = self.create_bardata(simulation_dt_func=lambda: minute)
order = Order(dt=data.current_dt, asset=self.ASSET, amount=10)
price, amount = model.process_order(data, order)
avg_price = (data.current(self.ASSET, 'high') +
data.current(self.ASSET, 'low')) / 2
expected_price = \
avg_price + (avg_price * NO_DATA_VOLATILITY_SLIPPAGE_IMPACT)
self.assertEqual(price, expected_price)
self.assertEqual(amount, 10)
def test_volume_share_slippage_with_future(self):
slippage_model = VolumeShareSlippage(volume_limit=1, price_impact=0.3)
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=10,
filled=0,
asset=self.ASSET1000,
),
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(bar_data, self.ASSET1000, open_orders))
assert len(orders_txns) == 1
_, txn = orders_txns[0]
# We expect to fill the order for all 10 contracts. The volume for the
# futures contract in this bar is 100, so our volume share is:
# 10.0 / 100 = 0.1
# The current price is 5.0 and the price impact is 0.3, so the expected
# impacted price is:
# 5.0 + (5.0 * (0.1 ** 2) * 0.3) = 5.015
expected_txn = {
"price": 5.015,
"dt": datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
"amount": 10,
"asset": self.ASSET1000,
"type": DATASOURCE_TYPE.TRANSACTION,
"order_id": open_orders[0].id,
}
assert txn is not None
assert expected_txn == txn.__dict__
def _order2zp(self, order):
zp_order = ZPOrder(
id=order.client_order_id,
asset=symbol_lookup(order.symbol),
amount=int(order.qty) if order.side == 'buy' else -int(order.qty),
stop=float(order.stop_price) if order.stop_price else None,
limit=float(order.limit_price) if order.limit_price else None,
dt=order.submitted_at,
commission=0,
)
zp_order.status = ZP_ORDER_STATUS.OPEN
if order.canceled_at:
zp_order.status = ZP_ORDER_STATUS.CANCELLED
if order.failed_at:
zp_order.status = ZP_ORDER_STATUS.REJECTED
if order.filled_at:
zp_order.status = ZP_ORDER_STATUS.FILLED
zp_order.filled = int(order.filled_qty)
return zp_order
def make_order(self, amount):
return Order(
self.minute,
self.asset,
amount,
)
def test_orders_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
# long, does not trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": 100,
"filled": 0,
"asset": self.ASSET133,
"limit": 3.5,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# long, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": 100,
"filled": 0,
"asset": self.ASSET133,
"limit": 3.5,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# long, does trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": 100,
"filled": 0,
"asset": self.ASSET133,
"limit": 3.6,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 1
txn = orders_txns[0][1]
expected_txn = {
"price": float(3.50021875),
"dt": datetime.datetime(2006, 1, 5, 14, 34, tzinfo=pytz.utc),
# we ordered 100 shares, but default volume slippage only allows
# for 2.5% of the volume. 2.5% * 2000 = 50 shares
"amount": int(50),
"asset": self.ASSET133,
"order_id": open_orders[0].id,
}
assert txn is not None
for key, value in expected_txn.items():
assert value == txn[key]
# short, does not trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": -100,
"filled": 0,
"asset": self.ASSET133,
"limit": 3.5,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# short, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": -100,
"filled": 0,
"asset": self.ASSET133,
"limit": 3.5,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# short, does trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": -100,
"filled": 0,
"asset": self.ASSET133,
"limit": 3.4,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 1
_, txn = orders_txns[0]
expected_txn = {
"price": float(3.49978125),
"dt": datetime.datetime(2006, 1, 5, 14, 32, tzinfo=pytz.utc),
"amount": int(-50),
"asset": self.ASSET133,
}
assert txn is not None
for key, value in expected_txn.items():
assert value == txn[key]
def test_orders_stop_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
# long, does not trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": 100,
"filled": 0,
"asset": self.ASSET133,
"stop": 4.0,
"limit": 3.0,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# long, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": 100,
"filled": 0,
"asset": self.ASSET133,
"stop": 4.0,
"limit": 3.5,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# long, does trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": 100,
"filled": 0,
"asset": self.ASSET133,
"stop": 4.0,
"limit": 3.6,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 1
_, txn = orders_txns[0]
expected_txn = {
"price": float(3.50021875),
"dt": datetime.datetime(2006, 1, 5, 14, 34, tzinfo=pytz.utc),
"amount": int(50),
"asset": self.ASSET133,
}
for key, value in expected_txn.items():
assert value == txn[key]
# short, does not trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": -100,
"filled": 0,
"asset": self.ASSET133,
"stop": 3.0,
"limit": 4.0,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# short, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": -100,
"filled": 0,
"asset": self.ASSET133,
"stop": 3.0,
"limit": 3.5,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
# short, does trade
open_orders = [
Order(
**{
"dt": datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
"amount": -100,
"filled": 0,
"asset": self.ASSET133,
"stop": 3.0,
"limit": 3.4,
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 1
_, txn = orders_txns[0]
expected_txn = {
"price": float(3.49978125),
"dt": datetime.datetime(2006, 1, 5, 14, 32, tzinfo=pytz.utc),
"amount": int(-50),
"asset": self.ASSET133,
}
for key, value in expected_txn.items():
assert value == txn[key]
def test_volume_share_slippage(self):
slippage_model = VolumeShareSlippage()
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=100,
filled=0,
asset=self.ASSET133,
)
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 1
_, txn = orders_txns[0]
expected_txn = {
"price": float(3.0001875),
"dt": datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
"amount": int(5),
"asset": self.ASSET133,
"type": DATASOURCE_TYPE.TRANSACTION,
"order_id": open_orders[0].id,
}
assert txn is not None
# TODO: Make expected_txn an Transaction object and ensure there
# is a __eq__ for that class.
assert expected_txn == txn.__dict__
open_orders = [
Order(
dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
amount=100,
filled=0,
asset=self.ASSET133,
)
]
# Set bar_data to be a minute ahead of last trade.
# Volume share slippage should not execute when there is no trade.
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
assert len(orders_txns) == 0
def test_orders_stop_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
# long, does not trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'asset': self.ASSET133,
'stop': 4.0,
'limit': 3.0
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'asset': self.ASSET133,
'stop': 4.0,
'limit': 3.5
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'asset': self.ASSET133,
'stop': 4.0,
'limit': 3.6
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[2], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.50021875),
'dt': datetime.datetime(2006, 1, 5, 14, 34, tzinfo=pytz.utc),
'amount': int(50),
'asset': self.ASSET133
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
# short, does not trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'stop': 3.0,
'limit': 4.0
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'stop': 3.0,
'limit': 3.5
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'stop': 3.0,
'limit': 3.4
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.49978125),
'dt': datetime.datetime(2006, 1, 5, 14, 32, tzinfo=pytz.utc),
'amount': int(-50),
'asset': self.ASSET133,
}
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
def test_orders_limit(self):
slippage_model = VolumeShareSlippage()
slippage_model.data_portal = self.data_portal
# long, does not trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.5
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.5
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# long, does trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.6
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[3], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
txn = orders_txns[0][1]
expected_txn = {
'price': float(3.50021875),
'dt': datetime.datetime(2006, 1, 5, 14, 34, tzinfo=pytz.utc),
# we ordered 100 shares, but default volume slippage only allows
# for 2.5% of the volume. 2.5% * 2000 = 50 shares
'amount': int(50),
'asset': self.ASSET133,
'order_id': open_orders[0].id
}
self.assertIsNotNone(txn)
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
# short, does not trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.5
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does not trade - impacted price worse than limit price
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.5
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[0], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 0)
# short, does trade
open_orders = [
Order(
**{
'dt': datetime.datetime(
2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': -100,
'filled': 0,
'asset': self.ASSET133,
'limit': 3.4
})
]
bar_data = self.create_bardata(
simulation_dt_func=lambda: self.minutes[1], )
orders_txns = list(
slippage_model.simulate(
bar_data,
self.ASSET133,
open_orders,
))
self.assertEquals(len(orders_txns), 1)
_, txn = orders_txns[0]
expected_txn = {
'price': float(3.49978125),
'dt': datetime.datetime(2006, 1, 5, 14, 32, tzinfo=pytz.utc),
'amount': int(-50),
'asset': self.ASSET133,
}
self.assertIsNotNone(txn)
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
