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, )) self.assertEquals(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, 'commission': None, 'type': DATASOURCE_TYPE.TRANSACTION, 'order_id': open_orders[0].id } self.assertIsNotNone(txn) # TODO: Make expected_txn an Transaction object and ensure there # is a __eq__ for that class. self.assertEquals(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, )) self.assertEquals(len(orders_txns), 0)
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.env.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 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.env.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 __init__(self, data_frequency, equity_slippage=None, future_slippage=None, equity_commission=None, future_commission=None, cancel_policy=None): # these orders are aggregated by asset self.open_orders = defaultdict(list) # keep a dict of orders by their own id self.orders = {} # holding orders that have come in since the last event. self.new_orders = [] self.current_dt = None self.max_shares = int(1e+11) self.slippage_models = { Equity: equity_slippage or VolumeShareSlippage(), Future: future_slippage or VolatilityVolumeShare( volume_limit=DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT, ), } self.commission_models = { Equity: equity_commission or PerShare(), Future: future_commission or PerContract( cost=DEFAULT_PER_CONTRACT_COST, exchange_fee=FUTURE_EXCHANGE_FEES_BY_SYMBOL, ), } self.data_frequency = data_frequency self.cancel_policy = cancel_policy if cancel_policy else NeverCancel()
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 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 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, )) self.assertEquals(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, 'commission': None, 'type': DATASOURCE_TYPE.TRANSACTION, 'order_id': open_orders[0].id } self.assertIsNotNone(txn) # TODO: Make expected_txn an Transaction object and ensure there # is a __eq__ for that class. self.assertEquals(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, )) self.assertEquals(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])
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])