def test_cancel(self): blotter = Blotter('daily') oid_1 = blotter.order(self.asset_24, 100, MarketOrder()) oid_2 = blotter.order(self.asset_24, 200, MarketOrder()) oid_3 = blotter.order(self.asset_24, 300, MarketOrder()) # Create an order for another asset to verify that we don't remove it # when we do cancel_all on 24. blotter.order(self.asset_25, 150, MarketOrder()) self.assertEqual(len(blotter.open_orders), 2) self.assertEqual(len(blotter.open_orders[self.asset_24]), 3) self.assertEqual( [o.amount for o in blotter.open_orders[self.asset_24]], [100, 200, 300], ) blotter.cancel(oid_2) self.assertEqual(len(blotter.open_orders), 2) self.assertEqual(len(blotter.open_orders[self.asset_24]), 2) self.assertEqual( [o.amount for o in blotter.open_orders[self.asset_24]], [100, 300], ) self.assertEqual( [o.id for o in blotter.open_orders[self.asset_24]], [oid_1, oid_3], ) blotter.cancel_all_orders_for_asset(self.asset_24) self.assertEqual(len(blotter.open_orders), 1) self.assertEqual(list(blotter.open_orders), [self.asset_25])
def test_blotter_processes_splits(self): blotter = Blotter('daily', equity_slippage=FixedSlippage()) # set up two open limit orders with very low limit prices, # one for sid 1 and one for sid 2 asset1 = self.asset_finder.retrieve_asset(1) asset2 = self.asset_finder.retrieve_asset(2) asset133 = self.asset_finder.retrieve_asset(133) blotter.order(asset1, 100, LimitOrder(10)) blotter.order(asset2, 100, LimitOrder(10)) # send in splits for assets 133 and 2. We have no open orders for # asset 133 so it should be ignored. blotter.process_splits([(asset133, 0.5), (asset2, 0.3333)]) for asset in [asset1, asset2]: order_lists = blotter.open_orders[asset] self.assertIsNotNone(order_lists) self.assertEqual(1, len(order_lists)) asset1_order = blotter.open_orders[1][0] asset2_order = blotter.open_orders[2][0] # make sure the asset1 order didn't change self.assertEqual(100, asset1_order.amount) self.assertEqual(10, asset1_order.limit) self.assertEqual(1, asset1_order.asset) # make sure the asset2 order did change # to 300 shares at 3.33 self.assertEqual(300, asset2_order.amount) self.assertEqual(3.33, asset2_order.limit) self.assertEqual(2, asset2_order.asset)
def test_blotter_order_types(self, style_obj, expected_lmt, expected_stp): blotter = Blotter('daily') blotter.order(self.asset_24, 100, style_obj) result = blotter.open_orders[self.asset_24][0] self.assertEqual(result.limit, expected_lmt) self.assertEqual(result.stop, expected_stp)
def test_slippage_and_commission_dispatching(self): blotter = Blotter( self.sim_params.data_frequency, equity_slippage=FixedSlippage(spread=0.0), future_slippage=FixedSlippage(spread=2.0), equity_commission=PerTrade(cost=1.0), future_commission=PerTrade(cost=2.0), ) blotter.order(self.asset_24, 1, MarketOrder()) blotter.order(self.future_cl, 1, MarketOrder()) bar_data = self.create_bardata( simulation_dt_func=lambda: self.sim_params.sessions[-1], ) txns, commissions, _ = blotter.get_transactions(bar_data) # The equity transaction should have the same price as its current # price because the slippage spread is zero. Its commission should be # $1.00. equity_txn = txns[0] self.assertEqual( equity_txn.price, bar_data.current(equity_txn.asset, 'price'), ) self.assertEqual(commissions[0]['cost'], 1.0) # The future transaction price should be 1.0 more than its current # price because half of the 'future_slippage' spread is added. Its # commission should be $2.00. future_txn = txns[1] self.assertEqual( future_txn.price, bar_data.current(future_txn.asset, 'price') + 1.0, ) self.assertEqual(commissions[1]['cost'], 2.0)
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_batch_order_matches_multiple_orders(self): """ Ensure the effect of order_batch is the same as multiple calls to order. """ blotter1 = Blotter(self.sim_params.data_frequency) blotter2 = Blotter(self.sim_params.data_frequency) for i in range(1, 4): order_arg_lists = [ (self.asset_24, i * 100, MarketOrder()), (self.asset_25, i * 100, LimitOrder(i * 100 + 1)), ] order_batch_ids = blotter1.batch_order(order_arg_lists) order_ids = [] for order_args in order_arg_lists: order_ids.append(blotter2.order(*order_args)) self.assertEqual(len(order_batch_ids), len(order_ids)) self.assertEqual(len(blotter1.open_orders), len(blotter2.open_orders)) for (asset, _, _), order_batch_id, order_id in zip( order_arg_lists, order_batch_ids, order_ids ): self.assertEqual(len(blotter1.open_orders[asset]), len(blotter2.open_orders[asset])) self.assertEqual(order_batch_id, blotter1.open_orders[asset][i-1].id) self.assertEqual(order_id, blotter2.open_orders[asset][i-1].id)
def test_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_blotter_eod_cancellation(self): blotter = Blotter('minute', cancel_policy=EODCancel()) # Make two orders for the same asset, so we can test that we are not # mutating the orders list as we are cancelling orders blotter.order(self.asset_24, 100, MarketOrder()) blotter.order(self.asset_24, -100, MarketOrder()) self.assertEqual(len(blotter.new_orders), 2) order_ids = [order.id for order in blotter.open_orders[self.asset_24]] self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN) self.assertEqual(blotter.new_orders[1].status, ORDER_STATUS.OPEN) blotter.execute_cancel_policy(BAR) self.assertEqual(blotter.new_orders[0].status, ORDER_STATUS.OPEN) self.assertEqual(blotter.new_orders[1].status, ORDER_STATUS.OPEN) blotter.execute_cancel_policy(SESSION_END) for order_id in order_ids: order = blotter.orders[order_id] self.assertEqual(order.status, ORDER_STATUS.CANCELLED)
def 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') asset1 = self.asset_finder.retrieve_asset(1) metadata = make_simple_equity_info([asset1.sid], self.start, self.end) with TempDirectory() as tempdir, \ tmp_trading_env(equities=metadata, load=self.make_load_function()) 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 = { asset1.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, 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, 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, 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( asset1, 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=data_portal, simulation_dt_func=lambda: tick, data_frequency=sim_params.data_frequency, trading_calendar=self.trading_calendar, restrictions=NoRestrictions(), ) 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.asset, asset1) 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[asset1] if total_volume == 0: self.assertIsNone(cumulative_pos) else: self.assertEqual(total_volume, cumulative_pos.amount) # the open orders should not contain the asset. oo = blotter.open_orders self.assertNotIn( asset1, 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 """ 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') asset1 = self.asset_finder.retrieve_asset(1) metadata = make_simple_equity_info([asset1.sid], self.start, self.end) with TempDirectory() as tempdir, \ tmp_trading_env(equities=metadata, load=self.make_load_function()) 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 = { asset1.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, 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( asset1, 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=data_portal, simulation_dt_func=lambda: tick, data_frequency=sim_params.data_frequency, trading_calendar=self.trading_calendar, restrictions=NoRestrictions(), ) 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.asset, asset1) 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[asset1] if total_volume == 0: self.assertIsNone(cumulative_pos) else: self.assertEqual(total_volume, cumulative_pos.amount) # the open orders should not contain the asset. oo = blotter.open_orders self.assertNotIn( asset1, oo, "Entry is removed when no open orders" )
def test_order_hold(self): """ Held orders act almost identically to open orders, except for the status indication. When a fill happens, the order should switch status to OPEN/FILLED as necessary """ blotter = Blotter(self.sim_params.data_frequency) # Nothing happens on held of a non-existent order blotter.hold(56) self.assertEqual(blotter.new_orders, []) open_id = blotter.order(self.asset_24, 100, MarketOrder()) open_order = blotter.open_orders[self.asset_24][0] self.assertEqual(open_order.id, open_id) blotter.hold(open_id) self.assertEqual(len(blotter.new_orders), 1) self.assertEqual(len(blotter.open_orders[self.asset_24]), 1) held_order = blotter.new_orders[0] self.assertEqual(held_order.status, ORDER_STATUS.HELD) self.assertEqual(held_order.reason, '') blotter.cancel(held_order.id) self.assertEqual(len(blotter.new_orders), 1) self.assertEqual(len(blotter.open_orders[self.asset_24]), 0) cancelled_order = blotter.new_orders[0] self.assertEqual(cancelled_order.id, held_order.id) self.assertEqual(cancelled_order.status, ORDER_STATUS.CANCELLED) for data in ([100, self.sim_params.sessions[0]], [400, self.sim_params.sessions[1]]): # Verify that incoming fills will change the order status. trade_amt = data[0] dt = data[1] order_size = 100 expected_filled = int(trade_amt * DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT) expected_open = order_size - expected_filled expected_status = ORDER_STATUS.OPEN if expected_open else \ ORDER_STATUS.FILLED blotter = Blotter(self.sim_params.data_frequency) open_id = blotter.order(self.asset_24, order_size, MarketOrder()) open_order = blotter.open_orders[self.asset_24][0] self.assertEqual(open_id, open_order.id) blotter.hold(open_id) held_order = blotter.new_orders[0] filled_order = None blotter.current_dt = dt bar_data = self.create_bardata( simulation_dt_func=lambda: dt, ) txns, _, _ = blotter.get_transactions(bar_data) for txn in txns: filled_order = blotter.orders[txn.order_id] self.assertEqual(filled_order.id, held_order.id) self.assertEqual(filled_order.status, expected_status) self.assertEqual(filled_order.filled, expected_filled) self.assertEqual(filled_order.open_amount, expected_open)
def test_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)