def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
sim_params = factory.create_simulation_parameters()
blotter = Blotter()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = sim_params.first_open
generated_trades = factory.create_trade_history(
sid,
price,
volume,
trade_interval,
sim_params
)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in range(order_count):
blotter.set_date(order_date)
blotter.order(sid, order_amount * alternator ** i, None, None)
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
# there should now be one open order list stored under the sid
oo = blotter.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(order_count, len(order_list))
for i in range(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator ** i)
tracker = PerformanceTracker(sim_params)
benchmark_returns = [
Event({'dt': dt,
'returns': ret,
'type':
zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
generated_events = date_sorted_sources(generated_trades,
benchmark_returns)
# this approximates the loop inside TradingSimulationClient
transactions = []
for dt, events in itertools.groupby(generated_events,
operator.attrgetter('dt')):
for event in events:
if event.type == DATASOURCE_TYPE.TRADE:
for txn, order in blotter.process_trade(event):
transactions.append(txn)
tracker.process_event(txn)
tracker.process_event(event)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in range(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should now be empty
oo = blotter.open_orders
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(0, len(order_list))
def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
sim_params = factory.create_simulation_parameters()
trade_sim = TransactionSimulator()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = sim_params.first_open
generated_trades = factory.create_trade_history(
sid, price, volume, trade_interval, sim_params)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in xrange(order_count):
order = Order(
**{
'sid': sid,
'amount': order_amount * alternator**i,
'dt': order_date
})
trade_sim.place_order(order)
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
# there should now be one open order list stored under the sid
oo = trade_sim.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(order_count, len(order_list))
for i in xrange(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator**i)
tracker = PerformanceTracker(sim_params)
# this approximates the loop inside TradingSimulationClient
transactions = []
for dt, trades in itertools.groupby(generated_trades,
operator.attrgetter('dt')):
for trade in trades:
trade_sim.update(trade)
if trade.TRANSACTION:
transactions.append(trade.TRANSACTION)
tracker.process_event(trade)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in xrange(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should now be empty
oo = trade_sim.open_orders
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(0, len(order_list))
class AlgorithmSimulator(object):
EMISSION_TO_PERF_KEY_MAP = {
'minute': 'intraday_perf',
'daily': 'daily_perf'
}
def get_hash(self):
"""
There should only ever be one TSC in the system, so
we don't bother passing args into the hash.
"""
return self.__class__.__name__ + hash_args()
def __init__(self, algo, sim_params):
# ==============
# Simulation
# Param Setup
# ==============
self.sim_params = sim_params
# ==============
# Perf Tracker
# Setup
# ==============
self.perf_tracker = PerformanceTracker(self.sim_params)
self.perf_key = self.EMISSION_TO_PERF_KEY_MAP[
self.perf_tracker.emission_rate]
# ==============
# Algo Setup
# ==============
self.algo = algo
self.algo_start = self.sim_params.first_open
self.algo_start = self.algo_start.replace(hour=0, minute=0,
second=0,
microsecond=0)
# ==============
# Snapshot Setup
# ==============
# The algorithm's data as of our most recent event.
# We want an object that will have empty objects as default
# values on missing keys.
self.current_data = BarData()
# We don't have a datetime for the current snapshot until we
# receive a message.
self.simulation_dt = None
self.snapshot_dt = None
# =============
# Logging Setup
# =============
# Processor function for injecting the algo_dt into
# user prints/logs.
def inject_algo_dt(record):
if not 'algo_dt' in record.extra:
record.extra['algo_dt'] = self.snapshot_dt
self.processor = Processor(inject_algo_dt)
def transform(self, stream_in):
"""
Main generator work loop.
"""
# Set the simulation date to be the first event we see.
peek_date, peek_snapshot = next(stream_in)
self.simulation_dt = peek_date
# Stitch back together the generator by placing the peeked
# event back in front
stream = itertools.chain([(peek_date, peek_snapshot)],
stream_in)
# inject the current algo
# snapshot time to any log record generated.
with self.processor.threadbound():
updated = False
bm_updated = False
for date, snapshot in stream:
self.perf_tracker.set_date(date)
self.algo.blotter.set_date(date)
# If we're still in the warmup period. Use the event to
# update our universe, but don't yield any perf messages,
# and don't send a snapshot to handle_data.
if date < self.algo_start:
for event in snapshot:
if event.type in (DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.CUSTOM):
self.update_universe(event)
self.perf_tracker.process_event(event)
else:
for event in snapshot:
if event.type in (DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.CUSTOM):
self.update_universe(event)
updated = True
if event.type == DATASOURCE_TYPE.BENCHMARK:
bm_updated = True
txns, orders = self.algo.blotter.process_trade(event)
for data in chain([event], txns, orders):
self.perf_tracker.process_event(data)
# Update our portfolio.
self.algo.set_portfolio(self.perf_tracker.get_portfolio())
# Send the current state of the universe
# to the user's algo.
if updated:
self.simulate_snapshot(date)
updated = False
# run orders placed in the algorithm call
# above through perf tracker before emitting
# the perf packet, so that the perf includes
# placed orders
for order in self.algo.blotter.new_orders:
self.perf_tracker.process_event(order)
self.algo.blotter.new_orders = []
# The benchmark is our internal clock. When it
# updates, we need to emit a performance message.
if bm_updated:
bm_updated = False
yield self.get_message(date)
risk_message = self.perf_tracker.handle_simulation_end()
# When emitting minutely, it is still useful to have a final
# packet with the entire days performance rolled up.
if self.perf_tracker.emission_rate == 'minute':
daily_rollup = self.perf_tracker.to_dict(
emission_type='daily'
)
daily_rollup['daily_perf']['recorded_vars'] = \
self.algo.recorded_vars
yield daily_rollup
yield risk_message
def get_message(self, date):
rvars = self.algo.recorded_vars
if self.perf_tracker.emission_rate == 'daily':
perf_message = \
self.perf_tracker.handle_market_close()
perf_message['daily_perf']['recorded_vars'] = rvars
return perf_message
elif self.perf_tracker.emission_rate == 'minute':
self.perf_tracker.handle_minute_close(date)
perf_message = self.perf_tracker.to_dict()
perf_message['intraday_perf']['recorded_vars'] = rvars
return perf_message
def update_universe(self, event):
"""
Update the universe with new event information.
"""
# Update our knowledge of this event's sid
sid_data = self.current_data[event.sid]
sid_data.__dict__.update(event.__dict__)
def simulate_snapshot(self, date):
"""
Run the user's algo against our current snapshot and update
the algo's simulated time.
"""
# Needs to be set so that we inject the proper date into algo
# log/print lines.
self.snapshot_dt = date
self.algo.set_datetime(self.snapshot_dt)
# Update the simulation time.
self.simulation_dt = date
self.algo.handle_data(self.current_data)
def transaction_sim(self, **params):
""" This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
sim_params = factory.create_simulation_parameters()
blotter = Blotter()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = sim_params.first_open
generated_trades = factory.create_trade_history(
sid, price, volume, trade_interval, sim_params)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in xrange(order_count):
blotter.set_date(order_date)
blotter.order(sid, order_amount * alternator**i, None, None)
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
# there should now be one open order list stored under the sid
oo = blotter.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(order_count, len(order_list))
for i in xrange(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator**i)
tracker = PerformanceTracker(sim_params)
benchmark_returns = [
Event({
'dt': ret.date,
'returns': ret.returns,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'
}) for ret in trading.environment.benchmark_returns
if ret.date.date() >= sim_params.period_start.date()
and ret.date.date() <= sim_params.period_end.date()
]
generated_events = date_sorted_sources(generated_trades,
benchmark_returns)
# this approximates the loop inside TradingSimulationClient
transactions = []
for dt, events in itertools.groupby(generated_events,
operator.attrgetter('dt')):
for event in events:
if event.type == DATASOURCE_TYPE.TRADE:
for txn, order in blotter.process_trade(event):
transactions.append(txn)
tracker.process_event(txn)
tracker.process_event(event)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in xrange(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should now be empty
oo = blotter.open_orders
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(0, len(order_list))
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']
trade_delay = params.get('trade_delay')
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
sim_params = factory.create_simulation_parameters()
trade_sim = TransactionSimulator()
price = [10.1] * trade_count
volume = [100] * trade_count
start_date = sim_params.first_open
generated_trades = factory.create_trade_history(
sid,
price,
volume,
trade_interval,
sim_params
)
if alternate:
alternator = -1
else:
alternator = 1
order_date = start_date
for i in xrange(order_count):
order = ndict({
'sid': sid,
'amount': order_amount * alternator ** i,
'dt': order_date
})
trade_sim.place_order(order)
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
# there should now be one open order list stored under the sid
oo = trade_sim.open_orders
self.assertEqual(len(oo), 1)
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(order_count, len(order_list))
for i in xrange(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator ** i)
tracker = PerformanceTracker(sim_params)
# this approximates the loop inside TradingSimulationClient
transactions = []
for trade in generated_trades:
if trade_delay:
trade.dt = trade.dt + trade_delay
trade_sim.update(trade)
if trade.TRANSACTION:
transactions.append(trade.TRANSACTION)
tracker.process_event(trade)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in xrange(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.cumulative_performance.positions[sid]
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should now be empty
oo = trade_sim.open_orders
self.assertTrue(sid in oo)
order_list = oo[sid]
self.assertEqual(0, len(order_list))
