def test_volume_share_slippage(self):
event = ndict({
'volume':
200,
'TRANSACTION':
None,
'type':
4,
'price':
3.0,
'datetime':
datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'high':
3.15,
'low':
2.85,
'sid':
133,
'source_id':
'test_source',
'close':
3.0,
'dt':
datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'open':
3.0
})
slippage_model = VolumeShareSlippage()
open_orders = {
133: [
ndict({
'dt':
datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount':
100,
'filled':
0,
'sid':
133
})
]
}
txn = slippage_model.simulate(event, open_orders)
expected_txn = {
'price': float(3.01875),
'dt': datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': int(50),
'sid': int(133)
}
self.assertIsNotNone(txn)
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
def order(self, sid, amount):
"""
Closure to pass into the user's algo to allow placing orders
into the transaction simulator's dict of open orders.
"""
order = ndict({
'dt': self.simulation_dt,
'sid': sid,
'amount': int(amount),
'filled': 0
})
# Tell the user if they try to buy 0 shares of something.
if order.amount == 0:
zero_message = "Requested to trade zero shares of {sid}".format(
sid=order.sid)
log.debug(zero_message)
# Don't bother placing orders for 0 shares.
return
# Add non-zero orders to the order book.
# !!!IMPORTANT SIDE-EFFECT!!!
# This modifies the internal state of the transaction
# simulator so that it can fill the placed order when it
# receives its next message.
self.order_book.place_order(order)
def order(self, sid, amount):
"""
Closure to pass into the user's algo to allow placing orders
into the transaction simulator's dict of open orders.
"""
order = ndict({
'dt': self.simulation_dt,
'sid': sid,
'amount': int(amount),
'filled': 0
})
# Tell the user if they try to buy 0 shares of something.
if order.amount == 0:
zero_message = "Requested to trade zero shares of {sid}".format(
sid=order.sid
)
log.debug(zero_message)
# Don't bother placing orders for 0 shares.
return
# Add non-zero orders to the order book.
# !!!IMPORTANT SIDE-EFFECT!!!
# This modifies the internal state of the transaction
# simulator so that it can fill the placed order when it
# receives its next message.
self.order_book.place_order(order)
def mock_done(id):
return ndict({
'dt': "DONE",
"source_id": id,
'tnfm_id': id,
'tnfm_value': None,
'type': DATASOURCE_TYPE.DONE
})
def mock_done(id):
return ndict({
'dt': "DONE",
"source_id": id,
'tnfm_id': id,
'tnfm_value': None,
'type': DATASOURCE_TYPE.DONE
})
def get_stddevs(self):
"""
Return an ndict of all our tracked standard deviations.
"""
out = ndict()
for field in self.fields:
out[field] = self.stdev(field)
return out
def get_averages(self):
"""
Return an ndict of all our tracked averages.
"""
out = ndict()
for field in self.fields:
out[field] = self.average(field)
return out
def get_averages(self):
"""
Return an ndict of all our tracked averages.
"""
out = ndict()
for field in self.fields:
out[field] = self.average(field)
return out
def _gen(self, stream_in):
# IMPORTANT: Messages may contain pointers that are shared with
# other streams. Transforms that modify their input
# messages should only manipulate copies.
log.info('Running StatefulTransform [%s]' % self.get_hash())
for message in stream_in:
# allow upstream generators to yield None to avoid
# blocking.
if message is None:
continue
assert_sort_unframe_protocol(message)
# This flag is set by by merged_transforms to ensure
# isolation of messages.
if self.merged:
message = deepcopy(message)
tnfm_value = self.state.update(message)
# PASSTHROUGH flag means we want to keep all original
# values, plus append tnfm_id and tnfm_value. Used for
# preserving the original event fields when our output
# will be fed into a merge. Currently only Passthrough
# uses this flag.
if self.passthrough and self.merged:
out_message = message
out_message.tnfm_id = self.namestring
out_message.tnfm_value = tnfm_value
yield out_message
# If the merged flag is set, we create a new message
# containing just the tnfm_id, the event's datetime, and
# the calculated tnfm_value. This is the default behavior
# for a non-passthrough transform being fed into a merge.
elif self.merged:
out_message = ndict()
out_message.tnfm_id = self.namestring
out_message.tnfm_value = tnfm_value
out_message.dt = message.dt
yield out_message
# Sequential flag should be used to add a single new
# key-value pair to the event. The new key is this
# transform's namestring, and its value is the value
# returned by state.update(event). This is almost
# identical to the behavior of FORWARDER, except we
# compress the two calculated values (tnfm_id, and
# tnfm_value) into a single field. This mode is used by
# the sequential_transforms composite and is the default
# if no behavior is specified by the internal state class.
elif self.sequential:
out_message = message
out_message[self.namestring] = tnfm_value
yield out_message
log.info('Finished StatefulTransform [%s]' % self.get_hash())
def _gen(self, stream_in):
# IMPORTANT: Messages may contain pointers that are shared with
# other streams. Transforms that modify their input
# messages should only manipulate copies.
log.info('Running StatefulTransform [%s]' % self.get_hash())
for message in stream_in:
# allow upstream generators to yield None to avoid
# blocking.
if message is None:
continue
assert_sort_unframe_protocol(message)
# This flag is set by by merged_transforms to ensure
# isolation of messages.
if self.merged:
message = deepcopy(message)
tnfm_value = self.state.update(message)
# PASSTHROUGH flag means we want to keep all original
# values, plus append tnfm_id and tnfm_value. Used for
# preserving the original event fields when our output
# will be fed into a merge. Currently only Passthrough
# uses this flag.
if self.passthrough and self.merged:
out_message = message
out_message.tnfm_id = self.namestring
out_message.tnfm_value = tnfm_value
yield out_message
# If the merged flag is set, we create a new message
# containing just the tnfm_id, the event's datetime, and
# the calculated tnfm_value. This is the default behavior
# for a non-passthrough transform being fed into a merge.
elif self.merged:
out_message = ndict()
out_message.tnfm_id = self.namestring
out_message.tnfm_value = tnfm_value
out_message.dt = message.dt
yield out_message
# Sequential flag should be used to add a single new
# key-value pair to the event. The new key is this
# transform's namestring, and its value is the value
# returned by state.update(event). This is almost
# identical to the behavior of FORWARDER, except we
# compress the two calculated values (tnfm_id, and
# tnfm_value) into a single field. This mode is used by
# the sequential_transforms composite and is the default
# if no behavior is specified by the internal state class.
elif self.sequential:
out_message = message
out_message[self.namestring] = tnfm_value
yield out_message
log.info('Finished StatefulTransform [%s]' % self.get_hash())
def test_volume_share_slippage(self):
event = ndict(
{'volume': 200,
'TRANSACTION': None,
'type': 4,
'price': 3.0,
'datetime': datetime.datetime(
2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'high': 3.15,
'low': 2.85,
'sid': 133,
'source_id': 'test_source',
'close': 3.0,
'dt':
datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'open': 3.0}
)
slippage_model = VolumeShareSlippage()
open_orders = {133: [
ndict(
{'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc),
'amount': 100,
'filled': 0, 'sid': 133})
]}
txn = slippage_model.simulate(
event,
open_orders
)
expected_txn = {
'price': float(3.01875),
'dt': datetime.datetime(
2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'amount': int(50),
'sid': int(133)
}
self.assertIsNotNone(txn)
for key, value in expected_txn.items():
self.assertEquals(value, txn[key])
def __init__(self,
blotter,
perf_tracker,
algo,
algo_start):
# ==========
# Algo Setup
# ==========
# We extract the order book from the txn client so that
# the algo can place new orders.
self.blotter = blotter
self.perf_tracker = perf_tracker
self.perf_key = self.EMISSION_TO_PERF_KEY_MAP[
perf_tracker.emission_rate]
self.algo = algo
self.algo_start = algo_start.replace(hour=0, minute=0,
second=0,
microsecond=0)
# Monkey patch the user algorithm to place orders in the
# TransactionSimulator's order book and use our logger.
self.algo.set_order(self.order)
# ==============
# Snapshot Setup
# ==============
# The algorithm's universe as of our most recent event.
# We want an ndict that will have empty objects as default
# values on missing keys.
self.universe = ndict(internal=defaultdict(SIDData))
# 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 __init__(self, blotter, perf_tracker, algo, algo_start):
# ==========
# Algo Setup
# ==========
# We extract the order book from the txn client so that
# the algo can place new orders.
self.blotter = blotter
self.perf_tracker = perf_tracker
self.perf_key = self.EMISSION_TO_PERF_KEY_MAP[
perf_tracker.emission_rate]
self.algo = algo
self.algo_start = algo_start.replace(hour=0,
minute=0,
second=0,
microsecond=0)
# Monkey patch the user algorithm to place orders in the
# TransactionSimulator's order book and use our logger.
self.algo.set_order(self.order)
# ==============
# Snapshot Setup
# ==============
# The algorithm's universe as of our most recent event.
# We want an ndict that will have empty objects as default
# values on missing keys.
self.universe = ndict(internal=defaultdict(SIDData))
# 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 create_trade(sid, price, amount, datetime, source_id="test_factory"):
row = ndict({
'source_id': source_id,
'type': DATASOURCE_TYPE.TRADE,
'sid': sid,
'dt': datetime,
'price': price,
'close': price,
'open': price,
'low': price * .95,
'high': price * 1.05,
'volume': amount
})
return row
def create_trade(sid, price, amount, datetime, source_id="test_factory"):
row = ndict({
'source_id': source_id,
'type': DATASOURCE_TYPE.TRADE,
'sid': sid,
'dt': datetime,
'price': price,
'close': price,
'open': price,
'low': price * .95,
'high': price * 1.05,
'volume': amount
})
return row
def __init__(self, order_book, algo, algo_start):
# ==========
# Algo Setup
# ==========
# We extract the order book from the txn client so that
# the algo can place new orders.
self.order_book = order_book
self.algo = algo
self.algo_start = algo_start.replace(hour=0, minute=0, second=0, microsecond=0)
# Monkey patch the user algorithm to place orders in the
# TransactionSimulator's order book and use our logger.
self.algo.set_order(self.order)
# ==============
# Snapshot Setup
# ==============
# The algorithm's universe as of our most recent event.
# We want an ndict that will have empty ndicts as default
# values on missing keys.
self.universe = ndict(internal=defaultdict(ndict))
# 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):
record.extra["algo_dt"] = self.snapshot_dt
self.processor = Processor(inject_algo_dt)
def handle_data(self, data, *args, **kwargs):
"""
New method to handle a data frame as sent to the algorithm's
handle_data method.
"""
# extract dates
#dts = [data[sid].datetime for sid in self.sids]
dts = [event.datetime for event in data.itervalues()]
# we have to provide the event with a dt. This is only for
# checking if the event is outside the window or not so a
# couple of seconds shouldn't matter. We don't add it to
# the data parameter, because it would mix dt with the
# sid keys.
event = ndict()
event.dt = max(dts)
event.data = data
# append data frame to window. update() will call handle_add() and
# handle_remove() appropriately
self.update(event)
# return newly computed or cached value
return self.get_transform_value(*args, **kwargs)
def handle_data(self, data, *args, **kwargs):
"""
New method to handle a data frame as sent to the algorithm's
handle_data method.
"""
# extract dates
#dts = [data[sid].datetime for sid in self.sids]
dts = [event.datetime for event in data.itervalues()]
# we have to provide the event with a dt. This is only for
# checking if the event is outside the window or not so a
# couple of seconds shouldn't matter. We don't add it to
# the data parameter, because it would mix dt with the
# sid keys.
event = ndict()
event.dt = max(dts)
event.data = data
# append data frame to window. update() will call handle_add() and
# handle_remove() appropriately
self.update(event)
# return newly computed or cached value
return self.get_transform_value(*args, **kwargs)
def to_dt(msg):
return ndict({'dt': msg})
def to_dt(msg):
return ndict({'dt': msg})
def genTrades(self):
# create a sequence of trades
events = [
ndict(
{'volume': 2000,
'TRANSACTION': None,
'type': DATASOURCE_TYPE.TRADE,
'price': 3.0,
'datetime': datetime.datetime(
2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'high': 3.15,
'low': 2.85,
'sid': 133,
'source_id': 'test_source',
'close': 3.0,
'dt':
datetime.datetime(2006, 1, 5, 14, 31, tzinfo=pytz.utc),
'open': 3.0
}),
ndict(
{'volume': 2000,
'TRANSACTION': None,
'type': DATASOURCE_TYPE.TRADE,
'price': 3.5,
'datetime': datetime.datetime(
2006, 1, 5, 14, 32, tzinfo=pytz.utc),
'high': 3.15,
'low': 2.85,
'sid': 133,
'source_id': 'test_source',
'close': 3.5,
'dt':
datetime.datetime(2006, 1, 5, 14, 32, tzinfo=pytz.utc),
'open': 3.0
}),
ndict(
{'volume': 2000,
'TRANSACTION': None,
'type': DATASOURCE_TYPE.TRADE,
'price': 4.0,
'datetime': datetime.datetime(
2006, 1, 5, 14, 33, tzinfo=pytz.utc),
'high': 3.15,
'low': 2.85,
'sid': 133,
'source_id': 'test_source',
'close': 4.0,
'dt':
datetime.datetime(2006, 1, 5, 14, 33, tzinfo=pytz.utc),
'open': 3.5
}),
ndict(
{'volume': 2000,
'TRANSACTION': None,
'type': DATASOURCE_TYPE.TRADE,
'price': 3.5,
'datetime': datetime.datetime(
2006, 1, 5, 14, 34, tzinfo=pytz.utc),
'high': 3.15,
'low': 2.85,
'sid': 133,
'source_id': 'test_source',
'close': 3.5,
'dt':
datetime.datetime(2006, 1, 5, 14, 34, tzinfo=pytz.utc),
'open': 4.0
}),
ndict(
{'volume': 2000,
'TRANSACTION': None,
'type': DATASOURCE_TYPE.TRADE,
'price': 3.0,
'datetime': datetime.datetime(
2006, 1, 5, 14, 35, tzinfo=pytz.utc),
'high': 3.15,
'low': 2.85,
'sid': 133,
'source_id': 'test_source',
'close': 3.0,
'dt':
datetime.datetime(2006, 1, 5, 14, 35, tzinfo=pytz.utc),
'open': 3.5
})
]
return events
ERRORS = ndict({
# Raised if a user script calls the override_slippage magic
# with a slipage object that isn't a VolumeShareSlippage or
# FixedSlipapge
'UNSUPPORTED_SLIPPAGE_MODEL':
"You attempted to override slippage with an unsupported class. \
Please use VolumeShareSlippage or FixedSlippage.",
# Raised if a users script calls override_slippage magic
# after the initialize method has returned.
'OVERRIDE_SLIPPAGE_POST_INIT':
"You attempted to override slippage after the simulation has \
started. You may only call override_slippage in your initialize \
method.",
# Raised if a user script calls the override_commission magic
# with a commission object that isn't a PerShare or
# PerTrade commission
'UNSUPPORTED_COMMISSION_MODEL':
"You attempted to override commission with an unsupported class. \
Please use PerShare or PerTrade.",
# Raised if a users script calls override_commission magic
# after the initialize method has returned.
'OVERRIDE_COMMISSION_POST_INIT':
"You attempted to override commission after the simulation has \
started. You may only call override_commission in your initialize \
method.",
})
def to_dt(msg):
return ndict({"dt": msg})
ERRORS = ndict({
# Raised if a user script calls the override_slippage magic
# with a slipage object that isn't a VolumeShareSlippage or
# FixedSlipapge
'UNSUPPORTED_SLIPPAGE_MODEL':
"You attempted to override slippage with an unsupported class. \
Please use VolumeShareSlippage or FixedSlippage.",
# Raised if a users script calls override_slippage magic
# after the initialize method has returned.
'OVERRIDE_SLIPPAGE_POST_INIT':
"You attempted to override slippage after the simulation has \
started. You may only call override_slippage in your initialize \
method.",
# Raised if a user script calls the override_commission magic
# with a commission object that isn't a PerShare or
# PerTrade commission
'UNSUPPORTED_COMMISSION_MODEL':
"You attempted to override commission with an unsupported class. \
Please use PerShare or PerTrade.",
# Raised if a users script calls override_commission magic
# after the initialize method has returned.
'OVERRIDE_COMMISSION_POST_INIT':
"You attempted to override commission after the simulation has \
started. You may only call override_commission in your initialize \
method.",
})
