tradesim as generator v. 2.0

This commit is contained in:
scottsanderson
2012-08-03 14:07:05 -04:00
parent d74bc1bbc5
commit ace5d05fab
6 changed files with 229 additions and 137 deletions
+8 -2
View File
@@ -11,8 +11,8 @@ log = logbook.Logger('Transaction Simulator')
class TransactionSimulator(object):
UPDATER = True
def __init__(self, open_orders, style=SIMULATION_STYLE.PARTIAL_VOLUME):
self.open_orders = open_orders
def __init__(self, sid_filter, style=SIMULATION_STYLE.PARTIAL_VOLUME):
self.open_orders = {}
self.txn_count = 0
self.trade_window = datetime.timedelta(seconds=30)
self.orderTTL = datetime.timedelta(days=1)
@@ -27,6 +27,12 @@ class TransactionSimulator(object):
elif style == SIMULATION_STYLE.NOOP:
self.apply_trade_to_open_orders = self.simulate_noop
for sid in sid_filter:
self.open_orders[sid] = []
def place_order(self, order):
self.open_orders[order.sid].append(order)
def update(self, event):
event.TRANSACTION = None
if event.type == zp.DATASOURCE_TYPE.TRADE:
+2 -4
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@@ -15,7 +15,7 @@ def date_sorted_sources(*sources):
"""
Takes an iterable of SortBundles, generating namestrings and initialized datasources
for each before piping them into a date_sort.
n """
"""
for source in sources:
assert iter(source), "Source %s not iterable" % source
@@ -55,7 +55,7 @@ def merged_transforms(sorted_stream, bundles):
tnfms_with_streams = zip(split, bundles)
# Convert the copies into transform streams.
tnfms = [
tnfm_gens = [
StatefulTransform(
stream_copy,
bundle.tnfm,
@@ -64,8 +64,6 @@ def merged_transforms(sorted_stream, bundles):
)
for stream_copy, bundle in tnfms_with_streams
]
tnfm_gens = [tnfm.gen() for tnfm in tnfms]
# Roundrobin the outputs of our transforms to create a single flat stream.
to_merge = roundrobin(tnfm_gens, namestrings)
+3 -1
View File
@@ -11,7 +11,7 @@ from zipline.gens.composites import SourceBundle, TransformBundle, \
date_sorted_sources, merged_transforms
from zipline.gens.tradegens import SpecificEquityTrades
from zipline.gens.transform import MovingAverage, Passthrough, StatefulTransform
from zipline.gens.tradesimulation import trade_simulation_client as tsc
from zipline.gens.tradesimulation import TradeSimulationClient as tsc
import zipline.protocol as zp
@@ -21,6 +21,7 @@ if __name__ == "__main__":
#Set up source a. One minute between events.
args_a = tuple()
kwargs_a = {
'count' : 2000,
'sids' : [1,2,3],
'start' : datetime(2012,1,3,15, tzinfo = pytz.utc),
'delta' : timedelta(minutes = 10),
@@ -31,6 +32,7 @@ if __name__ == "__main__":
#Set up source b. Two minutes between events.
args_b = tuple()
kwargs_b = {
'count' : 2000,
'sids' : [2,3,4],
'start' : datetime(2012,1,3,14, tzinfo = pytz.utc),
'delta' : timedelta(minutes = 10),
+1 -1
View File
@@ -84,7 +84,7 @@ class SpecificEquityTrades(object):
self.generator = self.create_fresh_generator()
def __iter__(self):
return self.generator
return self
def next(self):
return self.generator.next()
+200 -124
View File
@@ -9,7 +9,7 @@ from zipline.gens.transform import StatefulTransform
from zipline.finance.trading import TransactionSimulator
from zipline.finance.performance import PerformanceTracker
def trade_simulation_client(stream_in, algo, environment, sim_style):
class TradeSimulationClient(object):
"""
Generator that takes the expected output of a merge, a user
algorithm, a trading environment, and a simulator style as
@@ -42,61 +42,123 @@ def trade_simulation_client(stream_in, algo, environment, sim_style):
overwritten so that only the most recent snapshot of the universe
is sent to the algo.
"""
#============
# Algo Setup
#============
# Initialize txn_sim's dictionary of orders here so that we can
# reference it from within the user's algorithm.
sids = algo.get_sid_filter()
open_orders = {}
def __init__(self, stream_in, algo, environment, sim_style):
for sid in sids:
open_orders[sid] = []
self.stream_in = stream_in
self.algo = algo
self.sids = algo.get_sid_filter()
self.environment = environment
self.style = sim_style
self.__generator = None
def get_hash(self):
"""
There should only ever be one TSC in the system.
"""
return self.__class__.__name__ + hash_args()
def __iter__(self):
return self
def next(self):
if self.__generator:
return self.__generator.next()
else:
self.__generator = self.run_simulation()
return self.__generator.next()
def run_simulation(self):
"""
Main generator work loop.
"""
# Simulate filling any open orders made by the previous run of
# the user's algorithm. Sets the txn field to true on any
# event that results in a filled order.
ordering_client = StatefulTransform(
self.stream_in,
TransactionSimulator,
self.sids,
style = self.style
)
# Pipe the events with transactions to perf. This will remove
# the txn field added by TransactionSimulator and replace it
# with a portfolio object to be passed to the user's
# algorithm. Also adds a PERF_MESSAGE field which is usually
# none, but contains an update message once per day.
current_portfolio = StatefulTransform(
ordering_client,
PerformanceTracker,
self.environment,
self.sids
)
# Pass both the ordering client's state and messages with the
# current portfolio into the algorithm for simulation.
algo_results = AlgorithmSimulator(
current_portfolio,
ordering_client.state,
self.algo,
)
for message in algo_results:
yield message
class AlgorithmSimulator(object):
# Pipe the in stream into the transaction simulator.
# Creates a txn field on the event containing transaction
# information if we filled any pending orders on the event's sid.
# TRANSACTION is None if we didn't fill any orders.
with_txns = StatefulTransform(
stream_in,
TransactionSimulator,
open_orders,
style = sim_style
)
# Pipe the events with transactions to perf. This will remove the
# txn field added by TransactionSimulator and replace it with
# a portfolio object to be passed to the user's algorithm. Also adds
# a PERF_MESSAGE field which is usually none, but contains an update
# message once per day.
with_portfolio_and_perf_msg = StatefulTransform(
with_txns,
PerformanceTracker,
environment,
sids
)
# Batch the event stream by dt to be processed by the user's algo.
# Yields perf messages whenever it encounters them.
perf_messages = algo_simulator(with_portfolio_and_perf_msg, sids, algo, open_orders)
for message in perf_messages:
yield message
def algo_simulator(stream_in, sids, algo, order_book):
def __init__(self, stream_in, order_book, algo):
simulation_dt = None
self.stream_in = stream_in
# Closure to pass into the user's algo to allow placing orders
# into the txn_sim's dict of open orders.
def order(sid, amount):
assert sid in sids, "Order on invalid sid: %i" % sid
# 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.sids = algo.get_sid_filter()
# Monkey patch the user algorithm to place orders in the
# txn_sim order book.
self.algo.set_order(self.order)
self.algo.set_logger(logbook.Logger("Algolog"))
# Call the user's initialize method.
self.algo.initialize()
# The algorithm's universe as of our most recent event.
self.universe = ndict()
for sid in self.sids:
self.universe[sid] = ndict()
self.universe.portfolio = None
# We don't have a datetime for the current snapshot until we
# receive a message.
self.simulation_dt = None
self.this_snapshot_dt = None
self.__generator = None
def __iter__(self):
return self
def next(self):
if self.__generator:
return self.__generator.next()
else:
self.__generator = self._gen()
return self.__generator.next()
def order(self, sid, amount):
"""
Closure to pass into the user's algo to allow placing orders
into the txn_sim's dict of open orders.
"""
assert sid in self.sids, "Order on invalid sid: %i" % sid
order = ndict({
'dt' : simulation_dt,
'dt' : self.simulation_dt,
'sid' : sid,
'amount' : int(amount),
'filled' : 0
@@ -104,91 +166,105 @@ def algo_simulator(stream_in, sids, algo, order_book):
# Tell the user if they try to buy 0 shares of something.
if order.amount == 0:
log = "requested to trade zero shares of {sid}".format(
zero_message = "Requested to trade zero shares of {sid}".format(
sid=event.sid
)
log.debug(log)
log.debug(zero_message)
# Don't bother placing orders for 0 shares.
return
order_book[sid].append(order)
# Set the algo's order method.
algo.set_order(order)
# Provide a logbook logging interface to user code.
algo.set_logger(logbook.Logger("Algolog"))
# 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)
# Call user-defined initialize method before we process any
# events.
algo.initialize()
universe = ndict()
for sid in sids:
universe[sid] = ndict()
universe.portfolio = None
this_snapshot_dt = None
for event in stream_in:
# Yield any perf messages received to be relayed back to the browser.
if event.perf_message:
yield event.perf_message
del event['perf_message']
# This should only happen for the first event we run.
if simulation_dt == None:
simulation_dt = event.dt
# If we are currently creating a new message and this update
# matches the message dt, update the state of the universe.
if this_snapshot_dt != None:
if event.dt == this_snapshot_dt:
update_universe(event, universe)
# If we are constructing a snapshot and we hit a new dt, call
# handle_data and record how long it takes.
else:
start_tic = datetime.now()
algo.handle_data(universe)
stop_tic = datetime.now()
# How long did you take?
delta = stop_tic - start_tic
# Update the simulation time.
simulation_dt = this_snapshot_dt + delta
def _gen(self):
"""
Internal generator work loop.
"""
for event in self.stream_in:
# Yield any perf messages received to be relayed back to the browser.
if event.perf_message:
yield event.perf_message
del event['perf_message']
# Update the universe with the new event.
update_universe(event, universe)
# This should only happen for the first event we run.
if self.simulation_dt == None:
self.simulation_dt = event.dt
# ======================
# Time Compression Logic
# ======================
if self.this_snapshot_dt != None:
self.update_current_snapshot(event)
# If the current event is later than the simulation
# time, update the universe and start constructing
# another snapshot.
if event.dt >= simulation_dt:
this_snapshot_dt = event.dt
else:
this_snapshot_dt = None
# We have been fastforwarding. Update the universe
# and check if we can start a new snapshot.
# The algorithm has been missing events because it took
# too long processing. Update the universe with data from
# this event, then check if enough time has passed that we
# can start a new snapshot.
else:
self.update_universe(event)
if event.dt >= self.simulation_dt:
self.this_snapshot_dt = event.dt
def update_current_snapshot(self, event):
"""
Update our current snapshot of the universe. Call handle_data if
"""
# The new event matches our snapshot dt. Just update the
# universe and move on.
if event.dt == self.this_snapshot_dt:
self.update_universe(event)
# The new event does not match our snapshot.
else:
update_universe(event, universe)
if event.dt >= simulation_dt:
this_snapshot_dt = event.dt
self.simulate_current_snapshot()
# Once we've finished simulating the old snapshot,
# we can update the universe with the new event.
self.update_universe(event)
# The current event is later than the simulation time,
# which means the algorithm finished quickly enough to
# receive the new event. Start a new snapshot with this
# event's dt.
if event.dt >= self.simulation_dt:
self.this_snapshot_dt = event.dt
# The algorithm spent enough time processing that it
# missed the new event. Wait to start a new snapshot until
# the events catch up to the algo's simulated dt.
else:
self.this_snapshot_dt = None
def simulate_current_snapshot(self):
"""
Run the user's algo against our current snapshot and update the algo's
simulated time.
"""
start_tic = datetime.now()
self.algo.handle_data(self.universe)
stop_tic = datetime.now()
# How long did you take?
delta = stop_tic - start_tic
# Update the simulation time.
self.simulation_dt = self.this_snapshot_dt + delta
def update_universe(event, universe):
universe.portfolio = event.portfolio
del event['portfolio']
def update_universe(self, event):
"""
Update the universe with new event information.
"""
# Update our portfolio.
self.universe.portfolio = event.portfolio
event_sid = event.sid
del event['sid']
for field in event.keys():
universe[event_sid][field] = event[field]
# Update our knowledge of this event's sid
for field in event.keys():
self.universe[event.sid][field] = event[field]
+15 -5
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@@ -42,7 +42,7 @@ def functional_transform(stream_in, func, *args, **kwargs):
class StatefulTransform(object):
"""
Generic transform generator that takes each message from an
in-stream and passes it to a state class. For each call to
in-stream and passes it to a state object. For each call to
update, the state class must produce a message to be fed
downstream. Any transform class with the FORWARDER class variable
set to true will forward all fields in the original message.
@@ -63,16 +63,26 @@ class StatefulTransform(object):
# Create an instance of our transform class.
self.state = tnfm_class(*args, **kwargs)
# Generate the string associated with this generator's output.
# Create the string associated with this generator's output.
self.namestring = tnfm_class.__name__ + hash_args(*args, **kwargs)
# Generator isn't initialized until someone calls __iter__ or next().
self.__generator = None
def get_hash(self):
return self.namestring
def next(self):
if self.__generator:
return self.__generator.next()
else:
self.__generator = self._gen()
return self.__generator.next()
def __iter__(self):
return self.gen()
def gen(self):
return self
def _gen(self):
# IMPORTANT: Messages may contain pointers that are shared with
# other streams, so we only manipulate copies.
for message in self.stream_in: