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update the comments in tradesim

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scottsanderson
2012-08-17 11:07:12 -04:00
parent 85bac68fae
commit 5a70c7464a
1 changed files with 27 additions and 12 deletions
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zipline/gens/tradesimulation.py
+27 -12
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@@ -1,3 +1,5 @@
import signal
from logbook import Logger, Processor
from datetime import datetime, timedelta
@@ -13,10 +15,19 @@ from zipline.gens.utils import hash_args
log = Logger('Trade Simulation')
class AlgoTimeoutException(Exception):
def __init__(self):
pass
def handle_init_timeout(signum, frame):
log.error("Algorithm timed out during initialize.")
raise
class TradeSimulationClient(object):
"""
Generator that takes the expected output of a merge, a user
algorithm, a trading environment, and a simulator style as
Generator-style class that takes the expected output of a merge, a
user algorithm, a trading environment, and a simulator style as
arguments. Pipes the merge stream through a TransactionSimulator
and a PerformanceTracker, which keep track of the current state of
our algorithm's simulated universe. Results are fed to the user's
@@ -24,7 +35,7 @@ class TradeSimulationClient(object):
TransactionSimulator's order book.
TransactionSimulator maintains a dictionary from sids to the
unfulfilled orders placed by the user's algorithm. As trade
as-yet unfilled orders placed by the user's algorithm. As trade
events arrive, if the algorithm has open orders against the
trade's sid, the simulator will fill orders up to 25% of market
cap. Applied transactions are added to a txn field on the event
@@ -40,9 +51,9 @@ class TradeSimulationClient(object):
performance report, which is appended to event's perf_report
field.
Fully processed events are run through a batcher generator, which
batches together events with the same dt field into a single event
to be fed to the algo. The portfolio object is repeatedly
Fully processed events are fed to AlgorithmSimulator, which
batches together events with the same dt field into a single
snapshot to be fed to the algo. The portfolio object is repeatedly
overwritten so that only the most recent snapshot of the universe
is sent to the algo.
"""
@@ -54,13 +65,14 @@ class TradeSimulationClient(object):
self.environment = environment
self.style = sim_style
self.algo_sim = None
self.warmup_start = self.environment.prior_day_open
self.algo_start = self.environment.first_open
def get_hash(self):
"""
There should only ever be one TSC in the system.
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()
@@ -92,9 +104,9 @@ class TradeSimulationClient(object):
with_portfolio = perf_tracker.transform(with_filled_orders)
# Pass the messages from perf along with the trading client's
# state into the algorithm for simulation. We provide the
# trading client so that the algorithm can place new orders
# into the client's order book.
# state into the algorithm for simulation. We provide a
# pointer to the ordering client's internal state so that the
# algorithm can place new orders into the client's order book.
self.algo_sim = AlgorithmSimulator(
with_portfolio,
ordering_client.state,
@@ -273,7 +285,9 @@ class AlgorithmSimulator(object):
def update_current_snapshot(self, event):
"""
Update our current snapshot of the universe. Call handle_data if
Update our current snapshot of the universe. If event.dt doesn't
match our current snapshot's dt, we simulate the current snapshot
before processing the event.
"""
# The new event matches our snapshot dt. Just update the
# universe and move on.
@@ -326,3 +340,4 @@ class AlgorithmSimulator(object):
# Update our knowledge of this event's sid
for field in event.keys():
self.universe[event.sid][field] = event[field]