Files
catalyst/zipline/gens/mongods.py
T

70 lines
2.0 KiB
Python

"""
Generator-style DataSource that loads from MongoDB.
"""
import pytz
import logbook
from pymongo import ASCENDING
from zipline import ndict
from zipline.gens.utils import stringify_args
import zipline.protocol as zp
def create_pymongo_iterator(self, collection, filter, start_date, end_date):
"""
See the comments on :py:class:`zipline.messaging.DataSource` for
expected content of self.filter. Spec must adhere to that definition.
Returns an iterator that spits out raw objects loaded from MongoDB.
"""
log = logbook.Logger("MongoDBQuery")
# Object that will hold our database query.
spec = {}
# add the filters from the algorithm.
for name, value in filter.iteritems():
# Add the list of sids that we care about.
if name == 'sid':
assert isinstance(value, sid)
sid_range = {'sid':{'$in':value}}
spec.update(sid_range)
# limit the data to the date range [start, end], inclusive
date_range = {'dt':{'$gte':self.start, '$lte':self.end}}
spec.update(date_range)
fields = ['sid','price','volume','dt']
# In our collection, load all objects matching spec. Of those
# objects, get only the fields matching fields, and return the
# loaded objects sorted by dt from least to greatest.
cursor = self.collection.find(
fields = fields,
spec = spec,
sort = [("dt",ASCENDING)],
slave_ok = True
)
# Optimize the cursor sort to query in 'dt' and 'sid' order.
cursor = cursor.hint([('dt', ASCENDING),('sid', ASCENDING)])
# Set up the iterator
iterator = iter(self.cursor)
log.info("MongoDataSource iterator ready")
return iterator
def MongoTradeHistoryGen(collection, filter, start_date, end_date):
iterator = create_pymongo_iterator(collection, filter, start_date, end_date)
source_id = "MongoTradeHistoryGen" + stringify_args(collection, filter, start_date, end_date)
for event in iterator: