mirror of
https://github.com/wassname/catalyst.git
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Eddie Hebert
f85ad50e60
Clearing the way for adding in a DataSource class within the sources module.
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
#
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# Copyright 2012 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Tools to generate data sources.
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"""
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from copy import copy
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from itertools import ifilter
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import pandas as pd
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from zipline.gens.utils import hash_args
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from zipline.protocol import DATASOURCE_TYPE
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from zipline.utils import ndict
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from zipline.sources.test_source import SpecificEquityTrades
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class DataFrameSource(SpecificEquityTrades):
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"""
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Yields all events in event_list that match the given sid_filter.
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If no event_list is specified, generates an internal stream of events
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to filter. Returns all events if filter is None.
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Configuration options:
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count : integer representing number of trades
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sids : list of values representing simulated internal sids
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start : start date
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delta : timedelta between internal events
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filter : filter to remove the sids
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"""
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def __init__(self, data, **kwargs):
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assert isinstance(data.index, pd.tseries.index.DatetimeIndex)
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self.data = data
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# Unpack config dictionary with default values.
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self.count = kwargs.get('count', len(data))
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self.sids = kwargs.get('sids', data.columns)
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self.start = kwargs.get('start', data.index[0])
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self.end = kwargs.get('end', data.index[-1])
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self.delta = kwargs.get('delta', data.index[1] - data.index[0])
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# Hash_value for downstream sorting.
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self.arg_string = hash_args(data, **kwargs)
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self.generator = self.create_fresh_generator()
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def create_fresh_generator(self):
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def _generator(df=self.data):
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for dt, series in df.iterrows():
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if (dt < self.start) or (dt > self.end):
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continue
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event = {
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'dt': dt,
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'source_id': self.get_hash(),
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'type': DATASOURCE_TYPE.TRADE
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}
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for sid, price in series.iterkv():
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event = copy(event)
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event['sid'] = sid
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event['price'] = price
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event['volume'] = 1000
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yield ndict(event)
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# Return the filtered event stream.
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drop_sids = lambda x: x.sid in self.sids
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return ifilter(drop_sids, _generator())
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