mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-06 05:14:38 +08:00
Eddie Hebert
252 lines
8.1 KiB
Python
252 lines
8.1 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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__all__ = ['DataFrameSource', 'SpecificEquityTrades']
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import random
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import pytz
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from itertools import cycle, ifilter, izip
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from datetime import datetime, timedelta
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import pandas as pd
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from copy import copy
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import numpy as np
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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.gens.utils import hash_args, create_trade
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def date_gen(start=datetime(2006, 6, 6, 12, tzinfo=pytz.utc),
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delta=timedelta(minutes=1),
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count=100,
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repeats=None):
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"""
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Utility to generate a stream of dates.
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"""
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if repeats:
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return (start + (i * delta)
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for i in xrange(count)
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for n in xrange(repeats))
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else:
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return (start + (i * delta) for i in xrange(count))
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def mock_prices(count, rand=False):
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"""
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Utility to generate a stream of mock prices. By default
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cycles through values from 0.0 to 10.0, n times. Optional
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flag to give random values between 0.0 and 10.0
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"""
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if rand:
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return (random.uniform(1.0, 10.0) for i in xrange(count))
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else:
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return (float(i % 10) + 1.0 for i in xrange(count))
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def mock_volumes(count, rand=False):
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"""
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Utility to generate a set of volumes. By default cycles
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through values from 100 to 1000, incrementing by 50. Optional
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flag to give random values between 100 and 1000.
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"""
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if rand:
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return (random.randrange(100, 1000) for i in xrange(count))
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else:
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return ((i * 50) % 900 + 100 for i in xrange(count))
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class SpecificEquityTrades(object):
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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, *args, **kwargs):
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# We shouldn't get any positional arguments.
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assert len(args) == 0
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# Default to None for event_list and filter.
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self.event_list = kwargs.get('event_list')
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self.filter = kwargs.get('filter')
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if self.event_list is not None:
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# If event_list is provided, extract parameters from there
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# This isn't really clean and ultimately I think this
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# class should serve a single purpose (either take an
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# event_list or autocreate events).
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self.count = kwargs.get('count', len(self.event_list))
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self.sids = kwargs.get(
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'sids',
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np.unique([event.sid for event in self.event_list]).tolist())
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self.start = kwargs.get('start', self.event_list[0].dt)
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self.end = kwargs.get('start', self.event_list[-1].dt)
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self.delta = kwargs.get(
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'delta',
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self.event_list[1].dt - self.event_list[0].dt)
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self.concurrent = kwargs.get('concurrent', False)
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else:
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# Unpack config dictionary with default values.
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self.count = kwargs.get('count', 500)
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self.sids = kwargs.get('sids', [1, 2])
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self.start = kwargs.get(
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'start',
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datetime(2008, 6, 6, 15, tzinfo=pytz.utc))
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self.delta = kwargs.get(
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'delta',
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timedelta(minutes=1))
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self.concurrent = kwargs.get('concurrent', False)
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# Hash_value for downstream sorting.
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self.arg_string = hash_args(*args, **kwargs)
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self.generator = self.create_fresh_generator()
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def __iter__(self):
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return self
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def next(self):
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return self.generator.next()
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def rewind(self):
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self.generator = self.create_fresh_generator()
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def get_hash(self):
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return self.__class__.__name__ + "-" + self.arg_string
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def update_source_id(self, gen):
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for event in gen:
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event.source_id = self.get_hash()
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yield event
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def create_fresh_generator(self):
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if self.event_list:
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event_gen = (event for event in self.event_list)
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unfiltered = self.update_source_id(event_gen)
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# Set up iterators for each expected field.
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else:
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if self.concurrent:
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# in this context the count is the number of
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# trades per sid, not the total.
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dates = date_gen(
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count=self.count,
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start=self.start,
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delta=self.delta,
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repeats=len(self.sids),
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)
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else:
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dates = date_gen(
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count=self.count,
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start=self.start,
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delta=self.delta
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)
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prices = mock_prices(self.count)
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volumes = mock_volumes(self.count)
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sids = cycle(self.sids)
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# Combine the iterators into a single iterator of arguments
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arg_gen = izip(sids, prices, volumes, dates)
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# Convert argument packages into events.
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unfiltered = (create_trade(*args, source_id=self.get_hash())
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for args in arg_gen)
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# If we specified a sid filter, filter out elements that don't
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# match the filter.
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if self.filter:
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filtered = ifilter(
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lambda event: event.sid in self.filter, unfiltered)
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# Otherwise just use all events.
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else:
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filtered = unfiltered
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# Return the filtered event stream.
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return filtered
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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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