mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 00:53:42 +08:00
Thomas Wiecki
496 lines
18 KiB
Python
496 lines
18 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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Generator versions of transforms.
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"""
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import types
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import logbook
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from copy import deepcopy
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from datetime import datetime
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from collections import deque
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from abc import ABCMeta, abstractmethod
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import pandas as pd
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from zipline import ndict
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from zipline.utils.tradingcalendar import non_trading_days
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from zipline.gens.utils import assert_sort_unframe_protocol, hash_args
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log = logbook.Logger('Transform')
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class Passthrough(object):
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PASSTHROUGH = True
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"""
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Trivial class for forwarding events.
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"""
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def __init__(self):
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pass
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def update(self, event):
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pass
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class TransformMeta(type):
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"""
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Metaclass that automatically packages a class inside of
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StatefulTransform on initialization. Specifically, if Foo is a
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class with its __metaclass__ attribute set to TransformMeta, then
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calling Foo(*args, **kwargs) will return StatefulTransform(Foo,
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*args, **kwargs) instead of an instance of Foo. (Note that you can
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still recover an instance of a "raw" Foo by introspecting the
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resulting StatefulTransform's 'state' field.
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"""
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def __call__(cls, *args, **kwargs):
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return StatefulTransform(cls, *args, **kwargs)
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class StatefulTransform(object):
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"""
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Generic transform generator that takes each message from an
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in-stream and passes it to a state object. For each call to
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update, the state class must produce a message to be fed
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downstream. Any transform class with the FORWARDER class variable
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set to true will forward all fields in the original message.
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Otherwise only dt, tnfm_id, and tnfm_value are forwarded.
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"""
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def __init__(self, tnfm_class, *args, **kwargs):
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assert isinstance(tnfm_class, (types.ObjectType, types.ClassType)), \
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"Stateful transform requires a class."
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assert hasattr(tnfm_class, 'update'), \
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"Stateful transform requires the class to have an update method"
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# Flag set inside the Passthrough transform class to signify special
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# behavior if we are being fed to merged_transforms.
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self.passthrough = hasattr(tnfm_class, 'PASSTHROUGH')
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# Flags specifying how to append the calculated value.
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# Merged is the default for ease of testing, but we use sequential
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# in production.
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self.sequential = False
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self.merged = True
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# Create an instance of our transform class.
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if isinstance(tnfm_class, TransformMeta):
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# Classes derived TransformMeta have their __call__
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# attribute overridden. Since this is what is usually
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# used to create an instance, we have to delegate the
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# responsibility of creating an instance to
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# TransformMeta's parent class, which is 'type'. This is
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# what is implicitly done behind the scenes by the python
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# interpreter for most classes anyway, but here we have to
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# be explicit because we've overridden the method that
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# usually resolves to our super call.
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self.state = super(TransformMeta, tnfm_class).__call__(
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*args, **kwargs)
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# Normal object instantiation.
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else:
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self.state = tnfm_class(*args, **kwargs)
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# Create the string associated with this generator's output.
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self.namestring = tnfm_class.__name__ + hash_args(*args, **kwargs)
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def get_hash(self):
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return self.namestring
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def transform(self, stream_in):
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return self._gen(stream_in)
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def _gen(self, stream_in):
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# IMPORTANT: Messages may contain pointers that are shared with
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# other streams. Transforms that modify their input
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# messages should only manipulate copies.
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log.info('Running StatefulTransform [%s]' % self.get_hash())
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for message in stream_in:
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# allow upstream generators to yield None to avoid
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# blocking.
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if message is None:
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continue
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assert_sort_unframe_protocol(message)
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# This flag is set by by merged_transforms to ensure
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# isolation of messages.
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if self.merged:
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message = deepcopy(message)
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tnfm_value = self.state.update(message)
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# PASSTHROUGH flag means we want to keep all original
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# values, plus append tnfm_id and tnfm_value. Used for
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# preserving the original event fields when our output
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# will be fed into a merge. Currently only Passthrough
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# uses this flag.
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if self.passthrough and self.merged:
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out_message = message
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out_message.tnfm_id = self.namestring
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out_message.tnfm_value = tnfm_value
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yield out_message
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# If the merged flag is set, we create a new message
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# containing just the tnfm_id, the event's datetime, and
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# the calculated tnfm_value. This is the default behavior
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# for a non-passthrough transform being fed into a merge.
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elif self.merged:
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out_message = ndict()
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out_message.tnfm_id = self.namestring
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out_message.tnfm_value = tnfm_value
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out_message.dt = message.dt
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yield out_message
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# Sequential flag should be used to add a single new
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# key-value pair to the event. The new key is this
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# transform's namestring, and its value is the value
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# returned by state.update(event). This is almost
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# identical to the behavior of FORWARDER, except we
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# compress the two calculated values (tnfm_id, and
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# tnfm_value) into a single field. This mode is used by
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# the sequential_transforms composite and is the default
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# if no behavior is specified by the internal state class.
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elif self.sequential:
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out_message = message
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out_message[self.namestring] = tnfm_value
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yield out_message
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log.info('Finished StatefulTransform [%s]' % self.get_hash())
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class EventWindow(object):
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"""
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Abstract base class for transform classes that calculate iterative
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metrics on events within a given timedelta. Maintains a list of
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events that are within a certain timedelta of the most recent
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tick. Calls self.handle_add(event) for each event added to the
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window. Calls self.handle_remove(event) for each event removed
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from the window. Subclass these methods along with init(*args,
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**kwargs) to calculate metrics over the window.
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If the market_aware flag is True, the EventWindow drops old events
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based on the number of elapsed trading days between newest and oldest.
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Otherwise old events are dropped based on a raw timedelta.
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See zipline/transforms/mavg.py and zipline/transforms/vwap.py for example
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implementations of moving average and volume-weighted average
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price.
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"""
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# Mark this as an abstract base class.
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__metaclass__ = ABCMeta
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def __init__(self, market_aware=True, days=None, delta=None):
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self.market_aware = market_aware
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self.days = days
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self.delta = delta
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self.ticks = deque()
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# Market-aware mode only works with full-day windows.
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if self.market_aware:
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assert self.days and self.delta is None, \
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"Market-aware mode only works with full-day windows."
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self.all_holidays = deque(non_trading_days)
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self.cur_holidays = deque()
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# Non-market-aware mode requires a timedelta.
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else:
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assert self.delta and not self.days, \
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"Non-market-aware mode requires a timedelta."
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# Set the behavior for dropping events from the back of the
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# event window.
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if self.market_aware:
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self.drop_condition = self.out_of_market_window
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else:
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self.drop_condition = self.out_of_delta
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@abstractmethod
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def handle_add(self, event):
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raise NotImplementedError()
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@abstractmethod
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def handle_remove(self, event):
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raise NotImplementedError()
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def __len__(self):
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return len(self.ticks)
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def update(self, event):
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self.assert_well_formed(event)
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# Add new event and increment totals.
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self.ticks.append(deepcopy(event))
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# Subclasses should override handle_add to define behavior for
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# adding new ticks.
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self.handle_add(event)
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if self.market_aware:
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self.add_new_holidays(event.dt)
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# Clear out any expired events. drop_condition changes depending
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# on whether or not we are running in market_aware mode.
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#
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# oldest newest
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# | |
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# V V
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while self.drop_condition(self.ticks[0].dt, self.ticks[-1].dt):
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# popleft removes and returns the oldest tick in self.ticks
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popped = self.ticks.popleft()
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# Subclasses should override handle_remove to define
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# behavior for removing ticks.
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self.handle_remove(popped)
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def add_new_holidays(self, newest):
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# Add to our tracked window any untracked holidays that are
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# older than our newest event. (newest should always be
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# self.ticks[-1])
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while len(self.all_holidays) > 0 and self.all_holidays[0] <= newest:
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self.cur_holidays.append(self.all_holidays.popleft())
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def drop_old_holidays(self, oldest):
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# Drop from our tracked window any holidays that are older
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# than our oldest tracked event. (oldest should always
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# be self.ticks[0])
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while len(self.cur_holidays) > 0 and self.cur_holidays[0] < oldest:
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self.cur_holidays.popleft()
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def out_of_market_window(self, oldest, newest):
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self.drop_old_holidays(oldest)
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calendar_dates_between = (newest.date() - oldest.date()).days
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holidays_between = len(self.cur_holidays)
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trading_days_between = calendar_dates_between - holidays_between
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# "Put back" a day if oldest is earlier in its day than newest,
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# reflecting the fact that we haven't yet completed the last
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# day in the window.
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if oldest.time() > newest.time():
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trading_days_between -= 1
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return trading_days_between >= self.days
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def out_of_delta(self, oldest, newest):
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return (newest - oldest) >= self.delta
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# All event windows expect to receive events with datetime fields
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# that arrive in sorted order.
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def assert_well_formed(self, event):
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assert isinstance(event, ndict), "Bad event in EventWindow:%s" % event
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assert 'dt' in event, "Missing dt in EventWindow:%s" % event
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assert isinstance(event.dt, datetime), \
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"Bad dt in EventWindow:%s" % event
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if len(self.ticks) > 0:
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# Something is wrong if new event is older than previous.
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assert event.dt >= self.ticks[-1].dt, \
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"Events arrived out of order in EventWindow: %s -> %s" % \
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(event, self.ticks[0])
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class BatchTransform(EventWindow):
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"""Base class for batch transforms with a trailing window of
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variable length. As opposed to pure EventWindows that get a stream
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of events and are bound to a single SID, this class creates stream
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of pandas DataFrames with each colum representing a sid.
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There are two ways to create a new batch window:
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(i) Inherit from BatchTransform and overload get_value(data).
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E.g.:
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```
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class MyBatchTransform(BatchTransform):
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def get_value(self, data):
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# compute difference between the means of sid 0 and sid 1
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return data[0].mean() - data[1].mean()
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```
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(ii) Use the batch_transform decorator.
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E.g.:
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```
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@batch_transform
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def my_batch_transform(data):
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return data[0].mean() - data[1].mean()
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```
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In you algorithm you would then have to instantiate
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this in the initialize() method:
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```
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self.my_batch_transform = MyBatchTransform()
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```
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To then use it, inside of the algorithm handle_data(), call the
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handle_data() of the BatchTransform and pass it the current event:
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```
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result = self.my_batch_transform(data)
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```
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"""
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def __init__(self,
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func=None,
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refresh_period=None,
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market_aware=True,
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delta=None,
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days=None):
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super(BatchTransform, self).__init__(market_aware,
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days=days, delta=delta)
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if func is not None:
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self.compute_transform_value = func
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else:
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self.compute_transform_value = self.get_value
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self.refresh_period = refresh_period
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self.days = days
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self.trading_days_since_update = 0
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self.trading_days_total = 0
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self.full = False
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self.last_dt = None
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self.updated = False
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self.data = None
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def handle_data(self, data, *args, **kwargs):
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"""
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New method to handle a data frame as sent to the algorithm's
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handle_data method.
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"""
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# extract dates
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#dts = [data[sid].datetime for sid in self.sids]
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dts = [event.datetime for event in data.itervalues()]
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# we have to provide the event with a dt. This is only for
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# checking if the event is outside the window or not so a
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# couple of seconds shouldn't matter. We don't add it to
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# the data parameter, because it would mix dt with the
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# sid keys.
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event = ndict()
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event.dt = max(dts)
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event.data = data
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# append data frame to window. update() will call handle_add() and
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# handle_remove() appropriately
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self.update(event)
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# return newly computed or cached value
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return self.get_transform_value(*args, **kwargs)
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def handle_add(self, event):
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if not self.last_dt:
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self.last_dt = event.dt
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return
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# update trading day counters
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if self.last_dt.day != event.dt.day:
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self.last_dt = event.dt
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self.trading_days_since_update += 1
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self.trading_days_total += 1
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if self.trading_days_since_update >= self.refresh_period and\
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self.trading_days_total >= self.days:
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# Create datapanel of running event window.
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self.data = self.get_data()
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# Setting updated to True will cause get_transform_value()
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# to call the user-defined batch-transform with the most
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# recent datapanel
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self.updated = True
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self.trading_days_since_update = 0
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else:
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self.updated = False
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def get_data(self):
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"""Create a pandas.Panel (i.e. 3d DataFrame) from the
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events in the current window.
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Returns:
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The resulting panel looks like this:
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index : field_name (e.g. price)
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major axis/rows : dt
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minor axis/colums : sid
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"""
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# This Panel data structure ultimately gets passed to the
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# user-overloaded get_value() method.
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#
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# self.ticks contains ndicts with data, dt keys.
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# event parameter is an ndict with data, dt keys.
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fields = {}
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for field_name in ['price', 'volume']:
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sids = self.ticks[0].data.keys()
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# Skip non-existant fields
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if field_name not in self.ticks[0].data[sids[0]]:
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continue
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values_per_sid = {}
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for sid in sids:
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values_per_sid[sid] = pd.Series(
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{tick.data[sid].dt: tick.data[sid][field_name]
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for tick in self.ticks}
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)
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# concatenate different sids into one df
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fields[field_name] = pd.DataFrame.from_dict(values_per_sid)
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data = pd.Panel.from_dict(fields, orient='items')
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return data
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def handle_remove(self, event):
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# since an event is expiring, we know the window is full
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self.full = True
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def get_value(self, *args, **kwargs):
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raise NotImplementedError(
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"Either overwrite get_value or provide a func argument.")
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def get_transform_value(self, *args, **kwargs):
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"""Call user-defined batch-transform function passing all
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arguments.
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Note that this will only call the transform if the datapanel
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has actually been updated. Otherwise, the previously, cached
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value will be returned.
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"""
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if self.data is None:
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return None
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if self.updated:
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self.cached = self.compute_transform_value(self.data,
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*args, **kwargs)
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return self.cached
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def __call__(self, f):
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self.compute_transform_value = f
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return self.handle_data
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def batch_transform(func):
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"""Decorator function to use instead of inheriting from BatchTransform.
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For an example on how to use this, see the doc string of BatchTransform.
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"""
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def create_window(*args, **kwargs):
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# passes the user defined function to BatchTransform which it
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# will call instead of self.get_value()
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return BatchTransform(*args, func=func, **kwargs)
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return create_window
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