mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 09:37:54 +08:00
Eddie Hebert
4c761c6708
The call to `Panel.dropna` after the fillna was deleting all values, if a stock stopped trading mid run and thus provided volume 0. i.e. if any sid had 0 non-null values the entire panel of frames would be truncated. It's possible to avoid the collapse via by adding the `how='all'` flag to `dropna`, however with the current tick based creation of the panel, the `dropna` with `how='all'` should be functionally equivalent to not dropping at all. The dropna has been dropped in favor of leaving the drop to algorithm code.
546 lines
19 KiB
Python
546 lines
19 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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from numbers import Integral
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import pandas as pd
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from zipline.protocol import Event, DATASOURCE_TYPE
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from zipline.gens.utils import assert_sort_unframe_protocol, hash_args
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import zipline.finance.trading as trading
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log = logbook.Logger('Transform')
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class UnsupportedEventWindowFlagValue(Exception):
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"""
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Error state when an EventWindow option is attempted to be set
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to a value that is no longer supported by the library.
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This is to help enforce deprecation of the market_aware and delta flags,
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without completely removing it and breaking existing algorithms.
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"""
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pass
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class InvalidWindowLength(Exception):
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"""
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Error raised when the window length is unusable.
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"""
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pass
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class TransformMessage(object):
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pass
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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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# 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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# we only handle TRADE events.
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if (hasattr(message, 'type')
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and message.type != DATASOURCE_TYPE.TRADE):
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continue
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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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tnfm_value = self.state.update(message)
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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, window_length=None, delta=None):
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self.window_length = window_length
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self.ticks = deque()
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# Only Market-aware mode is now supported.
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if not market_aware:
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raise UnsupportedEventWindowFlagValue(
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"Non-'market aware' mode is no longer supported."
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)
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if delta:
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raise UnsupportedEventWindowFlagValue(
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"delta values are no longer supported."
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)
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if self.window_length is None:
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raise InvalidWindowLength("window_length must be provided")
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if not isinstance(self.window_length, Integral):
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raise InvalidWindowLength(
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"window_length must be an integer-like number")
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if self.window_length == 0:
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raise InvalidWindowLength("window_length must be non-zero")
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if self.window_length < 0:
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raise InvalidWindowLength("window_length must be positive")
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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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self.drop_condition = self.out_of_market_window
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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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if hasattr(event, 'type') and event.type != DATASOURCE_TYPE.TRADE:
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return
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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 len(self.ticks) > self.window_length:
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# import nose.tools; nose.tools.set_trace()
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# Clear out any expired events.
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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 out_of_market_window(self, oldest, newest):
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oldest_index = \
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trading.environment.trading_days.searchsorted(oldest)
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newest_index = \
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trading.environment.trading_days.searchsorted(newest)
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trading_days_between = newest_index - oldest_index
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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.window_length
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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.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 your 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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window_length=None,
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clean_nans=True,
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sids=None,
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fields=None,
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create_panel=True,
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compute_only_full=True):
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"""Instantiate new batch_transform object.
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:Arguments:
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func : python function <optional>
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If supplied will be called after each refresh_period
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with the data panel and all args and kwargs supplied
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to the handle_data() call.
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refresh_period : int
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Interval to call batch_transform function.
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window_length : int
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How many days the trailing window should have.
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clean_nans : bool <default=True>
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Whether to (forward) fill in nans.
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sids : list <optional>
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Which sids to include in the moving window. If not
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supplied sids will be extracted from incoming
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events.
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fields : list <optional>
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Which fields to include in the moving window
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(e.g. 'price'). If not supplied, fields will be
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extracted from incoming events.
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create_panel : bool <default=True>
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If True, will create a pandas panel every refresh
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period and pass it to the user-defined function.
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If False, will pass the underlying deque reference
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directly to the function which will be significantly
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faster.
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compute_only_full : bool <default=True>
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Only call the user-defined function once the window is
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full. Returns None if window is not full yet.
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"""
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super(BatchTransform, self).__init__(True, window_length=window_length)
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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.clean_nans = clean_nans
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self.create_panel = create_panel
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self.compute_only_full = compute_only_full
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self.sids = sids
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if isinstance(self.sids, (basestring, Integral)):
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self.sids = [self.sids]
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self.field_names = fields
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if isinstance(self.field_names, str):
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self.field_names = [self.field_names]
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self.refresh_period = refresh_period
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self.window_length = window_length
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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.cached = None
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# Data panel that provides bar information to fill in the window,
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# when no bar ticks are available from the data source generator
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# Used in universes that 'rollover', e.g. one that has a different
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# set of stocks per quarter
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self.supplemental_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 = Event()
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event.dt = max(dts)
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event.data = {k: v.__dict__ for k, v in data.iteritems()
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# Need to check if data has a 'length' to filter
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# out sids without trade data available.
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# TODO: expose more of 'no trade available'
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# functionality to zipline
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if len(v)}
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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 _extract_field_names(self, event):
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# extract field names from sids (price, volume etc), make sure
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# every sid has the same fields.
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sid_keys = []
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for sid in event.data.itervalues():
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keys = set([name for name, value in sid.items()
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if (isinstance(value, (int, float)))])
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sid_keys.append(keys)
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assert sid_keys[0] == set.intersection(*sid_keys),\
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"Each sid must have the same keys."
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unwanted_fields = set(['portfolio', 'sid', 'dt', 'type',
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'datetime', 'source_id'])
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return sid_keys[0] - unwanted_fields
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def handle_add(self, event):
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if not self.last_dt:
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if self.field_names is None:
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self.field_names = self._extract_field_names(event)
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self.last_dt = event.dt
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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_total >= self.window_length:
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self.full = True
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if self.trading_days_since_update >= self.refresh_period:
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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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data_dict = {tick['dt']: tick['data'] for tick in self.ticks}
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data = pd.Panel(data_dict, major_axis=self.field_names,
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minor_axis=self.sids,
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dtype='float')
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if self.supplemental_data:
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# item will be a date stamp
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for item in data.items:
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try:
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data[item] = self.supplemental_data[item].combine_first(
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data[item])
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except KeyError:
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# Only filling in data available in supplemental data.
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pass
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data = data.swapaxes(0, 1)
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if self.clean_nans:
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# Fills in gaps of missing data during transform
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# of multiple stocks. E.g. we may be missing
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# minute data because of illiquidity of one stock
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data = data.fillna(method='ffill')
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# Hold on to a reference to the data,
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# so that it's easier to find the current data when stepping
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# through with a debugger
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self.curr_data = data
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return data
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def handle_remove(self, event):
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pass
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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.compute_only_full and not self.full:
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return None
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if self.updated:
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# Either create new pandas panel or pass ticks dequeue
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# directly
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data = self.get_data() if self.create_panel else self.ticks
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self.cached = self.compute_transform_value(data, *args,
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**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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|
|
|
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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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|
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return create_window
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