mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-07 15:27:05 +08:00
Eddie Hebert
39f44a44f8
Though the addition of tracking mulitple values in the window is powerful, the changes broke behavior of existing algorithms by changing method signatures and names. So temporarily reverting these changes, to be pulled back in when a way to have the multiple fields tracked with the existing API is written, or a cutover of the API is figured out and determined.
114 lines
3.6 KiB
Python
114 lines
3.6 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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from numbers import Number
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from collections import defaultdict
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from math import sqrt
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from zipline.transforms.utils import EventWindow, TransformMeta
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class MovingStandardDev(object):
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"""
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Class that maintains a dicitonary from sids to
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MovingStandardDevWindows. For each sid, we maintain a the
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standard deviation of all events falling within the specified
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window.
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"""
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__metaclass__ = TransformMeta
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def __init__(self, market_aware=True, window_length=None, delta=None):
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self.market_aware = market_aware
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self.delta = delta
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self.window_length = window_length
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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.window_length and not self.delta,\
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"Market-aware mode only works with full-day windows."
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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.window_length, \
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"Non-market-aware mode requires a timedelta."
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# No way to pass arguments to the defaultdict factory, so we
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# need to define a method to generate the correct EventWindows.
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self.sid_windows = defaultdict(self.create_window)
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def create_window(self):
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"""
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Factory method for self.sid_windows.
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"""
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return MovingStandardDevWindow(
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self.market_aware,
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self.window_length,
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self.delta
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)
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def update(self, event):
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"""
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Update the event window for this event's sid. Return an ndict
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from tracked fields to moving averages.
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"""
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# This will create a new EventWindow if this is the first
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# message for this sid.
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window = self.sid_windows[event.sid]
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window.update(event)
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return window.get_stddev()
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class MovingStandardDevWindow(EventWindow):
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"""
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Iteratively calculates standard deviation for a particular sid
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over a given time window. The expected functionality of this
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class is to be instantiated inside a MovingStandardDev.
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"""
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def __init__(self, market_aware, days, delta):
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# Call the superclass constructor to set up base EventWindow
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# infrastructure.
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EventWindow.__init__(self, market_aware, days, delta)
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self.sum = 0.0
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self.sum_sqr = 0.0
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def handle_add(self, event):
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assert isinstance(event.price, Number)
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self.sum += event.price
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self.sum_sqr += event.price ** 2
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def handle_remove(self, event):
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assert isinstance(event.price, Number)
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self.sum -= event.price
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self.sum_sqr -= event.price ** 2
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def get_stddev(self):
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# Sample standard deviation is undefined for a single event or
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# no events.
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if len(self) <= 1:
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return None
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else:
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average = self.sum / len(self)
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s_squared = (self.sum_sqr - self.sum * average) \
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/ (len(self) - 1)
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stddev = sqrt(s_squared)
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return stddev
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