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fawce
2c7355a0dc
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.
Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.
================
Details:
- adding delorean to the requirements.
- made index symbol a parameter for loading the benchmark data. changed
messagepack storage to be symbol specific.
- ported risk, performance, algorithm, transforms, batch transforms
and associated tests to use simulation parameters and global environment
- factory and sim factory use global state and sim params
- factory method parameter names now reflect the class expected
91 lines
3.0 KiB
Python
91 lines
3.0 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 zipline.transforms.utils import TransformMeta
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from collections import defaultdict, deque
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class Returns(object):
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"""
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Class that maintains a dictionary from sids to the sid's
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closing price N trading days ago.
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"""
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__metaclass__ = TransformMeta
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def __init__(self, window_length):
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self.window_length = window_length
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self.mapping = defaultdict(self._create)
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def update(self, event):
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"""
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Update and return the calculated returns for this event's sid.
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"""
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tracker = self.mapping[event.sid]
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tracker.update(event)
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return tracker.returns
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def _create(self):
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return ReturnsFromPriorClose(
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self.window_length
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)
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class ReturnsFromPriorClose(object):
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"""
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Records the last N closing events for a given security as well as the
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last event for the security. When we get an event for a new day, we
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treat the last event seen as the close for the previous day.
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"""
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def __init__(self, window_length):
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self.closes = deque()
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self.last_event = None
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self.returns = 0.0
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self.window_length = window_length
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def update(self, event):
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if self.last_event:
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# Day has changed since the last event we saw. Treat
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# the last event as the closing price for its day and
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# clear out the oldest close if it has expired.
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if self.last_event.dt.date() != event.dt.date():
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self.closes.append(self.last_event)
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# We keep an event for the end of each trading day, so
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# if the number of stored events is greater than the
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# number of days we want to track, the oldest close
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# is expired and should be discarded.
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while len(self.closes) > self.window_length:
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# Pop the oldest event.
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self.closes.popleft()
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# We only generate a return value once we've seen enough days
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# to give a sensible value. Would be nice if we could query
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# db for closes prior to our initial event, but that would
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# require giving this transform database creds, which we want
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# to avoid.
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if len(self.closes) == self.window_length:
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last_close = self.closes[0].price
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change = event.price - last_close
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self.returns = change / last_close
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# the current event is now the last_event
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self.last_event = event
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