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https://github.com/wassname/catalyst.git
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twiecki
465 lines
17 KiB
Python
465 lines
17 KiB
Python
#
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# Copyright 2013 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 functools
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import logbook
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import numpy
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from numbers import Integral
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import pandas as pd
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from six import (
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string_types,
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itervalues,
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iteritems
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)
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from zipline.utils.data import RollingPanel
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from zipline.protocol import Event
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from zipline.finance import trading
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from . utils import check_window_length
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log = logbook.Logger('BatchTransform')
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func_map = {'open_price': 'first',
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'close_price': 'last',
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'low': 'min',
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'high': 'max',
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'volume': 'sum'
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}
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def get_sample_func(item):
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if item in func_map:
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return func_map[item]
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else:
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return 'last'
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def downsample_panel(minute_rp, daily_rp, mkt_close):
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"""
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@minute_rp is a rolling panel, which should have minutely rows
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@daily_rp is a rolling panel, which should have daily rows
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@dt is the timestamp to use when adding a frame to daily_rp
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Using the history in minute_rp, a new daily bar is created by
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downsampling. The data from the daily bar is then added to the
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daily rolling panel using add_frame.
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"""
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cur_panel = minute_rp.get_current()
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sids = minute_rp.minor_axis
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day_frame = pd.DataFrame(columns=sids, index=cur_panel.items)
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dt1 = trading.environment.normalize_date(mkt_close)
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dt2 = trading.environment.next_trading_day(mkt_close)
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by_close = functools.partial(get_date, mkt_close, dt1, dt2)
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for item in minute_rp.items:
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frame = cur_panel[item]
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func = get_sample_func(item)
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# group by trading day, using the market close of the current
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# day. If events occurred after the last close (yesterday) but
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# before today's close, group them into today.
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dframe = frame.groupby(lambda d: by_close(d)).agg(func)
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for stock in sids:
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day_frame[stock][item] = dframe[stock].ix[dt1]
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# store the frame at midnight instead of the close
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daily_rp.add_frame(dt1, day_frame)
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def get_date(mkt_close, d1, d2, d):
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if d > mkt_close:
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return d2
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else:
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return d1
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class BatchTransform(object):
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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=0,
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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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compute_only_full=True,
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bars='daily',
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downsample=False):
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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 wait between advances in the window.
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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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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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downsample : bool <default=False>
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If true, downsample bars to daily bars. Otherwise, do nothing.
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"""
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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.compute_only_full = compute_only_full
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# no need to down sample if the bars are already daily
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self.downsample = downsample and (bars == 'minute')
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# How many bars are in a day
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self.bars = bars
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if self.bars == 'daily':
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self.bars_in_day = 1
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elif self.bars == 'minute':
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self.bars_in_day = int(6.5 * 60)
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else:
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raise ValueError('%s bars not understood.' % self.bars)
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# The following logic is to allow pre-specified sid filters
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# to operate on the data, but to also allow new symbols to
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# enter the batch transform's window IFF a sid filter is not
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# specified.
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if sids is not None:
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if isinstance(sids, (string_types, Integral)):
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self.static_sids = set([sids])
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else:
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self.static_sids = set(sids)
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else:
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self.static_sids = None
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self.initial_field_names = fields
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if isinstance(self.initial_field_names, string_types):
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self.initial_field_names = [self.initial_field_names]
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self.field_names = set()
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self.refresh_period = refresh_period
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check_window_length(window_length)
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self.window_length = window_length
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self.trading_days_total = 0
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self.window = None
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self.full = False
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# Set to -inf essentially to cause update on first attempt.
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self.last_dt = pd.Timestamp('1900-1-1', tz='UTC')
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self.updated = False
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self.cached = None
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self.last_args = None
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self.last_kwargs = 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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self.rolling_panel = None
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self.daily_rolling_panel = None
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def handle_data(self, data, *args, **kwargs):
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"""
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Point of entry. Process an event frame.
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"""
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# extract dates
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dts = [event.dt for event in itervalues(data._data)]
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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 iteritems(data._data)
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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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# only modify the trailing window if this is
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# a new event. This is intended to make handle_data
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# idempotent.
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if self.last_dt < event.dt:
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self.updated = True
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self._append_to_window(event)
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else:
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self.updated = False
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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 _init_panels(self, sids):
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if self.downsample:
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self.rolling_panel = RollingPanel(self.bars_in_day,
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self.field_names, sids)
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self.daily_rolling_panel = RollingPanel(self.window_length,
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self.field_names, sids)
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else:
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self.rolling_panel = RollingPanel(self.window_length *
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self.bars_in_day,
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self.field_names, sids)
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def _append_to_window(self, event):
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self.field_names = self._get_field_names(event)
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if self.static_sids is None:
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sids = set(event.data.keys())
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else:
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sids = self.static_sids
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# the panel sent to the transform code will have
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# columns masked with this set of sids. This is how
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# we guarantee that all (and only) the sids sent to the
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# algorithm's handle_data and passed to the batch
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# transform. See the get_data method to see it applied.
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# N.B. that the underlying panel grows monotonically
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# if the set of sids changes over time.
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self.latest_sids = sids
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# Create rolling panel if not existant
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if self.rolling_panel is None:
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self._init_panels(sids)
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# Store event in rolling frame
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self.rolling_panel.add_frame(event.dt,
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pd.DataFrame(event.data,
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index=self.field_names,
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columns=sids))
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# update trading day counters
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# we may get events from non-trading sources which occurr on
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# non-trading days. The book-keeping for market close and
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# trading day counting should only consider trading days.
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if trading.environment.is_trading_day(event.dt):
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_, mkt_close = trading.environment.get_open_and_close(event.dt)
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if self.bars == 'daily':
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# Daily bars have their dt set to midnight.
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mkt_close = trading.environment.normalize_date(mkt_close)
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if event.dt == mkt_close:
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if self.downsample:
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downsample_panel(self.rolling_panel,
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self.daily_rolling_panel,
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mkt_close
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)
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self.trading_days_total += 1
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self.mkt_close = mkt_close
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self.last_dt = event.dt
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if self.trading_days_total >= self.window_length:
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self.full = True
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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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#################################################
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# Determine whether we should call the transform
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# 0. Support historical/legacy usage of '0' signaling,
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# 'update on every bar'
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if self.refresh_period == 0:
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period_signals_update = True
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else:
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# 1. Is the refresh period over?
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period_signals_update = (
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self.trading_days_total % self.refresh_period == 0)
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# 2. Have the args or kwargs been changed since last time?
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args_updated = args != self.last_args or kwargs != self.last_kwargs
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# 3. Is this a downsampled batch, and is the last event mkt close?
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downsample_ready = not self.downsample or \
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self.last_dt == self.mkt_close
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recalculate_needed = downsample_ready and \
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(args_updated or (period_signals_update and self.updated))
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###################################################
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if recalculate_needed:
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self.cached = self.compute_transform_value(
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self.get_data(),
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*args,
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**kwargs
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)
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self.last_args = args
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self.last_kwargs = kwargs
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return self.cached
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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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if self.downsample:
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data = self.daily_rolling_panel.get_current()
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else:
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data = self.rolling_panel.get_current()
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if self.supplemental_data is not None:
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for item in data.items:
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if item not in self.supplemental_data.items:
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continue
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for dt in data.major_axis:
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try:
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supplemental_for_dt = self.supplemental_data.ix[
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item, dt, :]
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except KeyError:
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# Only filling in data available in supplemental data.
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supplemental_for_dt = None
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if supplemental_for_dt is not None:
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data[item].ix[dt] = \
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supplemental_for_dt.combine_first(
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data[item].ix[dt])
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# screen out sids no longer in the multiverse
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data = data.ix[:, :, self.latest_sids]
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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 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 __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 _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 itervalues(event.data):
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keys = set([name for name, value in sid.items()
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if isinstance(value,
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(int,
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float,
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numpy.integer,
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numpy.float,
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numpy.long))
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])
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sid_keys.append(keys)
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# with CUSTOM data events, there may be different fields
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# per sid. So the allowable keys are the union of all events.
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union = set.union(*sid_keys)
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unwanted_fields = set(['portfolio', 'sid', 'dt', 'type', 'source_id'])
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return union - unwanted_fields
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def _get_field_names(self, event):
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if self.initial_field_names is not None:
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return self.initial_field_names
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else:
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self.latest_names = self._extract_field_names(event)
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return set.union(self.field_names, self.latest_names)
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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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@functools.wraps(func)
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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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