Files
catalyst/zipline/utils/data.py
T
Joe Jevnik bc0b117dc9 MAINT: make the data loading apis more consistent.
Changes BcolzDailyBarWriter to not be an abc, data is passed as an
iterator of (sid, dataframe) pairs to the write method.

Changes the AssetsDBWriter to be a single class which accepts an engine
at construction time and has a `write` method for writing dataframes for
the various tables. We no longer support writing the various other data
types, callers should coerce their data into a dataframe themselves. See
zipline.assets.synthetic for some helpers to do this.

Adds many new fixtures and updates some existing fixtures to use the new
ones:

WithDefaultDateBounds
  A fixture that provides the suite a START_DATE and END_DATE. This is
  meant to make it easy for other fixtures to synchronize their date
  ranges without depending on eachother in strange ways. For example,
  WithBcolzMinuteBarReader and WithBcolzDailyBarReader by default should
  both have data for the same dates, so they may use depend on
  WithDefaultDates without forcing a dependency between them.

WithTmpDir, WithInstanceTmpDir
  Provides the suite or individual test case a temporary directory.

WithBcolzDailyBarReader
  Provides the suite a BcolzDailyBarReader which reads from bcolz data
  written to a temporary directory. The data will be read from
  dataframes and then converted to bcolz files with
  BcolzDailyBarWriter.write

WithBcolzDailyBarReaderFromCSVs
  Provides the suite a BcolzDailyBarReader which reads from bcolz data
  written to a temporary directory. The data will be read from a
  collection of CSV files and then converted into the bcolz data through
  BcolzDailyBarWriter.write_csvs

WithBcolzMinuteBarReader
  Provides the suite a BcolzMinuteBarReader which reads from bcolz data
  written to a temporary directory. The data will be read from
  dataframes and then converted to bcolz files with
  BcolzMinuteBarWriter.write

WithAdjustmentReader
  Provides the suite a SQLiteAdjustmentReader which reads from an in
  memory sqlite database. The data will be read from dataframes and then
  converted into sqlite with SQLiteAdjustmentWriter.write

WithDataPortal
  Provides each test case a DataPortal object with data from temporary
  resources.
2016-04-15 23:46:10 -04:00

393 lines
12 KiB
Python

#
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
from copy import deepcopy
import numpy as np
import pandas as pd
def _ensure_index(x):
if not isinstance(x, pd.Index):
x = pd.Index(sorted(x))
return x
class RollingPanel(object):
"""
Preallocation strategies for rolling window over expanding data set
Restrictions: major_axis can only be a DatetimeIndex for now
"""
def __init__(self,
window,
items,
sids,
cap_multiple=2,
dtype=np.float64,
initial_dates=None):
self._pos = window
self._window = window
self.items = _ensure_index(items)
self.minor_axis = _ensure_index(sids)
self.cap_multiple = cap_multiple
self.dtype = dtype
if initial_dates is None:
self.date_buf = np.empty(self.cap, dtype='M8[ns]') * pd.NaT
elif len(initial_dates) != window:
raise ValueError('initial_dates must be of length window')
else:
self.date_buf = np.hstack(
(
initial_dates,
np.empty(
window * (cap_multiple - 1),
dtype='datetime64[ns]',
),
),
)
self.buffer = self._create_buffer()
@property
def cap(self):
return self.cap_multiple * self._window
@property
def _start_index(self):
return self._pos - self._window
@property
def start_date(self):
return self.date_buf[self._start_index]
def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
if raw:
return self.buffer.values[:, self._start_index, :]
return self.buffer.iloc[:, self._start_index, :]
def set_minor_axis(self, minor_axis):
self.minor_axis = _ensure_index(minor_axis)
self.buffer = self.buffer.reindex(minor_axis=self.minor_axis)
def set_items(self, items):
self.items = _ensure_index(items)
self.buffer = self.buffer.reindex(items=self.items)
def _create_buffer(self):
panel = pd.Panel(
items=self.items,
minor_axis=self.minor_axis,
major_axis=range(self.cap),
dtype=self.dtype,
)
return panel
def extend_back(self, missing_dts):
"""
Resizes the buffer to hold a new window with a new cap_multiple.
If cap_multiple is None, then the old cap_multiple is used.
"""
delta = len(missing_dts)
if not delta:
raise ValueError(
'missing_dts must be a non-empty index',
)
self._window += delta
self._pos += delta
self.date_buf = self.date_buf.copy()
self.date_buf.resize(self.cap)
self.date_buf = np.roll(self.date_buf, delta)
old_vals = self.buffer.values
shape = old_vals.shape
nan_arr = np.empty((shape[0], delta, shape[2]))
nan_arr.fill(np.nan)
new_vals = np.column_stack(
(nan_arr,
old_vals,
np.empty((shape[0], delta * (self.cap_multiple - 1), shape[2]))),
)
self.buffer = pd.Panel(
data=new_vals,
items=self.items,
minor_axis=self.minor_axis,
major_axis=np.arange(self.cap),
dtype=self.dtype,
)
# Fill the delta with the dates we calculated.
where = slice(self._start_index, self._start_index + delta)
self.date_buf[where] = missing_dts
def add_frame(self, tick, frame, minor_axis=None, items=None):
"""
"""
if self._pos == self.cap:
self._roll_data()
values = frame
if isinstance(frame, pd.DataFrame):
values = frame.values
self.buffer.values[:, self._pos, :] = values.astype(self.dtype)
self.date_buf[self._pos] = tick
self._pos += 1
def get_current(self, item=None, raw=False, start=None, end=None):
"""
Get a Panel that is the current data in view. It is not safe to persist
these objects because internal data might change
"""
item_indexer = slice(None)
if item:
item_indexer = self.items.get_loc(item)
start_index = self._start_index
end_index = self._pos
# get inital date window
where = slice(start_index, end_index)
current_dates = self.date_buf[where]
def convert_datelike_to_long(dt):
if isinstance(dt, pd.Timestamp):
return dt.asm8
if isinstance(dt, datetime.datetime):
return np.datetime64(dt)
return dt
# constrict further by date
if start:
start = convert_datelike_to_long(start)
start_index += current_dates.searchsorted(start)
if end:
end = convert_datelike_to_long(end)
_end = current_dates.searchsorted(end, 'right')
end_index -= len(current_dates) - _end
where = slice(start_index, end_index)
values = self.buffer.values[item_indexer, where, :]
current_dates = self.date_buf[where]
if raw:
# return copy so we can change it without side effects here
return values.copy()
major_axis = pd.DatetimeIndex(deepcopy(current_dates), tz='utc')
if values.ndim == 3:
return pd.Panel(values, self.items, major_axis, self.minor_axis,
dtype=self.dtype)
elif values.ndim == 2:
return pd.DataFrame(values, major_axis, self.minor_axis,
dtype=self.dtype)
def set_current(self, panel):
"""
Set the values stored in our current in-view data to be values of the
passed panel. The passed panel must have the same indices as the panel
that would be returned by self.get_current.
"""
where = slice(self._start_index, self._pos)
self.buffer.values[:, where, :] = panel.values
def current_dates(self):
where = slice(self._start_index, self._pos)
return pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
def _roll_data(self):
"""
Roll window worth of data up to position zero.
Save the effort of having to expensively roll at each iteration
"""
self.buffer.values[:, :self._window, :] = \
self.buffer.values[:, -self._window:, :]
self.date_buf[:self._window] = self.date_buf[-self._window:]
self._pos = self._window
@property
def window_length(self):
return self._window
class MutableIndexRollingPanel(object):
"""
A version of RollingPanel that exists for backwards compatibility with
batch_transform. This is a copy to allow behavior of RollingPanel to drift
away from this without breaking this class.
This code should be considered frozen, and should not be used in the
future. Instead, see RollingPanel.
"""
def __init__(self, window, items, sids, cap_multiple=2, dtype=np.float64):
self._pos = 0
self._window = window
self.items = _ensure_index(items)
self.minor_axis = _ensure_index(sids)
self.cap_multiple = cap_multiple
self.cap = cap_multiple * window
self.dtype = dtype
self.date_buf = np.empty(self.cap, dtype='M8[ns]')
self.buffer = self._create_buffer()
def _oldest_frame_idx(self):
return max(self._pos - self._window, 0)
def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
if raw:
return self.buffer.values[:, self._oldest_frame_idx(), :]
return self.buffer.iloc[:, self._oldest_frame_idx(), :]
def set_sids(self, sids):
self.minor_axis = _ensure_index(sids)
self.buffer = self.buffer.reindex(minor_axis=self.minor_axis)
def _create_buffer(self):
panel = pd.Panel(
items=self.items,
minor_axis=self.minor_axis,
major_axis=range(self.cap),
dtype=self.dtype,
)
return panel
def get_current(self):
"""
Get a Panel that is the current data in view. It is not safe to persist
these objects because internal data might change
"""
where = slice(self._oldest_frame_idx(), self._pos)
major_axis = pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
return pd.Panel(self.buffer.values[:, where, :], self.items,
major_axis, self.minor_axis, dtype=self.dtype)
def set_current(self, panel):
"""
Set the values stored in our current in-view data to be values of the
passed panel. The passed panel must have the same indices as the panel
that would be returned by self.get_current.
"""
where = slice(self._oldest_frame_idx(), self._pos)
self.buffer.values[:, where, :] = panel.values
def current_dates(self):
where = slice(self._oldest_frame_idx(), self._pos)
return pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
def _roll_data(self):
"""
Roll window worth of data up to position zero.
Save the effort of having to expensively roll at each iteration
"""
self.buffer.values[:, :self._window, :] = \
self.buffer.values[:, -self._window:, :]
self.date_buf[:self._window] = self.date_buf[-self._window:]
self._pos = self._window
def add_frame(self, tick, frame, minor_axis=None, items=None):
"""
"""
if self._pos == self.cap:
self._roll_data()
if isinstance(frame, pd.DataFrame):
minor_axis = frame.columns
items = frame.index
if set(minor_axis).difference(set(self.minor_axis)) or \
set(items).difference(set(self.items)):
self._update_buffer(frame)
vals = frame.T.astype(self.dtype)
self.buffer.loc[:, self._pos, :] = vals
self.date_buf[self._pos] = tick
self._pos += 1
def _update_buffer(self, frame):
# Get current frame as we only need to care about the data that is in
# the active window
old_buffer = self.get_current()
if self._pos >= self._window:
# Don't count the last major_axis entry if we're past our window,
# since it's about to roll off the end of the panel.
old_buffer = old_buffer.iloc[:, 1:, :]
nans = pd.isnull(old_buffer)
# Find minor_axes that have only nans
# Note that minor is axis 2
non_nan_cols = set(old_buffer.minor_axis[~np.all(nans, axis=(0, 1))])
# Determine new columns to be added
new_cols = set(frame.columns).difference(non_nan_cols)
# Update internal minor axis
self.minor_axis = _ensure_index(new_cols.union(non_nan_cols))
# Same for items (fields)
# Find items axes that have only nans
# Note that items is axis 0
non_nan_items = set(old_buffer.items[~np.all(nans, axis=(1, 2))])
new_items = set(frame.index).difference(non_nan_items)
self.items = _ensure_index(new_items.union(non_nan_items))
# :NOTE:
# There is a simpler and 10x faster way to do this:
#
# Reindex buffer to update axes (automatically adds nans)
# self.buffer = self.buffer.reindex(items=self.items,
# major_axis=np.arange(self.cap),
# minor_axis=self.minor_axis)
#
# However, pandas==0.12.0, for which we remain backwards compatible,
# has a bug in .reindex() that this triggers. Using .update() as before
# seems to work fine.
new_buffer = self._create_buffer()
new_buffer.update(
self.buffer.loc[non_nan_items, :, non_nan_cols])
self.buffer = new_buffer