# Copyright 2015 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.
from numpy import (
iinfo,
uint32,
)
from trading_calendars import get_calendar
from zipline.data.us_equity_pricing import (
BcolzDailyBarReader,
SQLiteAdjustmentReader,
)
from zipline.lib.adjusted_array import AdjustedArray
from .base import PipelineLoader
from .utils import shift_dates
UINT32_MAX = iinfo(uint32).max
[docs]class USEquityPricingLoader(PipelineLoader):
"""
PipelineLoader for US Equity Pricing data
Delegates loading of baselines and adjustments.
"""
[docs] def __init__(self, raw_price_loader, adjustments_loader):
self.raw_price_loader = raw_price_loader
self.adjustments_loader = adjustments_loader
cal = self.raw_price_loader.trading_calendar or \
get_calendar("NYSE")
self._all_sessions = cal.all_sessions
[docs] @classmethod
def from_files(cls, pricing_path, adjustments_path):
"""
Create a loader from a bcolz equity pricing dir and a SQLite
adjustments path.
Parameters
----------
pricing_path : str
Path to a bcolz directory written by a BcolzDailyBarWriter.
adjusments_path : str
Path to an adjusments db written by a SQLiteAdjustmentWriter.
"""
return cls(
BcolzDailyBarReader(pricing_path),
SQLiteAdjustmentReader(adjustments_path)
)
def load_adjusted_array(self, columns, dates, assets, mask):
# load_adjusted_array is called with dates on which the user's algo
# will be shown data, which means we need to return the data that would
# be known at the start of each date. We assume that the latest data
# known on day N is the data from day (N - 1), so we shift all query
# dates back by a day.
start_date, end_date = shift_dates(
self._all_sessions, dates[0], dates[-1], shift=1,
)
colnames = [c.name for c in columns]
raw_arrays = self.raw_price_loader.load_raw_arrays(
colnames,
start_date,
end_date,
assets,
)
adjustments = self.adjustments_loader.load_adjustments(
colnames,
dates,
assets,
)
out = {}
for c, c_raw, c_adjs in zip(columns, raw_arrays, adjustments):
out[c] = AdjustedArray(
c_raw.astype(c.dtype),
c_adjs,
c.missing_value,
)
return out