zipline.data.BcolzMinuteBarWriter.first_trading_day() - Code Metrics - Inspection of "Q 2.0" - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#858)

by Eddie

created 2016-01-06 18:38 UTC

first_trading_day() A

↳ Parent: zipline.data.BcolzMinuteBarWriter

Complexity

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Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	1
dl	0
loc	3
rs	10

# 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 abc import (
    ABCMeta,
    abstractmethod,
)
import bcolz
import json
import os
from bcolz import ctable
from datetime import datetime
import numpy as np
from numpy import float64
from os.path import join
import pandas as pd
from pandas import read_csv
from six import with_metaclass

from zipline.finance.trading import TradingEnvironment
from zipline.utils import tradingcalendar

MINUTES_PER_DAY = 390

_writer_env = TradingEnvironment()

_NANOS_IN_MINUTE = 60000000000

METADATA_FILENAME = 'metadata.json'


def write_metadata(directory, first_trading_day):
    metadata_path = os.path.join(directory, METADATA_FILENAME)

    metadata = {
        'first_trading_day': str(first_trading_day.date())
    }

    with open(metadata_path, 'w') as fp:
        json.dump(metadata, fp)


def _bcolz_minute_index(trading_days):
    minutes = np.zeros(len(trading_days) * MINUTES_PER_DAY,
                       dtype='datetime64[ns]')
    market_opens = tradingcalendar.open_and_closes.market_open
    mask = market_opens.index.slice_indexer(start=trading_days[0],
                                            end=trading_days[-1])
    opens = market_opens[mask]

    deltas = np.arange(0, MINUTES_PER_DAY, dtype='timedelta64[m]')
    for i, market_open in enumerate(opens):
        start = market_open.asm8
        minute_values = start + deltas
        start_ix = MINUTES_PER_DAY * i
        end_ix = start_ix + MINUTES_PER_DAY
        minutes[start_ix:end_ix] = minute_values
    return pd.to_datetime(minutes, utc=True, box=True)


class BcolzMinuteBarWriter(with_metaclass(ABCMeta)):
    """
    Class capable of writing minute OHLCV data to disk into bcolz format.
    """
    @property
    def first_trading_day(self):
        return self._first_trading_day

    @abstractmethod
    def gen_frames(self, assets):
        """
        Return an iterator of pairs of (asset_id, pd.dataframe).
        """
        raise NotImplementedError()

    def write(self, directory, assets, sid_path_func=None):
        _iterator = self.gen_frames(assets)

        return self._write_internal(directory, _iterator,
                                    sid_path_func=sid_path_func)

    def full_minutes_for_days(self, dt1, dt2):
        start_date = _writer_env.normalize_date(dt1)
        end_date = _writer_env.normalize_date(dt2)

        trading_days = _writer_env.days_in_range(start_date, end_date)
        return _bcolz_minute_index(trading_days)

    def _write_internal(self, directory, iterator, sid_path_func=None):
        first_trading_day = self.first_trading_day

        write_metadata(directory, first_trading_day)

        first_open = pd.Timestamp(
            datetime(
                year=first_trading_day.year,
                month=first_trading_day.month,
                day=first_trading_day.day,
                hour=9,
                minute=31
            ), tz='US/Eastern').tz_convert('UTC')

        all_minutes = None

        for asset_id, df in iterator:
            if sid_path_func is None:
                path = join(directory, "{0}.bcolz".format(asset_id))
            else:
                path = sid_path_func(directory, asset_id)

            os.makedirs(path)

            last_dt = df.index[-1]

            if all_minutes is None:
                all_minutes = \
                    self.full_minutes_for_days(first_open, last_dt)
                minutes = all_minutes
            else:
                if df.index[-1] in all_minutes:
                    mask = all_minutes.slice_indexer(end=last_dt)
                    minutes = all_minutes[mask]
                else:
                    # Need to extend all minutes from open after last value
                    # in all_minutes to the last_dt.
                    next_open, _ = _writer_env.next_open_and_close(
                        all_minutes[-1])
                    to_append = self.full_minutes_for_days(next_open, last_dt)
                    all_minutes = all_minutes.append(to_append)
                    minutes = all_minutes

            minutes_count = len(minutes)

            open_col = np.zeros(minutes_count, dtype=np.uint32)
            high_col = np.zeros(minutes_count, dtype=np.uint32)
            low_col = np.zeros(minutes_count, dtype=np.uint32)
            close_col = np.zeros(minutes_count, dtype=np.uint32)
            vol_col = np.zeros(minutes_count, dtype=np.uint32)

            dt_ixs = np.searchsorted(minutes.values, df.index.values)

            open_col[dt_ixs] = df.open.values.astype(np.uint32)
            high_col[dt_ixs] = df.high.values.astype(np.uint32)
            low_col[dt_ixs] = df.low.values.astype(np.uint32)
            close_col[dt_ixs] = df.close.values.astype(np.uint32)
            vol_col[dt_ixs] = df.volume.values.astype(np.uint32)

            ctable(
                columns=[
                    open_col,
                    high_col,
                    low_col,
                    close_col,
                    vol_col,
                ],
                names=[
                    "open",
                    "high",
                    "low",
                    "close",
                    "volume",
                ],
                rootdir=path,
                mode='w'
            )


class MinuteBarWriterFromDataFrames(BcolzMinuteBarWriter):
    _csv_dtypes = {
        'open': float64,
        'high': float64,
        'low': float64,
        'close': float64,
        'volume': float64,
    }

    def __init__(self, first_trading_day):
        self._first_trading_day = first_trading_day

    def gen_frames(self, assets):
        for asset in assets:
            df = assets[asset]
            yield asset, df.set_index("minute")


class MinuteBarWriterFromCSVs(BcolzMinuteBarWriter):
    """
    BcolzMinuteBarWriter constructed from a map of CSVs to assets.

    Parameters
    ----------
    asset_map: dict
        A map from asset_id -> path to csv with data for that asset.

    CSVs should have the following columns:
        minute : datetime64
        open : float64
        high : float64
        low : float64
        close : float64
        volume : int64
    """
    _csv_dtypes = {
        'open': float64,
        'high': float64,
        'low': float64,
        'close': float64,
        'volume': float64,
    }

    def __init__(self, asset_map, first_trading_day):
        self._asset_map = asset_map
        self._first_trading_day = first_trading_day

    def gen_frames(self, assets):
        """
        Read CSVs as DataFrames from our asset map.
        """
        dtypes = self._csv_dtypes

        for asset in assets:
            path = self._asset_map.get(asset)
            if path is None:
                raise KeyError("No path supplied for asset %s" % asset)
            df = read_csv(path, parse_dates=['minute'], dtype=dtypes)
            df = df.set_index("minute").tz_localize("UTC")

            yield asset, df


class BcolzMinuteBarReader(object):

    def __init__(self, rootdir, sid_path_func=None):
        self.rootdir = rootdir

        metadata = self._get_metadata()

        self.first_trading_day = pd.Timestamp(
            metadata['first_trading_day'], tz='UTC')
        mask = tradingcalendar.trading_days.slice_indexer(
            self.first_trading_day)
        # TODO: Read/write calendar to match daily, so that calendar is not
        # 'hardcoded'.
        self.trading_days = tradingcalendar.trading_days[mask]
        self._sid_path_func = sid_path_func

        self._carrays = {
            'open': {},
            'high': {},
            'low': {},
            'close': {},
            'volume': {},
            'sid': {},
            'dt': {},
        }

        self._minute_index = _bcolz_minute_index(self.trading_days)

    def _get_metadata(self):
        with open(os.path.join(self.rootdir, METADATA_FILENAME)) as fp:
            return json.load(fp)

    def _get_ctable(self, asset):
        sid = int(asset)
        if self._sid_path_func is not None:
            path = self._sid_path_func(self.rootdir, sid)
        else:
            path = "{0}/{1}.bcolz".format(self.rootdir, sid)

        return bcolz.open(path, mode='r')

    def get_last_traded_dt(self, asset, dt):
        minute_pos = self._find_last_traded_position(asset, dt)
        if minute_pos == -1:
            return pd.NaT
        return self._minute_index[minute_pos]

    def _find_last_traded_position(self, asset, dt):
        volumes = self._open_minute_file('volume', asset)
        start_date = asset.start_date
        _minute_index = self._minute_index

        minute_pos = self._find_position_of_minute(dt)

        while True:
            dt = _minute_index[minute_pos]
            if dt < start_date:
                return -1
            if minute_pos == 0 or volumes[minute_pos] != 0:
                return minute_pos
            minute_pos -= 1

    def _find_position_of_minute(self, minute_dt):
        """
        Internal method that returns the position of the given minute in the
        list of every trading minute since market open of the first trading
        day.

        IMPORTANT: This method assumes every day is 390 minutes long, even
        early closes.  Our minute bcolz files are generated like this to
        support fast lookup.

        ex. this method would return 2 for 1/2/2002 9:32 AM Eastern, if
        1/2/2002 is the first trading day of the dataset.

        Parameters
        ----------
        minute_dt: pd.Timestamp
            The minute whose position should be calculated.

        Returns
        -------
        The position of the given minute in the list of all trading minutes
        since market open on the first trading day.
        """
        return self._minute_index.get_loc(minute_dt)

    def _open_minute_file(self, field, asset):
        sid_str = str(int(asset))

        try:
            carray = self._carrays[field][sid_str]
        except KeyError:
            carray = self._carrays[field][sid_str] = \
                self._get_ctable(asset)[field]

        return carray


1			# Copyright 2015 Quantopian, Inc.
2			#
3			# Licensed under the Apache License, Version 2.0 (the "License");
4			# you may not use this file except in compliance with the License.
5			# You may obtain a copy of the License at
6			#
7			# http://www.apache.org/licenses/LICENSE-2.0
8			#
9			# Unless required by applicable law or agreed to in writing, software
10			# distributed under the License is distributed on an "AS IS" BASIS,
11			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12			# See the License for the specific language governing permissions and
13			# limitations under the License.
14
15			from abc import (
16			ABCMeta,
17			abstractmethod,
18			)
19			import bcolz
20			import json
21			import os
22			from bcolz import ctable
23			from datetime import datetime
24			import numpy as np
25			from numpy import float64
26			from os.path import join
27			import pandas as pd
28			from pandas import read_csv
29			from six import with_metaclass
30
31			from zipline.finance.trading import TradingEnvironment
32			from zipline.utils import tradingcalendar
33
34			MINUTES_PER_DAY = 390
35
36			_writer_env = TradingEnvironment()
37
38			_NANOS_IN_MINUTE = 60000000000
39
40			METADATA_FILENAME = 'metadata.json'
41
42
43			def write_metadata(directory, first_trading_day):
44			metadata_path = os.path.join(directory, METADATA_FILENAME)
45
46			metadata = {
47			'first_trading_day': str(first_trading_day.date())
48			}
49
50			with open(metadata_path, 'w') as fp:
51			json.dump(metadata, fp)
52
53
54			def _bcolz_minute_index(trading_days):
55			minutes = np.zeros(len(trading_days) * MINUTES_PER_DAY,
56			dtype='datetime64[ns]')
57			market_opens = tradingcalendar.open_and_closes.market_open
58			mask = market_opens.index.slice_indexer(start=trading_days[0],
59			end=trading_days[-1])
60			opens = market_opens[mask]
61
62			deltas = np.arange(0, MINUTES_PER_DAY, dtype='timedelta64[m]')
63			for i, market_open in enumerate(opens):
64			start = market_open.asm8
65			minute_values = start + deltas
66			start_ix = MINUTES_PER_DAY * i
67			end_ix = start_ix + MINUTES_PER_DAY
68			minutes[start_ix:end_ix] = minute_values
69			return pd.to_datetime(minutes, utc=True, box=True)
70
71
72			class BcolzMinuteBarWriter(with_metaclass(ABCMeta)):
73			"""
74			Class capable of writing minute OHLCV data to disk into bcolz format.
75			"""
76			@property
77			def first_trading_day(self):
78			return self._first_trading_day
79
80			@abstractmethod
81			def gen_frames(self, assets):
82			"""
83			Return an iterator of pairs of (asset_id, pd.dataframe).
84			"""
85			raise NotImplementedError()
86
87			def write(self, directory, assets, sid_path_func=None):
88			_iterator = self.gen_frames(assets)
89
90			return self._write_internal(directory, _iterator,
91			sid_path_func=sid_path_func)
92
93			def full_minutes_for_days(self, dt1, dt2):
94			start_date = _writer_env.normalize_date(dt1)
95			end_date = _writer_env.normalize_date(dt2)
96
97			trading_days = _writer_env.days_in_range(start_date, end_date)
98			return _bcolz_minute_index(trading_days)
99
100			def _write_internal(self, directory, iterator, sid_path_func=None):
101			first_trading_day = self.first_trading_day
102
103			write_metadata(directory, first_trading_day)
104
105			first_open = pd.Timestamp(
106			datetime(
107			year=first_trading_day.year,
108			month=first_trading_day.month,
109			day=first_trading_day.day,
110			hour=9,
111			minute=31
112			), tz='US/Eastern').tz_convert('UTC')
113
114			all_minutes = None
115
116			for asset_id, df in iterator:
117			if sid_path_func is None:
118			path = join(directory, "{0}.bcolz".format(asset_id))
119			else:
120			path = sid_path_func(directory, asset_id)
121
122			os.makedirs(path)
123
124			last_dt = df.index[-1]
125
126			if all_minutes is None:
127			all_minutes = \
128			self.full_minutes_for_days(first_open, last_dt)
129			minutes = all_minutes
130			else:
131			if df.index[-1] in all_minutes:
132			mask = all_minutes.slice_indexer(end=last_dt)
133			minutes = all_minutes[mask]
134			else:
135			# Need to extend all minutes from open after last value
136			# in all_minutes to the last_dt.
137			next_open, _ = _writer_env.next_open_and_close(
138			all_minutes[-1])
139			to_append = self.full_minutes_for_days(next_open, last_dt)
140			all_minutes = all_minutes.append(to_append)
141			minutes = all_minutes
142
143			minutes_count = len(minutes)
144
145			open_col = np.zeros(minutes_count, dtype=np.uint32)
146			high_col = np.zeros(minutes_count, dtype=np.uint32)
147			low_col = np.zeros(minutes_count, dtype=np.uint32)
148			close_col = np.zeros(minutes_count, dtype=np.uint32)
149			vol_col = np.zeros(minutes_count, dtype=np.uint32)
150
151			dt_ixs = np.searchsorted(minutes.values, df.index.values)
152
153			open_col[dt_ixs] = df.open.values.astype(np.uint32)
154			high_col[dt_ixs] = df.high.values.astype(np.uint32)
155			low_col[dt_ixs] = df.low.values.astype(np.uint32)
156			close_col[dt_ixs] = df.close.values.astype(np.uint32)
157			vol_col[dt_ixs] = df.volume.values.astype(np.uint32)
158
159			ctable(
160			columns=[
161			open_col,
162			high_col,
163			low_col,
164			close_col,
165			vol_col,
166			],
167			names=[
168			"open",
169			"high",
170			"low",
171			"close",
172			"volume",
173			],
174			rootdir=path,
175			mode='w'
176			)
177
178
179			class MinuteBarWriterFromDataFrames(BcolzMinuteBarWriter):
180			_csv_dtypes = {
181			'open': float64,
182			'high': float64,
183			'low': float64,
184			'close': float64,
185			'volume': float64,
186			}
187
188			def __init__(self, first_trading_day):
189			self._first_trading_day = first_trading_day
190
191			def gen_frames(self, assets):
192			for asset in assets:
193			df = assets[asset]
194			yield asset, df.set_index("minute")
195
196
197			class MinuteBarWriterFromCSVs(BcolzMinuteBarWriter):
198			"""
199			BcolzMinuteBarWriter constructed from a map of CSVs to assets.
200
201			Parameters
202			----------
203			asset_map: dict
204			A map from asset_id -> path to csv with data for that asset.
205
206			CSVs should have the following columns:
207			minute : datetime64
208			open : float64
209			high : float64
210			low : float64
211			close : float64
212			volume : int64
213			"""
214			_csv_dtypes = {
215			'open': float64,
216			'high': float64,
217			'low': float64,
218			'close': float64,
219			'volume': float64,
220			}
221
222			def __init__(self, asset_map, first_trading_day):
223			self._asset_map = asset_map
224			self._first_trading_day = first_trading_day
225
226			def gen_frames(self, assets):
227			"""
228			Read CSVs as DataFrames from our asset map.
229			"""
230			dtypes = self._csv_dtypes
231
232			for asset in assets:
233			path = self._asset_map.get(asset)
234			if path is None:
235			raise KeyError("No path supplied for asset %s" % asset)
236			df = read_csv(path, parse_dates=['minute'], dtype=dtypes)
237			df = df.set_index("minute").tz_localize("UTC")
238
239			yield asset, df
240
241
242			class BcolzMinuteBarReader(object):
243
244			def __init__(self, rootdir, sid_path_func=None):
245			self.rootdir = rootdir
246
247			metadata = self._get_metadata()
248
249			self.first_trading_day = pd.Timestamp(
250			metadata['first_trading_day'], tz='UTC')
251			mask = tradingcalendar.trading_days.slice_indexer(
252			self.first_trading_day)
253			# TODO: Read/write calendar to match daily, so that calendar is not
254			# 'hardcoded'.
255			self.trading_days = tradingcalendar.trading_days[mask]
256			self._sid_path_func = sid_path_func
257
258			self._carrays = {
259			'open': {},
260			'high': {},
261			'low': {},
262			'close': {},
263			'volume': {},
264			'sid': {},
265			'dt': {},
266			}
267
268			self._minute_index = _bcolz_minute_index(self.trading_days)
269
270			def _get_metadata(self):
271			with open(os.path.join(self.rootdir, METADATA_FILENAME)) as fp:
272			return json.load(fp)
273
274			def _get_ctable(self, asset):
275			sid = int(asset)
276			if self._sid_path_func is not None:
277			path = self._sid_path_func(self.rootdir, sid)
278			else:
279			path = "{0}/{1}.bcolz".format(self.rootdir, sid)
280
281			return bcolz.open(path, mode='r')
282
283			def get_last_traded_dt(self, asset, dt):
284			minute_pos = self._find_last_traded_position(asset, dt)
285			if minute_pos == -1:
286			return pd.NaT
287			return self._minute_index[minute_pos]
288
289			def _find_last_traded_position(self, asset, dt):
290			volumes = self._open_minute_file('volume', asset)
291			start_date = asset.start_date
292			_minute_index = self._minute_index
293
294			minute_pos = self._find_position_of_minute(dt)
295
296			while True:
297			dt = _minute_index[minute_pos]
298			if dt < start_date:
299			return -1
300			if minute_pos == 0 or volumes[minute_pos] != 0:
301			return minute_pos
302			minute_pos -= 1
303
304			def _find_position_of_minute(self, minute_dt):
305			"""
306			Internal method that returns the position of the given minute in the
307			list of every trading minute since market open of the first trading
308			day.
309
310			IMPORTANT: This method assumes every day is 390 minutes long, even
311			early closes. Our minute bcolz files are generated like this to
312			support fast lookup.
313
314			ex. this method would return 2 for 1/2/2002 9:32 AM Eastern, if
315			1/2/2002 is the first trading day of the dataset.
316
317			Parameters
318			----------
319			minute_dt: pd.Timestamp
320			The minute whose position should be calculated.
321
322			Returns
323			-------
324			The position of the given minute in the list of all trading minutes
325			since market open on the first trading day.
326			"""
327			return self._minute_index.get_loc(minute_dt)
328
329			def _open_minute_file(self, field, asset):
330			sid_str = str(int(asset))
331
332			try:
333			carray = self._carrays[field][sid_str]
334			except KeyError:
335			carray = self._carrays[field][sid_str] = \
336			self._get_ctable(asset)[field]
337
338			return carray
339

quantopian / zipline

Pull Request — master (#858)

first_trading_day() A

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