zipline.sources.BenchmarkSource._initialize_precalculated_series() - 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 18:40 UTC

_initialize_precalculated_series() B

↳ Parent: zipline.sources.BenchmarkSource

Complexity

Conditions

Size

Total Lines

108

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	6
dl	0
loc	108
rs	7.0769

How to fix Long Method

#
# 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 zipline.errors import (
    InvalidBenchmarkAsset,
    BenchmarkAssetNotAvailableTooEarly,
    BenchmarkAssetNotAvailableTooLate
)


class BenchmarkSource(object):
    def __init__(self, benchmark_sid, env, trading_days, data_portal,
                 emission_rate="daily"):
        self.benchmark_sid = benchmark_sid
        self.env = env
        self.trading_days = trading_days
        self.emission_rate = emission_rate
        self.data_portal = data_portal

        if self.benchmark_sid:
            self.benchmark_asset = self.env.asset_finder.retrieve_asset(
                self.benchmark_sid)

            self._validate_benchmark()

        self.precalculated_series = \
            self._initialize_precalculated_series(
                self.benchmark_sid,
                self.env,
                self.trading_days,
                self.data_portal
            )

    def get_value(self, dt):
        return self.precalculated_series.loc[dt]

    def _validate_benchmark(self):
        # check if this security has a stock dividend.  if so, raise an
        # error suggesting that the user pick a different asset to use
        # as benchmark.
        stock_dividends = \
            self.data_portal.get_stock_dividends(self.benchmark_sid,
                                                 self.trading_days)

        if len(stock_dividends) > 0:
            raise InvalidBenchmarkAsset(
                sid=str(self.benchmark_sid),
                dt=stock_dividends[0]["ex_date"]
            )

        if self.benchmark_asset.start_date > self.trading_days[0]:
            # the asset started trading after the first simulation day
            raise BenchmarkAssetNotAvailableTooEarly(
                sid=str(self.benchmark_sid),
                dt=self.trading_days[0],
                start_dt=self.benchmark_asset.start_date
            )

        if self.benchmark_asset.end_date < self.trading_days[-1]:
            # the asset stopped trading before the last simulation day
            raise BenchmarkAssetNotAvailableTooLate(
                sid=str(self.benchmark_sid),
                dt=self.trading_days[0],
                end_dt=self.benchmark_asset.end_date
            )

    def _initialize_precalculated_series(self, sid, env, trading_days,
                                         data_portal):
        """
        Internal method that precalculates the benchmark return series for
        use in the simulation.

        Parameters
        ----------
        sid: (int) Asset to use

        env: TradingEnvironment

        trading_days: pd.DateTimeIndex

        data_portal: DataPortal

        Notes
        -----
        If the benchmark asset started trading after the simulation start,
        or finished trading before the simulation end, exceptions are raised.

        If the benchmark asset started trading the same day as the simulation
        start, the first available minute price on that day is used instead
        of the previous close.

        We use history to get an adjusted price history for each day's close,
        as of the look-back date (the last day of the simulation).  Prices are
        fully adjusted for dividends, splits, and mergers.

        Returns
        -------
        A pd.Series, indexed by trading day, whose values represent the %
        change from close to close.
        """
        if sid is None:
            # get benchmark info from trading environment, which defaults to
            # downloading data from Yahoo.
            daily_series = \
                env.benchmark_returns[trading_days[0]:trading_days[-1]]

            if self.emission_rate == "minute":
                # we need to take the env's benchmark returns, which are daily,
                # and resample them to minute
                minutes = env.minutes_for_days_in_range(
                    start=trading_days[0],
                    end=trading_days[-1]
                )

                minute_series = daily_series.reindex(
                    index=minutes,
                    method="ffill"
                )

                return minute_series
            else:
                return daily_series
        elif self.emission_rate == "minute":
            minutes = env.minutes_for_days_in_range(self.trading_days[0],
                                                    self.trading_days[-1])
            benchmark_series = data_portal.get_history_window(
                [sid],
                minutes[-1],
                bar_count=len(minutes) + 1,
                frequency="1m",
                field="price",
                ffill=True
            )

            return benchmark_series.pct_change()[1:]
        else:
            start_date = env.asset_finder.retrieve_asset(sid).start_date
            if start_date < trading_days[0]:
                # get the window of close prices for benchmark_sid from the
                # last trading day of the simulation, going up to one day
                # before the simulation start day (so that we can get the %
                # change on day 1)
                benchmark_series = data_portal.get_history_window(
                    [sid],
                    trading_days[-1],
                    bar_count=len(trading_days) + 1,
                    frequency="1d",
                    field="price",
                    ffill=True
                )[sid]
                return benchmark_series.pct_change()[1:]
            elif start_date == trading_days[0]:
                # Attempt to handle case where stock data starts on first
                # day, in this case use the open to close return.
                benchmark_series = data_portal.get_history_window(
                    [sid],
                    trading_days[-1],
                    bar_count=len(trading_days),
                    frequency="1d",
                    field="price",
                    ffill=True
                )[sid]

                # get a minute history window of the first day
                first_open = data_portal.get_spot_value(
                    sid, 'open', trading_days[0], 'daily')
                first_close = data_portal.get_spot_value(
                    sid, 'close', trading_days[0], 'daily')

                first_day_return = (first_close - first_open) / first_open

                returns = benchmark_series.pct_change()[:]
                returns[0] = first_day_return
                return returns


1			#
2			# Copyright 2015 Quantopian, Inc.
3			#
4			# Licensed under the Apache License, Version 2.0 (the "License");
5			# you may not use this file except in compliance with the License.
6			# You may obtain a copy of the License at
7			#
8			# http://www.apache.org/licenses/LICENSE-2.0
9			#
10			# Unless required by applicable law or agreed to in writing, software
11			# distributed under the License is distributed on an "AS IS" BASIS,
12			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13			# See the License for the specific language governing permissions and
14			# limitations under the License.
15
16			from zipline.errors import (
17			InvalidBenchmarkAsset,
18			BenchmarkAssetNotAvailableTooEarly,
19			BenchmarkAssetNotAvailableTooLate
20			)
21
22
23			class BenchmarkSource(object):
24			def __init__(self, benchmark_sid, env, trading_days, data_portal,
25			emission_rate="daily"):
26			self.benchmark_sid = benchmark_sid
27			self.env = env
28			self.trading_days = trading_days
29			self.emission_rate = emission_rate
30			self.data_portal = data_portal
31
32			if self.benchmark_sid:
33			self.benchmark_asset = self.env.asset_finder.retrieve_asset(
34			self.benchmark_sid)
35
36			self._validate_benchmark()
37
38			self.precalculated_series = \
39			self._initialize_precalculated_series(
40			self.benchmark_sid,
41			self.env,
42			self.trading_days,
43			self.data_portal
44			)
45
46			def get_value(self, dt):
47			return self.precalculated_series.loc[dt]
48
49			def _validate_benchmark(self):
50			# check if this security has a stock dividend. if so, raise an
51			# error suggesting that the user pick a different asset to use
52			# as benchmark.
53			stock_dividends = \
54			self.data_portal.get_stock_dividends(self.benchmark_sid,
55			self.trading_days)
56
57			if len(stock_dividends) > 0:
58			raise InvalidBenchmarkAsset(
59			sid=str(self.benchmark_sid),
60			dt=stock_dividends[0]["ex_date"]
61			)
62
63			if self.benchmark_asset.start_date > self.trading_days[0]:
64			# the asset started trading after the first simulation day
65			raise BenchmarkAssetNotAvailableTooEarly(
66			sid=str(self.benchmark_sid),
67			dt=self.trading_days[0],
68			start_dt=self.benchmark_asset.start_date
69			)
70
71			if self.benchmark_asset.end_date < self.trading_days[-1]:
72			# the asset stopped trading before the last simulation day
73			raise BenchmarkAssetNotAvailableTooLate(
74			sid=str(self.benchmark_sid),
75			dt=self.trading_days[0],
76			end_dt=self.benchmark_asset.end_date
77			)
78
79			def _initialize_precalculated_series(self, sid, env, trading_days,
80			data_portal):
81			"""
82			Internal method that precalculates the benchmark return series for
83			use in the simulation.
84
85			Parameters
86			----------
87			sid: (int) Asset to use
88
89			env: TradingEnvironment
90
91			trading_days: pd.DateTimeIndex
92
93			data_portal: DataPortal
94
95			Notes
96			-----
97			If the benchmark asset started trading after the simulation start,
98			or finished trading before the simulation end, exceptions are raised.
99
100			If the benchmark asset started trading the same day as the simulation
101			start, the first available minute price on that day is used instead
102			of the previous close.
103
104			We use history to get an adjusted price history for each day's close,
105			as of the look-back date (the last day of the simulation). Prices are
106			fully adjusted for dividends, splits, and mergers.
107
108			Returns
109			-------
110			A pd.Series, indexed by trading day, whose values represent the %
111			change from close to close.
112			"""
113			if sid is None:
114			# get benchmark info from trading environment, which defaults to
115			# downloading data from Yahoo.
116			daily_series = \
117			env.benchmark_returns[trading_days[0]:trading_days[-1]]
118
119			if self.emission_rate == "minute":
120			# we need to take the env's benchmark returns, which are daily,
121			# and resample them to minute
122			minutes = env.minutes_for_days_in_range(
123			start=trading_days[0],
124			end=trading_days[-1]
125			)
126
127			minute_series = daily_series.reindex(
128			index=minutes,
129			method="ffill"
130			)
131
132			return minute_series
133			else:
134			return daily_series
135			elif self.emission_rate == "minute":
136			minutes = env.minutes_for_days_in_range(self.trading_days[0],
137			self.trading_days[-1])
138			benchmark_series = data_portal.get_history_window(
139			[sid],
140			minutes[-1],
141			bar_count=len(minutes) + 1,
142			frequency="1m",
143			field="price",
144			ffill=True
145			)
146
147			return benchmark_series.pct_change()[1:]
148			else:
149			start_date = env.asset_finder.retrieve_asset(sid).start_date
150			if start_date < trading_days[0]:
151			# get the window of close prices for benchmark_sid from the
152			# last trading day of the simulation, going up to one day
153			# before the simulation start day (so that we can get the %
154			# change on day 1)
155			benchmark_series = data_portal.get_history_window(
156			[sid],
157			trading_days[-1],
158			bar_count=len(trading_days) + 1,
159			frequency="1d",
160			field="price",
161			ffill=True
162			)[sid]
163			return benchmark_series.pct_change()[1:]
164			elif start_date == trading_days[0]:
165			# Attempt to handle case where stock data starts on first
166			# day, in this case use the open to close return.
167			benchmark_series = data_portal.get_history_window(
168			[sid],
169			trading_days[-1],
170			bar_count=len(trading_days),
171			frequency="1d",
172			field="price",
173			ffill=True
174			)[sid]
175
176			# get a minute history window of the first day
177			first_open = data_portal.get_spot_value(
178			sid, 'open', trading_days[0], 'daily')
179			first_close = data_portal.get_spot_value(
180			sid, 'close', trading_days[0], 'daily')
181
182			first_day_return = (first_close - first_open) / first_open
183
184			returns = benchmark_series.pct_change()[:]
185			returns[0] = first_day_return
186			return returns
187

quantopian / zipline

Pull Request — master (#858)

_initialize_precalculated_series() B

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Duplication

How to fix Long Method

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