zipline.finance.performance.PositionTracker._update_asset() - 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 2015-12-18 16:20 UTC

_update_asset() B

↳ Parent: zipline.finance.performance.PositionTracker

Complexity

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Metric	Value
cc	5
dl	0
loc	27
rs	8.0896

#
# 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 __future__ import division

import logbook
import numpy as np
from collections import namedtuple
from zipline.finance.performance.position import Position
from zipline.finance.transaction import Transaction

try:
    # optional cython based OrderedDict
    from cyordereddict import OrderedDict
except ImportError:
    from collections import OrderedDict
from six import iteritems, itervalues

from zipline.protocol import Event, DATASOURCE_TYPE
from zipline.utils.serialization_utils import (
    VERSION_LABEL
)

import zipline.protocol as zp
from zipline.assets import (
    Equity, Future
)
from zipline.errors import PositionTrackerMissingAssetFinder
from . position import positiondict

log = logbook.Logger('Performance')


PositionStats = namedtuple('PositionStats',
                           ['net_exposure',
                            'gross_value',
                            'gross_exposure',
                            'short_value',
                            'short_exposure',
                            'shorts_count',
                            'long_value',
                            'long_exposure',
                            'longs_count',
                            'net_value'])


def calc_position_values(amounts,
                         last_sale_prices,
                         value_multipliers):
    iter_amount_price_multiplier = zip(
        amounts,
        last_sale_prices,
        itervalues(value_multipliers),
    )
    return [
        price * amount * multiplier for
        price, amount, multiplier in iter_amount_price_multiplier
    ]


def calc_net(values):
    # Returns 0.0 if there are no values.
    return sum(values, np.float64())


def calc_position_exposures(amounts,
                            last_sale_prices,
                            exposure_multipliers):
    iter_amount_price_multiplier = zip(
        amounts,
        last_sale_prices,
        itervalues(exposure_multipliers),
    )
    return [
        price * amount * multiplier for
        price, amount, multiplier in iter_amount_price_multiplier
    ]


def calc_long_value(position_values):
    return sum(i for i in position_values if i > 0)


def calc_short_value(position_values):
    return sum(i for i in position_values if i < 0)


def calc_long_exposure(position_exposures):
    return sum(i for i in position_exposures if i > 0)


def calc_short_exposure(position_exposures):
    return sum(i for i in position_exposures if i < 0)


def calc_longs_count(position_exposures):
    return sum(1 for i in position_exposures if i > 0)


def calc_shorts_count(position_exposures):
    return sum(1 for i in position_exposures if i < 0)


def calc_gross_exposure(long_exposure, short_exposure):
    return long_exposure + abs(short_exposure)


def calc_gross_value(long_value, short_value):
    return long_value + abs(short_value)


def calc_position_stats(positions,
                        position_value_multipliers,
                        position_exposure_multipliers):
    amounts = []
    last_sale_prices = []
    for pos in itervalues(positions):
        amounts.append(pos.amount)
        last_sale_prices.append(pos.last_sale_price)

    position_values = calc_position_values(
        amounts,
        last_sale_prices,
        position_value_multipliers
    )

    position_exposures = calc_position_exposures(
        amounts,
        last_sale_prices,
        position_exposure_multipliers
    )

    long_value = calc_long_value(position_values)
    short_value = calc_short_value(position_values)
    gross_value = calc_gross_value(long_value, short_value)
    long_exposure = calc_long_exposure(position_exposures)
    short_exposure = calc_short_exposure(position_exposures)
    gross_exposure = calc_gross_exposure(long_exposure, short_exposure)
    net_exposure = calc_net(position_exposures)
    longs_count = calc_longs_count(position_exposures)
    shorts_count = calc_shorts_count(position_exposures)
    net_value = calc_net(position_values)

    return PositionStats(
        long_value=long_value,
        gross_value=gross_value,
        short_value=short_value,
        long_exposure=long_exposure,
        short_exposure=short_exposure,
        gross_exposure=gross_exposure,
        net_exposure=net_exposure,
        longs_count=longs_count,
        shorts_count=shorts_count,
        net_value=net_value
    )


class PositionTracker(object):

    def __init__(self, asset_finder, data_portal, data_frequency):
        self.asset_finder = asset_finder

        # FIXME really want to avoid storing a data portal here,
        # but the path to get to maybe_create_close_position_transaction
        # is long and tortuous
        self._data_portal = data_portal

        # sid => position object
        self.positions = positiondict()
        # Arrays for quick calculations of positions value
        self._position_value_multipliers = OrderedDict()
        self._position_exposure_multipliers = OrderedDict()
        self._position_payout_multipliers = OrderedDict()
        self._unpaid_dividends = {}
        self._unpaid_stock_dividends = {}
        self._positions_store = zp.Positions()

        # Dict, keyed on dates, that contains lists of close position events
        # for any Assets in this tracker's positions
        self._auto_close_position_sids = {}

        self.data_frequency = data_frequency

    def _update_asset(self, sid):
        try:
            self._position_value_multipliers[sid]
            self._position_exposure_multipliers[sid]
            self._position_payout_multipliers[sid]
        except KeyError:
            # Check if there is an AssetFinder
            if self.asset_finder is None:
                raise PositionTrackerMissingAssetFinder()

            # Collect the value multipliers from applicable sids
            asset = self.asset_finder.retrieve_asset(sid)
            if isinstance(asset, Equity):
                self._position_value_multipliers[sid] = 1
                self._position_exposure_multipliers[sid] = 1
                self._position_payout_multipliers[sid] = 0
            if isinstance(asset, Future):
                self._position_value_multipliers[sid] = 0
                self._position_exposure_multipliers[sid] = \
                    asset.contract_multiplier
                self._position_payout_multipliers[sid] = \
                    asset.contract_multiplier
                # Futures auto-close timing is controlled by the Future's
                # auto_close_date property
                self._insert_auto_close_position_date(
                    dt=asset.auto_close_date,
                    sid=sid
                )

    def _insert_auto_close_position_date(self, dt, sid):
        """
        Inserts the given SID in to the list of positions to be auto-closed by
        the given dt.

        Parameters
        ----------
        dt : pandas.Timestamp
            The date before-which the given SID will be auto-closed
        sid : int
            The SID of the Asset to be auto-closed
        """
        if dt is not None:
            self._auto_close_position_sids.setdefault(dt, set()).add(sid)

    def auto_close_position_events(self, next_trading_day):
        """
        Generates CLOSE_POSITION events for any SIDs whose auto-close date is
        before or equal to the given date.

        Parameters
        ----------
        next_trading_day : pandas.Timestamp
            The time before-which certain Assets need to be closed

        Yields
        ------
        Event
            A close position event for any sids that should be closed before
            the next_trading_day parameter
        """
        past_asset_end_dates = set()

        # Check the auto_close_position_dates dict for SIDs to close
        for date, sids in self._auto_close_position_sids.items():
            if date > next_trading_day:
                continue
            past_asset_end_dates.add(date)

            for sid in sids:
                # Yield a CLOSE_POSITION event
                event = Event({
                    'dt': date,
                    'type': DATASOURCE_TYPE.CLOSE_POSITION,
                    'sid': sid,
                })
                yield event

        # Clear out past dates
        while past_asset_end_dates:
            self._auto_close_position_sids.pop(past_asset_end_dates.pop())

    def update_positions(self, positions):
        # update positions in batch
        self.positions.update(positions)
        for sid, pos in iteritems(positions):
            self._update_asset(sid)

    def update_position(self, sid, amount=None, last_sale_price=None,
                        last_sale_date=None, cost_basis=None):
        if sid not in self.positions:
            position = Position(sid)
            self.positions[sid] = position
        else:
            position = self.positions[sid]

        if amount is not None:
            position.amount = amount
            self._update_asset(sid=sid)
        if last_sale_price is not None:
            position.last_sale_price = last_sale_price
        if last_sale_date is not None:
            position.last_sale_date = last_sale_date
        if cost_basis is not None:
            position.cost_basis = cost_basis

    def execute_transaction(self, txn):
        # Update Position
        # ----------------
        sid = txn.sid

        if sid not in self.positions:
            position = Position(sid)
            self.positions[sid] = position
        else:
            position = self.positions[sid]

        position.update(txn)
        self._update_asset(sid)

    def handle_commission(self, sid, cost):
        # Adjust the cost basis of the stock if we own it
        if sid in self.positions:
            self.positions[sid].adjust_commission_cost_basis(sid, cost)

    def handle_splits(self, splits):
        """
        Processes a list of splits by modifying any positions as needed.

        Parameters
        ----------
        splits: list
            A list of splits.  Each split is a tuple of (sid, ratio).

        Returns
        -------
        None
        """
        for split in splits:
            sid = split[0]
            if sid in self.positions:
                # Make the position object handle the split. It returns the
                # leftover cash from a fractional share, if there is any.
                position = self.positions[sid]
                leftover_cash = position.handle_split(sid, split[1])
                self._update_asset(split[0])
                return leftover_cash

    def earn_dividends(self, dividends, stock_dividends):
        """
        Given a list of dividends whose ex_dates are all the next trading day,
        calculate and store the cash and/or stock payments to be paid on each
        dividend's pay date.
        """
        for dividend in dividends:
            # Store the earned dividends so that they can be paid on the
            # dividends' pay_dates.
            div_owed = self.positions[dividend.sid].earn_dividend(dividend)
            try:
                self._unpaid_dividends[dividend.pay_date].append(
                    div_owed)
            except KeyError:
                self._unpaid_dividends[dividend.pay_date] = [div_owed]

        for stock_dividend in stock_dividends:
            div_owed = self.positions[stock_dividend.sid].earn_stock_dividend(
                stock_dividend)
            try:
                self._unpaid_stock_dividends[stock_dividend.pay_date].\
                    append(div_owed)
            except KeyError:
                self._unpaid_stock_dividends[stock_dividend.pay_date] = \
                    [div_owed]

    def pay_dividends(self, next_trading_day):
        """
        Returns a cash payment based on the dividends that should be paid out
        according to the accumulated bookkeeping of earned, unpaid, and stock
        dividends.
        """
        net_cash_payment = 0.0

        try:
            payments = self._unpaid_dividends[next_trading_day]
            # Mark these dividends as paid by dropping them from our unpaid
            del self._unpaid_dividends[next_trading_day]
        except KeyError:
            payments = []

        # representing the fact that we're required to reimburse the owner of
        # the stock for any dividends paid while borrowing.
        for payment in payments:
            net_cash_payment += payment['amount']

        # Add stock for any stock dividends paid.  Again, the values here may
        # be negative in the case of short positions.

        try:
            stock_payments = self._unpaid_stock_dividends[next_trading_day]
        except:
            stock_payments = []

        for stock_payment in stock_payments:
            stock = stock_payment['payment_sid']
            share_count = stock_payment['share_count']
            # note we create a Position for stock dividend if we don't
            # already own the asset
            if stock in self.positions:
                position = self.positions[stock]
            else:
                position = self.positions[stock] = Position(stock)

            position.amount += share_count
            self._update_asset(stock)

        return net_cash_payment

    def maybe_create_close_position_transaction(self, event):
        if not self.positions.get(event.sid):
            return None

        amount = self.positions.get(event.sid).amount
        price = self._data_portal.get_spot_value(
            event.sid, 'close', event.dt, self.data_frequency)

        txn = Transaction(
            sid=event.sid,
            amount=(-1 * amount),
            dt=event.dt,
            price=price,
            commission=0,
            order_id=0
        )
        return txn

    def get_positions(self):

        positions = self._positions_store

        for sid, pos in iteritems(self.positions):

            if pos.amount == 0:
                # Clear out the position if it has become empty since the last
                # time get_positions was called.  Catching the KeyError is
                # faster than checking `if sid in positions`, and this can be
                # potentially called in a tight inner loop.
                try:
                    del positions[sid]
                except KeyError:
                    pass
                continue

            # Note that this will create a position if we don't currently have
            # an entry
            position = positions[sid]
            position.amount = pos.amount
            position.cost_basis = pos.cost_basis
            position.last_sale_price = pos.last_sale_price
            position.last_sale_date = pos.last_sale_date

        return positions

    def get_positions_list(self):
        positions = []
        for sid, pos in iteritems(self.positions):
            if pos.amount != 0:
                positions.append(pos.to_dict())
        return positions

    def sync_last_sale_prices(self, dt):
        data_portal = self._data_portal
        for sid, position in iteritems(self.positions):
            position.last_sale_price = data_portal.get_spot_value(
                sid, 'close', dt, self.data_frequency)

    def stats(self):
        return calc_position_stats(self.positions,
                                   self._position_value_multipliers,
                                   self._position_exposure_multipliers)

    def __getstate__(self):
        state_dict = {}

        state_dict['asset_finder'] = self.asset_finder
        state_dict['positions'] = dict(self.positions)
        state_dict['unpaid_dividends'] = self._unpaid_dividends
        state_dict['unpaid_stock_dividends'] = self._unpaid_stock_dividends
        state_dict['auto_close_position_sids'] = self._auto_close_position_sids
        state_dict['data_frequency'] = self.data_frequency

        STATE_VERSION = 3
        state_dict[VERSION_LABEL] = STATE_VERSION
        return state_dict

    def __setstate__(self, state):
        OLDEST_SUPPORTED_STATE = 3
        version = state.pop(VERSION_LABEL)

        if version < OLDEST_SUPPORTED_STATE:
            raise BaseException("PositionTracker saved state is too old.")

        self.asset_finder = state['asset_finder']
        self.positions = positiondict()
        self.data_frequency = state['data_frequency']
        # note that positions_store is temporary and gets regened from
        # .positions
        self._positions_store = zp.Positions()

        self._unpaid_dividends = state['unpaid_dividends']
        self._unpaid_stock_dividends = state['unpaid_stock_dividends']
        self._auto_close_position_sids = state['auto_close_position_sids']

        # Arrays for quick calculations of positions value
        self._position_value_multipliers = OrderedDict()
        self._position_exposure_multipliers = OrderedDict()
        self._position_payout_multipliers = OrderedDict()

        # Update positions is called without a finder
        self.update_positions(state['positions'])

        # FIXME
        self._data_portal = None


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 __future__ import division
17
18			import logbook
19			import numpy as np
20			from collections import namedtuple
21			from zipline.finance.performance.position import Position
22			from zipline.finance.transaction import Transaction
23
24			try:
25			# optional cython based OrderedDict
26			from cyordereddict import OrderedDict
27			except ImportError:
28			from collections import OrderedDict
29			from six import iteritems, itervalues
30
31			from zipline.protocol import Event, DATASOURCE_TYPE
32			from zipline.utils.serialization_utils import (
33			VERSION_LABEL
34			)
35
36			import zipline.protocol as zp
37			from zipline.assets import (
38			Equity, Future
39			)
40			from zipline.errors import PositionTrackerMissingAssetFinder
41			from . position import positiondict
42
43			log = logbook.Logger('Performance')
44
45
46			PositionStats = namedtuple('PositionStats',
47			['net_exposure',
48			'gross_value',
49			'gross_exposure',
50			'short_value',
51			'short_exposure',
52			'shorts_count',
53			'long_value',
54			'long_exposure',
55			'longs_count',
56			'net_value'])
57
58
59			def calc_position_values(amounts,
60			last_sale_prices,
61			value_multipliers):
62			iter_amount_price_multiplier = zip(
63			amounts,
64			last_sale_prices,
65			itervalues(value_multipliers),
66			)
67			return [
68			price * amount * multiplier for
69			price, amount, multiplier in iter_amount_price_multiplier
70			]
71
72
73			def calc_net(values):
74			# Returns 0.0 if there are no values.
75			return sum(values, np.float64())
76
77
78			def calc_position_exposures(amounts,
79			last_sale_prices,
80			exposure_multipliers):
81			iter_amount_price_multiplier = zip(
82			amounts,
83			last_sale_prices,
84			itervalues(exposure_multipliers),
85			)
86			return [
87			price * amount * multiplier for
88			price, amount, multiplier in iter_amount_price_multiplier
89			]
90
91
92			def calc_long_value(position_values):
93			return sum(i for i in position_values if i > 0)
94
95
96			def calc_short_value(position_values):
97			return sum(i for i in position_values if i < 0)
98
99
100			def calc_long_exposure(position_exposures):
101			return sum(i for i in position_exposures if i > 0)
102
103
104			def calc_short_exposure(position_exposures):
105			return sum(i for i in position_exposures if i < 0)
106
107
108			def calc_longs_count(position_exposures):
109			return sum(1 for i in position_exposures if i > 0)
110
111
112			def calc_shorts_count(position_exposures):
113			return sum(1 for i in position_exposures if i < 0)
114
115
116			def calc_gross_exposure(long_exposure, short_exposure):
117			return long_exposure + abs(short_exposure)
118
119
120			def calc_gross_value(long_value, short_value):
121			return long_value + abs(short_value)
122
123
124			def calc_position_stats(positions,
125			position_value_multipliers,
126			position_exposure_multipliers):
127			amounts = []
128			last_sale_prices = []
129			for pos in itervalues(positions):
130			amounts.append(pos.amount)
131			last_sale_prices.append(pos.last_sale_price)
132
133			position_values = calc_position_values(
134			amounts,
135			last_sale_prices,
136			position_value_multipliers
137			)
138
139			position_exposures = calc_position_exposures(
140			amounts,
141			last_sale_prices,
142			position_exposure_multipliers
143			)
144
145			long_value = calc_long_value(position_values)
146			short_value = calc_short_value(position_values)
147			gross_value = calc_gross_value(long_value, short_value)
148			long_exposure = calc_long_exposure(position_exposures)
149			short_exposure = calc_short_exposure(position_exposures)
150			gross_exposure = calc_gross_exposure(long_exposure, short_exposure)
151			net_exposure = calc_net(position_exposures)
152			longs_count = calc_longs_count(position_exposures)
153			shorts_count = calc_shorts_count(position_exposures)
154			net_value = calc_net(position_values)
155
156			return PositionStats(
157			long_value=long_value,
158			gross_value=gross_value,
159			short_value=short_value,
160			long_exposure=long_exposure,
161			short_exposure=short_exposure,
162			gross_exposure=gross_exposure,
163			net_exposure=net_exposure,
164			longs_count=longs_count,
165			shorts_count=shorts_count,
166			net_value=net_value
167			)
168
169
170			class PositionTracker(object):
171
172			def __init__(self, asset_finder, data_portal, data_frequency):
173			self.asset_finder = asset_finder
174
175			# FIXME really want to avoid storing a data portal here,
176			# but the path to get to maybe_create_close_position_transaction
177			# is long and tortuous
178			self._data_portal = data_portal
179
180			# sid => position object
181			self.positions = positiondict()
182			# Arrays for quick calculations of positions value
183			self._position_value_multipliers = OrderedDict()
184			self._position_exposure_multipliers = OrderedDict()
185			self._position_payout_multipliers = OrderedDict()
186			self._unpaid_dividends = {}
187			self._unpaid_stock_dividends = {}
188			self._positions_store = zp.Positions()
189
190			# Dict, keyed on dates, that contains lists of close position events
191			# for any Assets in this tracker's positions
192			self._auto_close_position_sids = {}
193
194			self.data_frequency = data_frequency
195
196			def _update_asset(self, sid):
197			try:
198			self._position_value_multipliers[sid]
199			self._position_exposure_multipliers[sid]
200			self._position_payout_multipliers[sid]
201			except KeyError:
202			# Check if there is an AssetFinder
203			if self.asset_finder is None:
204			raise PositionTrackerMissingAssetFinder()
205
206			# Collect the value multipliers from applicable sids
207			asset = self.asset_finder.retrieve_asset(sid)
208			if isinstance(asset, Equity):
209			self._position_value_multipliers[sid] = 1
210			self._position_exposure_multipliers[sid] = 1
211			self._position_payout_multipliers[sid] = 0
212			if isinstance(asset, Future):
213			self._position_value_multipliers[sid] = 0
214			self._position_exposure_multipliers[sid] = \
215			asset.contract_multiplier
216			self._position_payout_multipliers[sid] = \
217			asset.contract_multiplier
218			# Futures auto-close timing is controlled by the Future's
219			# auto_close_date property
220			self._insert_auto_close_position_date(
221			dt=asset.auto_close_date,
222			sid=sid
223			)
224
225			def _insert_auto_close_position_date(self, dt, sid):
226			"""
227			Inserts the given SID in to the list of positions to be auto-closed by
228			the given dt.
229
230			Parameters
231			----------
232			dt : pandas.Timestamp
233			The date before-which the given SID will be auto-closed
234			sid : int
235			The SID of the Asset to be auto-closed
236			"""
237			if dt is not None:
238			self._auto_close_position_sids.setdefault(dt, set()).add(sid)
239
240			def auto_close_position_events(self, next_trading_day):
241			"""
242			Generates CLOSE_POSITION events for any SIDs whose auto-close date is
243			before or equal to the given date.
244
245			Parameters
246			----------
247			next_trading_day : pandas.Timestamp
248			The time before-which certain Assets need to be closed
249
250			Yields
251			------
252			Event
253			A close position event for any sids that should be closed before
254			the next_trading_day parameter
255			"""
256			past_asset_end_dates = set()
257
258			# Check the auto_close_position_dates dict for SIDs to close
259			for date, sids in self._auto_close_position_sids.items():
260			if date > next_trading_day:
261			continue
262			past_asset_end_dates.add(date)
263
264			for sid in sids:
265			# Yield a CLOSE_POSITION event
266			event = Event({
267			'dt': date,
268			'type': DATASOURCE_TYPE.CLOSE_POSITION,
269			'sid': sid,
270			})
271			yield event
272
273			# Clear out past dates
274			while past_asset_end_dates:
275			self._auto_close_position_sids.pop(past_asset_end_dates.pop())
276
277			def update_positions(self, positions):
278			# update positions in batch
279			self.positions.update(positions)
280			for sid, pos in iteritems(positions):
281			self._update_asset(sid)
282
283			def update_position(self, sid, amount=None, last_sale_price=None,
284			last_sale_date=None, cost_basis=None):
285			if sid not in self.positions:
286			position = Position(sid)
287			self.positions[sid] = position
288			else:
289			position = self.positions[sid]
290
291			if amount is not None:
292			position.amount = amount
293			self._update_asset(sid=sid)
294			if last_sale_price is not None:
295			position.last_sale_price = last_sale_price
296			if last_sale_date is not None:
297			position.last_sale_date = last_sale_date
298			if cost_basis is not None:
299			position.cost_basis = cost_basis
300
301			def execute_transaction(self, txn):
302			# Update Position
303			# ----------------
304			sid = txn.sid
305
306			if sid not in self.positions:
307			position = Position(sid)
308			self.positions[sid] = position
309			else:
310			position = self.positions[sid]
311
312			position.update(txn)
313			self._update_asset(sid)
314
315			def handle_commission(self, sid, cost):
316			# Adjust the cost basis of the stock if we own it
317			if sid in self.positions:
318			self.positions[sid].adjust_commission_cost_basis(sid, cost)
319
320			def handle_splits(self, splits):
321			"""
322			Processes a list of splits by modifying any positions as needed.
323
324			Parameters
325			----------
326			splits: list
327			A list of splits. Each split is a tuple of (sid, ratio).
328
329			Returns
330			-------
331			None
332			"""
333			for split in splits:
334			sid = split[0]
335			if sid in self.positions:
336			# Make the position object handle the split. It returns the
337			# leftover cash from a fractional share, if there is any.
338			position = self.positions[sid]
339			leftover_cash = position.handle_split(sid, split[1])
340			self._update_asset(split[0])
341			return leftover_cash
342
343			def earn_dividends(self, dividends, stock_dividends):
344			"""
345			Given a list of dividends whose ex_dates are all the next trading day,
346			calculate and store the cash and/or stock payments to be paid on each
347			dividend's pay date.
348			"""
349			for dividend in dividends:
350			# Store the earned dividends so that they can be paid on the
351			# dividends' pay_dates.
352			div_owed = self.positions[dividend.sid].earn_dividend(dividend)
353			try:
354			self._unpaid_dividends[dividend.pay_date].append(
355			div_owed)
356			except KeyError:
357			self._unpaid_dividends[dividend.pay_date] = [div_owed]
358
359			for stock_dividend in stock_dividends:
360			div_owed = self.positions[stock_dividend.sid].earn_stock_dividend(
361			stock_dividend)
362			try:
363			self._unpaid_stock_dividends[stock_dividend.pay_date].\
364			append(div_owed)
365			except KeyError:
366			self._unpaid_stock_dividends[stock_dividend.pay_date] = \
367			[div_owed]
368
369			def pay_dividends(self, next_trading_day):
370			"""
371			Returns a cash payment based on the dividends that should be paid out
372			according to the accumulated bookkeeping of earned, unpaid, and stock
373			dividends.
374			"""
375			net_cash_payment = 0.0
376
377			try:
378			payments = self._unpaid_dividends[next_trading_day]
379			# Mark these dividends as paid by dropping them from our unpaid
380			del self._unpaid_dividends[next_trading_day]
381			except KeyError:
382			payments = []
383
384			# representing the fact that we're required to reimburse the owner of
385			# the stock for any dividends paid while borrowing.
386			for payment in payments:
387			net_cash_payment += payment['amount']
388
389			# Add stock for any stock dividends paid. Again, the values here may
390			# be negative in the case of short positions.
391
392			try:
393			stock_payments = self._unpaid_stock_dividends[next_trading_day]
394			except:
395			stock_payments = []
396
397			for stock_payment in stock_payments:
398			stock = stock_payment['payment_sid']
399			share_count = stock_payment['share_count']
400			# note we create a Position for stock dividend if we don't
401			# already own the asset
402			if stock in self.positions:
403			position = self.positions[stock]
404			else:
405			position = self.positions[stock] = Position(stock)
406
407			position.amount += share_count
408			self._update_asset(stock)
409
410			return net_cash_payment
411
412			def maybe_create_close_position_transaction(self, event):
413			if not self.positions.get(event.sid):
414			return None
415
416			amount = self.positions.get(event.sid).amount
417			price = self._data_portal.get_spot_value(
418			event.sid, 'close', event.dt, self.data_frequency)
419
420			txn = Transaction(
421			sid=event.sid,
422			amount=(-1 * amount),
423			dt=event.dt,
424			price=price,
425			commission=0,
426			order_id=0
427			)
428			return txn
429
430			def get_positions(self):
431
432			positions = self._positions_store
433
434			for sid, pos in iteritems(self.positions):
435
436			if pos.amount == 0:
437			# Clear out the position if it has become empty since the last
438			# time get_positions was called. Catching the KeyError is
439			# faster than checking `if sid in positions`, and this can be
440			# potentially called in a tight inner loop.
441			try:
442			del positions[sid]
443			except KeyError:
444			pass
445			continue
446
447			# Note that this will create a position if we don't currently have
448			# an entry
449			position = positions[sid]
450			position.amount = pos.amount
451			position.cost_basis = pos.cost_basis
452			position.last_sale_price = pos.last_sale_price
453			position.last_sale_date = pos.last_sale_date
454
455			return positions
456
457			def get_positions_list(self):
458			positions = []
459			for sid, pos in iteritems(self.positions):
460			if pos.amount != 0:
461			positions.append(pos.to_dict())
462			return positions
463
464			def sync_last_sale_prices(self, dt):
465			data_portal = self._data_portal
466			for sid, position in iteritems(self.positions):
467			position.last_sale_price = data_portal.get_spot_value(
468			sid, 'close', dt, self.data_frequency)
469
470			def stats(self):
471			return calc_position_stats(self.positions,
472			self._position_value_multipliers,
473			self._position_exposure_multipliers)
474
475			def __getstate__(self):
476			state_dict = {}
477
478			state_dict['asset_finder'] = self.asset_finder
479			state_dict['positions'] = dict(self.positions)
480			state_dict['unpaid_dividends'] = self._unpaid_dividends
481			state_dict['unpaid_stock_dividends'] = self._unpaid_stock_dividends
482			state_dict['auto_close_position_sids'] = self._auto_close_position_sids
483			state_dict['data_frequency'] = self.data_frequency
484
485			STATE_VERSION = 3
486			state_dict[VERSION_LABEL] = STATE_VERSION
487			return state_dict
488
489			def __setstate__(self, state):
490			OLDEST_SUPPORTED_STATE = 3
491			version = state.pop(VERSION_LABEL)
492
493			if version < OLDEST_SUPPORTED_STATE:
494			raise BaseException("PositionTracker saved state is too old.")
495
496			self.asset_finder = state['asset_finder']
497			self.positions = positiondict()
498			self.data_frequency = state['data_frequency']
499			# note that positions_store is temporary and gets regened from
500			# .positions
501			self._positions_store = zp.Positions()
502
503			self._unpaid_dividends = state['unpaid_dividends']
504			self._unpaid_stock_dividends = state['unpaid_stock_dividends']
505			self._auto_close_position_sids = state['auto_close_position_sids']
506
507			# Arrays for quick calculations of positions value
508			self._position_value_multipliers = OrderedDict()
509			self._position_exposure_multipliers = OrderedDict()
510			self._position_payout_multipliers = OrderedDict()
511
512			# Update positions is called without a finder
513			self.update_positions(state['positions'])
514
515			# FIXME
516			self._data_portal = None
517

quantopian / zipline

Pull Request — master (#858)

_update_asset() B

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