zipline.finance.performance.PositionTracker.stats() - Code Metrics - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

stats() B
last analyzed 2016-01-08 22:03 UTC

↳ Parent: zipline.finance.performance.PositionTracker

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cc	2
dl	0
loc	41
rs	8.8571

#
# 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
import pandas as pd
from pandas.lib import checknull
from collections import namedtuple
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.finance.transaction import Transaction
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)


class PositionTracker(object):

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

        # sid => position object
        self.positions = positiondict()
        # Arrays for quick calculations of positions value
        self._position_value_multipliers = OrderedDict()
        self._position_exposure_multipliers = OrderedDict()
        self._unpaid_dividends = pd.DataFrame(
            columns=zp.DIVIDEND_PAYMENT_FIELDS,
        )
        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 = {}

    def _update_asset(self, sid):
        try:
            self._position_value_multipliers[sid]
            self._position_exposure_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
            if isinstance(asset, Future):
                self._position_value_multipliers[sid] = 0
                self._position_exposure_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_last_sale(self, event):
        # NOTE, PerformanceTracker already vetted as TRADE type
        sid = event.sid
        if sid not in self.positions:
            return 0

        price = event.price

        if checknull(price):
            return 0

        pos = self.positions[sid]
        pos.last_sale_date = event.dt
        pos.last_sale_price = price

    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):
        pos = self.positions[sid]

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

    def execute_transaction(self, txn):
        # Update Position
        # ----------------
        sid = txn.sid
        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_split(self, split):
        if split.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[split.sid]
            leftover_cash = position.handle_split(split.sid, split.ratio)
            self._update_asset(split.sid)
            return leftover_cash

    def _maybe_earn_dividend(self, dividend):
        """
        Take a historical dividend record and return a Series with fields in
        zipline.protocol.DIVIDEND_FIELDS (plus an 'id' field) representing
        the cash/stock amount we are owed when the dividend is paid.
        """
        if dividend['sid'] in self.positions:
            return self.positions[dividend['sid']].earn_dividend(dividend)
        else:
            return zp.dividend_payment()

    def earn_dividends(self, dividend_frame):
        """
        Given a frame 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.
        """
        earned = dividend_frame.apply(self._maybe_earn_dividend, axis=1)\
                               .dropna(how='all')
        if len(earned) > 0:
            # Store the earned dividends so that they can be paid on the
            # dividends' pay_dates.
            self._unpaid_dividends = pd.concat(
                [self._unpaid_dividends, earned],
            )

    def _maybe_pay_dividend(self, dividend):
        """
        Take a historical dividend record, look up any stored record of
        cash/stock we are owed for that dividend, and return a Series
        with fields drawn from zipline.protocol.DIVIDEND_PAYMENT_FIELDS.
        """
        try:
            unpaid_dividend = self._unpaid_dividends.loc[dividend['id']]
            return unpaid_dividend
        except KeyError:
            return zp.dividend_payment()

    def pay_dividends(self, dividend_frame):
        """
        Given a frame of dividends whose pay_dates are all the next trading
        day, grant the cash and/or stock payments that were calculated on the
        given dividends' ex dates.
        """
        payments = dividend_frame.apply(self._maybe_pay_dividend, axis=1)\
                                 .dropna(how='all')

        # Mark these dividends as paid by dropping them from our unpaid
        # table.
        self._unpaid_dividends.drop(payments.index)

        # Add stock for any stock dividends paid.  Again, the values here may
        # be negative in the case of short positions.
        stock_payments = payments[payments['payment_sid'].notnull()]
        for _, row in stock_payments.iterrows():
            stock = row['payment_sid']
            share_count = row['share_count']
            # note we create a Position for stock dividend if we don't
            # already own the asset
            position = self.positions[stock]

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

        # Add cash equal to the net cash payed from all dividends.  Note that
        # "negative cash" is effectively paid if we're short an asset,
        # representing the fact that we're required to reimburse the owner of
        # the stock for any dividends paid while borrowing.
        net_cash_payment = payments['cash_amount'].fillna(0).sum()
        return net_cash_payment

    def maybe_create_close_position_transaction(self, event):
        try:
            pos = self.positions[event.sid]
            amount = pos.amount
            if amount == 0:
                return None
        except KeyError:
            return None
        if 'price' in event:
            price = event.price
        else:
            price = pos.last_sale_price
        txn = Transaction(
            sid=event.sid,
            amount=(-1 * pos.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
        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 stats(self):
        amounts = []
        last_sale_prices = []
        for pos in itervalues(self.positions):
            amounts.append(pos.amount)
            last_sale_prices.append(pos.last_sale_price)

        position_values = calc_position_values(
            amounts,
            last_sale_prices,
            self._position_value_multipliers
        )

        position_exposures = calc_position_exposures(
            amounts,
            last_sale_prices,
            self._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
        )

    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['auto_close_position_sids'] = self._auto_close_position_sids

        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()
        # note that positions_store is temporary and gets regened from
        # .positions
        self._positions_store = zp.Positions()

        self._unpaid_dividends = state['unpaid_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()

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


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			import pandas as pd
21			from pandas.lib import checknull
22			from collections import namedtuple
23			try:
24			# optional cython based OrderedDict
25			from cyordereddict import OrderedDict
26			except ImportError:
27			from collections import OrderedDict
28			from six import iteritems, itervalues
29
30			from zipline.protocol import Event, DATASOURCE_TYPE
31			from zipline.finance.transaction import Transaction
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			class PositionTracker(object):
125
126			def __init__(self, asset_finder):
127			self.asset_finder = asset_finder
128
129			# sid => position object
130			self.positions = positiondict()
131			# Arrays for quick calculations of positions value
132			self._position_value_multipliers = OrderedDict()
133			self._position_exposure_multipliers = OrderedDict()
134			self._unpaid_dividends = pd.DataFrame(
135			columns=zp.DIVIDEND_PAYMENT_FIELDS,
136			)
137			self._positions_store = zp.Positions()
138
139			# Dict, keyed on dates, that contains lists of close position events
140			# for any Assets in this tracker's positions
141			self._auto_close_position_sids = {}
142
143			def _update_asset(self, sid):
144			try:
145			self._position_value_multipliers[sid]
146			self._position_exposure_multipliers[sid]
147			except KeyError:
148			# Check if there is an AssetFinder
149			if self.asset_finder is None:
150			raise PositionTrackerMissingAssetFinder()
151
152			# Collect the value multipliers from applicable sids
153			asset = self.asset_finder.retrieve_asset(sid)
154			if isinstance(asset, Equity):
155			self._position_value_multipliers[sid] = 1
156			self._position_exposure_multipliers[sid] = 1
157			if isinstance(asset, Future):
158			self._position_value_multipliers[sid] = 0
159			self._position_exposure_multipliers[sid] = \
160			asset.contract_multiplier
161			# Futures auto-close timing is controlled by the Future's
162			# auto_close_date property
163			self._insert_auto_close_position_date(
164			dt=asset.auto_close_date,
165			sid=sid
166			)
167
168			def _insert_auto_close_position_date(self, dt, sid):
169			"""
170			Inserts the given SID in to the list of positions to be auto-closed by
171			the given dt.
172
173			Parameters
174			----------
175			dt : pandas.Timestamp
176			The date before-which the given SID will be auto-closed
177			sid : int
178			The SID of the Asset to be auto-closed
179			"""
180			if dt is not None:
181			self._auto_close_position_sids.setdefault(dt, set()).add(sid)
182
183			def auto_close_position_events(self, next_trading_day):
184			"""
185			Generates CLOSE_POSITION events for any SIDs whose auto-close date is
186			before or equal to the given date.
187
188			Parameters
189			----------
190			next_trading_day : pandas.Timestamp
191			The time before-which certain Assets need to be closed
192
193			Yields
194			------
195			Event
196			A close position event for any sids that should be closed before
197			the next_trading_day parameter
198			"""
199			past_asset_end_dates = set()
200
201			# Check the auto_close_position_dates dict for SIDs to close
202			for date, sids in self._auto_close_position_sids.items():
203			if date > next_trading_day:
204			continue
205			past_asset_end_dates.add(date)
206
207			for sid in sids:
208			# Yield a CLOSE_POSITION event
209			event = Event({
210			'dt': date,
211			'type': DATASOURCE_TYPE.CLOSE_POSITION,
212			'sid': sid,
213			})
214			yield event
215
216			# Clear out past dates
217			while past_asset_end_dates:
218			self._auto_close_position_sids.pop(past_asset_end_dates.pop())
219
220			def update_last_sale(self, event):
221			# NOTE, PerformanceTracker already vetted as TRADE type
222			sid = event.sid
223			if sid not in self.positions:
224			return 0
225
226			price = event.price
227
228			if checknull(price):
229			return 0
230
231			pos = self.positions[sid]
232			pos.last_sale_date = event.dt
233			pos.last_sale_price = price
234
235			def update_positions(self, positions):
236			# update positions in batch
237			self.positions.update(positions)
238			for sid, pos in iteritems(positions):
239			self._update_asset(sid)
240
241			def update_position(self, sid, amount=None, last_sale_price=None,
242			last_sale_date=None, cost_basis=None):
243			pos = self.positions[sid]
244
245			if amount is not None:
246			pos.amount = amount
247			self._update_asset(sid=sid)
248			if last_sale_price is not None:
249			pos.last_sale_price = last_sale_price
250			if last_sale_date is not None:
251			pos.last_sale_date = last_sale_date
252			if cost_basis is not None:
253			pos.cost_basis = cost_basis
254
255			def execute_transaction(self, txn):
256			# Update Position
257			# ----------------
258			sid = txn.sid
259			position = self.positions[sid]
260			position.update(txn)
261			self._update_asset(sid)
262
263			def handle_commission(self, sid, cost):
264			# Adjust the cost basis of the stock if we own it
265			if sid in self.positions:
266			self.positions[sid].adjust_commission_cost_basis(sid, cost)
267
268			def handle_split(self, split):
269			if split.sid in self.positions:
270			# Make the position object handle the split. It returns the
271			# leftover cash from a fractional share, if there is any.
272			position = self.positions[split.sid]
273			leftover_cash = position.handle_split(split.sid, split.ratio)
274			self._update_asset(split.sid)
275			return leftover_cash
276
277			def _maybe_earn_dividend(self, dividend):
278			"""
279			Take a historical dividend record and return a Series with fields in
280			zipline.protocol.DIVIDEND_FIELDS (plus an 'id' field) representing
281			the cash/stock amount we are owed when the dividend is paid.
282			"""
283			if dividend['sid'] in self.positions:
284			return self.positions[dividend['sid']].earn_dividend(dividend)
285			else:
286			return zp.dividend_payment()
287
288			def earn_dividends(self, dividend_frame):
289			"""
290			Given a frame of dividends whose ex_dates are all the next trading day,
291			calculate and store the cash and/or stock payments to be paid on each
292			dividend's pay date.
293			"""
294			earned = dividend_frame.apply(self._maybe_earn_dividend, axis=1)\
295			.dropna(how='all')
296			if len(earned) > 0:
297			# Store the earned dividends so that they can be paid on the
298			# dividends' pay_dates.
299			self._unpaid_dividends = pd.concat(
300			[self._unpaid_dividends, earned],
301			)
302
303			def _maybe_pay_dividend(self, dividend):
304			"""
305			Take a historical dividend record, look up any stored record of
306			cash/stock we are owed for that dividend, and return a Series
307			with fields drawn from zipline.protocol.DIVIDEND_PAYMENT_FIELDS.
308			"""
309			try:
310			unpaid_dividend = self._unpaid_dividends.loc[dividend['id']]
311			return unpaid_dividend
312			except KeyError:
313			return zp.dividend_payment()
314
315			def pay_dividends(self, dividend_frame):
316			"""
317			Given a frame of dividends whose pay_dates are all the next trading
318			day, grant the cash and/or stock payments that were calculated on the
319			given dividends' ex dates.
320			"""
321			payments = dividend_frame.apply(self._maybe_pay_dividend, axis=1)\
322			.dropna(how='all')
323
324			# Mark these dividends as paid by dropping them from our unpaid
325			# table.
326			self._unpaid_dividends.drop(payments.index)
327
328			# Add stock for any stock dividends paid. Again, the values here may
329			# be negative in the case of short positions.
330			stock_payments = payments[payments['payment_sid'].notnull()]
331			for _, row in stock_payments.iterrows():
332			stock = row['payment_sid']
333			share_count = row['share_count']
334			# note we create a Position for stock dividend if we don't
335			# already own the asset
336			position = self.positions[stock]
337
338			position.amount += share_count
339			self._update_asset(stock)
340
341			# Add cash equal to the net cash payed from all dividends. Note that
342			# "negative cash" is effectively paid if we're short an asset,
343			# representing the fact that we're required to reimburse the owner of
344			# the stock for any dividends paid while borrowing.
345			net_cash_payment = payments['cash_amount'].fillna(0).sum()
346			return net_cash_payment
347
348			def maybe_create_close_position_transaction(self, event):
349			try:
350			pos = self.positions[event.sid]
351			amount = pos.amount
352			if amount == 0:
353			return None
354			except KeyError:
355			return None
356			if 'price' in event:
357			price = event.price
358			else:
359			price = pos.last_sale_price
360			txn = Transaction(
361			sid=event.sid,
362			amount=(-1 * pos.amount),
363			dt=event.dt,
364			price=price,
365			commission=0,
366			order_id=0
367			)
368			return txn
369
370			def get_positions(self):
371
372			positions = self._positions_store
373
374			for sid, pos in iteritems(self.positions):
375
376			if pos.amount == 0:
377			# Clear out the position if it has become empty since the last
378			# time get_positions was called. Catching the KeyError is
379			# faster than checking `if sid in positions`, and this can be
380			# potentially called in a tight inner loop.
381			try:
382			del positions[sid]
383			except KeyError:
384			pass
385			continue
386
387			# Note that this will create a position if we don't currently have
388			# an entry
389			position = positions[sid]
390			position.amount = pos.amount
391			position.cost_basis = pos.cost_basis
392			position.last_sale_price = pos.last_sale_price
393			return positions
394
395			def get_positions_list(self):
396			positions = []
397			for sid, pos in iteritems(self.positions):
398			if pos.amount != 0:
399			positions.append(pos.to_dict())
400			return positions
401
402			def stats(self):
403			amounts = []
404			last_sale_prices = []
405			for pos in itervalues(self.positions):
406			amounts.append(pos.amount)
407			last_sale_prices.append(pos.last_sale_price)
408
409			position_values = calc_position_values(
410			amounts,
411			last_sale_prices,
412			self._position_value_multipliers
413			)
414
415			position_exposures = calc_position_exposures(
416			amounts,
417			last_sale_prices,
418			self._position_exposure_multipliers
419			)
420
421			long_value = calc_long_value(position_values)
422			short_value = calc_short_value(position_values)
423			gross_value = calc_gross_value(long_value, short_value)
424			long_exposure = calc_long_exposure(position_exposures)
425			short_exposure = calc_short_exposure(position_exposures)
426			gross_exposure = calc_gross_exposure(long_exposure, short_exposure)
427			net_exposure = calc_net(position_exposures)
428			longs_count = calc_longs_count(position_exposures)
429			shorts_count = calc_shorts_count(position_exposures)
430			net_value = calc_net(position_values)
431
432			return PositionStats(
433			long_value=long_value,
434			gross_value=gross_value,
435			short_value=short_value,
436			long_exposure=long_exposure,
437			short_exposure=short_exposure,
438			gross_exposure=gross_exposure,
439			net_exposure=net_exposure,
440			longs_count=longs_count,
441			shorts_count=shorts_count,
442			net_value=net_value
443			)
444
445			def __getstate__(self):
446			state_dict = {}
447
448			state_dict['asset_finder'] = self.asset_finder
449			state_dict['positions'] = dict(self.positions)
450			state_dict['unpaid_dividends'] = self._unpaid_dividends
451			state_dict['auto_close_position_sids'] = self._auto_close_position_sids
452
453			STATE_VERSION = 3
454			state_dict[VERSION_LABEL] = STATE_VERSION
455			return state_dict
456
457			def __setstate__(self, state):
458			OLDEST_SUPPORTED_STATE = 3
459			version = state.pop(VERSION_LABEL)
460
461			if version < OLDEST_SUPPORTED_STATE:
462			raise BaseException("PositionTracker saved state is too old.")
463
464			self.asset_finder = state['asset_finder']
465			self.positions = positiondict()
466			# note that positions_store is temporary and gets regened from
467			# .positions
468			self._positions_store = zp.Positions()
469
470			self._unpaid_dividends = state['unpaid_dividends']
471			self._auto_close_position_sids = state['auto_close_position_sids']
472
473			# Arrays for quick calculations of positions value
474			self._position_value_multipliers = OrderedDict()
475			self._position_exposure_multipliers = OrderedDict()
476
477			# Update positions is called without a finder
478			self.update_positions(state['positions'])
479

quantopian / zipline

stats() B last analyzed 2016-01-08 22:03 UTC

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stats() B
last analyzed 2016-01-08 22:03 UTC