zipline.finance.performance.PerformancePeriod.handle_execution() - Code Metrics - Inspection of "MAINT: Futures cash adjustment on change and calc." - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#938)

by Eddie

created 2016-01-04 22:04 UTC

handle_execution() B

↳ Parent: zipline.finance.performance.PerformancePeriod

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	6
dl	0
loc	24
rs	7.6129

#
# Copyright 2014 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.

"""

Performance Period
==================

Performance Periods are updated with every trade. When calling
code needs a portfolio object that fulfills the algorithm
protocol, use the PerformancePeriod.as_portfolio method. See that
method for comments on the specific fields provided (and
omitted).

    +---------------+------------------------------------------------------+
    | key           | value                                                |
    +===============+======================================================+
    | ending_value  | the total market value of the positions held at the  |
    |               | end of the period                                    |
    +---------------+------------------------------------------------------+
    | cash_flow     | the cash flow in the period (negative means spent)   |
    |               | from buying and selling assets in the period.        |
    |               | Includes dividend payments in the period as well.    |
    +---------------+------------------------------------------------------+
    | starting_value| the total market value of the positions held at the  |
    |               | start of the period                                  |
    +---------------+------------------------------------------------------+
    | starting_cash | cash on hand at the beginning of the period          |
    +---------------+------------------------------------------------------+
    | ending_cash   | cash on hand at the end of the period                |
    +---------------+------------------------------------------------------+
    | positions     | a list of dicts representing positions, see          |
    |               | :py:meth:`Position.to_dict()`                        |
    |               | for details on the contents of the dict              |
    +---------------+------------------------------------------------------+
    | pnl           | Dollar value profit and loss, for both realized and  |
    |               | unrealized gains.                                    |
    +---------------+------------------------------------------------------+
    | returns       | percentage returns for the entire portfolio over the |
    |               | period                                               |
    +---------------+------------------------------------------------------+
    | cumulative\   | The net capital used (positive is spent) during      |
    | _capital_used | the period                                           |
    +---------------+------------------------------------------------------+
    | max_capital\  | The maximum amount of capital deployed during the    |
    | _used         | period.                                              |
    +---------------+------------------------------------------------------+
    | period_close  | The last close of the market in period. datetime in  |
    |               | pytz.utc timezone.                                   |
    +---------------+------------------------------------------------------+
    | period_open   | The first open of the market in period. datetime in  |
    |               | pytz.utc timezone.                                   |
    +---------------+------------------------------------------------------+
    | transactions  | all the transactions that were acrued during this    |
    |               | period. Unset/missing for cumulative periods.        |
    +---------------+------------------------------------------------------+


"""

from __future__ import division
import logbook

import numpy as np

from collections import namedtuple
from zipline.assets import Future

try:
    # optional cython based OrderedDict
    from cyordereddict import OrderedDict
except ImportError:
    from collections import OrderedDict

from six import itervalues, iteritems

import zipline.protocol as zp

from zipline.utils.serialization_utils import (
    VERSION_LABEL
)

log = logbook.Logger('Performance')
TRADE_TYPE = zp.DATASOURCE_TYPE.TRADE


PeriodStats = namedtuple('PeriodStats',
                         ['net_liquidation',
                          'gross_leverage',
                          'net_leverage'])


def calc_net_liquidation(ending_cash, long_value, short_value):
    return ending_cash + long_value + short_value


def calc_leverage(exposure, net_liq):
    if net_liq != 0:
        return exposure / net_liq

    return np.inf


def calc_period_stats(pos_stats, ending_cash):
    net_liq = calc_net_liquidation(ending_cash,
                                   pos_stats.long_value,
                                   pos_stats.short_value)
    gross_leverage = calc_leverage(pos_stats.gross_exposure, net_liq)
    net_leverage = calc_leverage(pos_stats.net_exposure, net_liq)

    return PeriodStats(
        net_liquidation=net_liq,
        gross_leverage=gross_leverage,
        net_leverage=net_leverage)


class PerformancePeriod(object):

    def __init__(
            self,
            starting_cash,
            asset_finder,
            period_open=None,
            period_close=None,
            keep_transactions=True,
            keep_orders=False,
            serialize_positions=True):

        self.asset_finder = asset_finder

        self.period_open = period_open
        self.period_close = period_close

        self.ending_value = 0.0
        self.ending_exposure = 0.0
        self.period_cash_flow = 0.0
        self.pnl = 0.0

        self.ending_cash = starting_cash

        # Keyed by asset, the previous last sale price of positions with
        # payouts on price differences, e.g. Futures.
        #
        # This dt is not the previous minute to the minute for which the
        # calculation is done, but the last sale price either before the period
        # start, or when the price at execution.
        self._payout_last_sale_prices = {}

        # rollover initializes a number of self's attributes:
        self.rollover()
        self.keep_transactions = keep_transactions
        self.keep_orders = keep_orders

        # An object to recycle via assigning new values
        # when returning portfolio information.
        # So as not to avoid creating a new object for each event
        self._portfolio_store = zp.Portfolio()
        self._account_store = zp.Account()
        self.serialize_positions = serialize_positions

        # This dict contains the known cash flow multipliers for sids and is
        # keyed on sid
        self._execution_cash_flow_multipliers = {}

    _position_tracker = None

    @property
    def position_tracker(self):
        return self._position_tracker

    @position_tracker.setter
    def position_tracker(self, obj):
        if obj is None:
            raise ValueError("position_tracker can not be None")
        self._position_tracker = obj
        # we only calculate perf once we inject PositionTracker
        self.calculate_performance()

    def rollover(self):
        self.starting_value = self.ending_value
        self.starting_exposure = self.ending_exposure
        self.starting_cash = self.ending_cash
        self.period_cash_flow = 0.0
        self.pnl = 0.0
        self.processed_transactions = {}
        self.orders_by_modified = {}
        self.orders_by_id = OrderedDict()

        payout_assets = self._payout_last_sale_prices.keys()

        for asset in payout_assets:
            if asset in self._payout_last_sale_prices:
                self._payout_last_sale_prices[asset] = \
                    self.position_tracker.positions[asset].last_sale_price
            else:
                del self._payout_last_sale_prices[asset]

    def handle_dividends_paid(self, net_cash_payment):
        if net_cash_payment:
            self.handle_cash_payment(net_cash_payment)
        self.calculate_performance()

    def handle_cash_payment(self, payment_amount):
        self.adjust_cash(payment_amount)

    def handle_commission(self, cost):
        # Deduct from our total cash pool.
        self.adjust_cash(-cost)

    def adjust_cash(self, amount):
        self.period_cash_flow += amount

    def adjust_field(self, field, value):
        setattr(self, field, value)

    def _get_payout_total(self, positions):
        payouts = []
        for asset, old_price in iteritems(self._payout_last_sale_prices):
            pos = positions[asset]
            price = pos.last_sale_price

            payout = (
                (price - old_price)
                *
                asset.contract_multiplier
                *
                pos.amount
            )
            payouts.append(payout)

        return sum(payouts)

    def calculate_performance(self):
        pt = self.position_tracker
        pos_stats = pt.stats()
        self.ending_value = pos_stats.net_value
        self.ending_exposure = pos_stats.net_exposure

        payout = self._get_payout_total(pt.positions)

        total_at_start = self.starting_cash + self.starting_value
        self.ending_cash = self.starting_cash + self.period_cash_flow
        total_at_end = self.ending_cash + self.ending_value + payout

        self.pnl = total_at_end - total_at_start
        if total_at_start != 0:
            self.returns = self.pnl / total_at_start
        else:
            self.returns = 0.0

    def record_order(self, order):
        if self.keep_orders:
            try:
                dt_orders = self.orders_by_modified[order.dt]
                if order.id in dt_orders:
                    del dt_orders[order.id]
            except KeyError:
                self.orders_by_modified[order.dt] = dt_orders = OrderedDict()
            dt_orders[order.id] = order
            # to preserve the order of the orders by modified date
            # we delete and add back. (ordered dictionary is sorted by
            # first insertion date).
            if order.id in self.orders_by_id:
                del self.orders_by_id[order.id]
            self.orders_by_id[order.id] = order

    def handle_execution(self, txn):
        self.period_cash_flow += self._calculate_execution_cash_flow(txn)

        asset = self.asset_finder.retrieve_asset(txn.sid)
        if isinstance(asset, Future):
            try:
                old_price = self._payout_last_sale_prices[asset]
                amount = self.position_tracker.positions[asset].amount
                price = txn.price
                cash_adj = (price - old_price) * asset.contract_multiplier * \
                    amount
                self.adjust_cash(cash_adj)
                if amount + txn.amount == 0:
                    del self._payout_last_sale_prices[asset]
                else:
                    self._payout_last_sale_prices[asset] = price
            except KeyError:
                self._payout_last_sale_prices[asset] = txn.price

        if self.keep_transactions:
            try:
                self.processed_transactions[txn.dt].append(txn)
            except KeyError:
                self.processed_transactions[txn.dt] = [txn]

    def _calculate_execution_cash_flow(self, txn):
        """
        Calculates the cash flow from executing the given transaction
        """
        # Check if the multiplier is cached. If it is not, look up the asset
        # and cache the multiplier.
        try:
            multiplier = self._execution_cash_flow_multipliers[txn.sid]
        except KeyError:
            asset = self.asset_finder.retrieve_asset(txn.sid)
            # Futures experience no cash flow on transactions
            if isinstance(asset, Future):
                multiplier = 0
            else:
                multiplier = 1
            self._execution_cash_flow_multipliers[txn.sid] = multiplier

        # Calculate and return the cash flow given the multiplier
        return -1 * txn.price * txn.amount * multiplier

    # backwards compat. TODO: remove?
    @property
    def positions(self):
        return self.position_tracker.positions

    @property
    def position_amounts(self):
        return self.position_tracker.position_amounts

    def __core_dict(self):
        pos_stats = self.position_tracker.stats()
        period_stats = calc_period_stats(pos_stats, self.ending_cash)

        rval = {
            'ending_value': self.ending_value,
            'ending_exposure': self.ending_exposure,
            # this field is renamed to capital_used for backward
            # compatibility.
            'capital_used': self.period_cash_flow,
            'starting_value': self.starting_value,
            'starting_exposure': self.starting_exposure,
            'starting_cash': self.starting_cash,
            'ending_cash': self.ending_cash,
            'portfolio_value': self.ending_cash + self.ending_value,
            'pnl': self.pnl,
            'returns': self.returns,
            'period_open': self.period_open,
            'period_close': self.period_close,
            'gross_leverage': period_stats.gross_leverage,
            'net_leverage': period_stats.net_leverage,
            'short_exposure': pos_stats.short_exposure,
            'long_exposure': pos_stats.long_exposure,
            'short_value': pos_stats.short_value,
            'long_value': pos_stats.long_value,
            'longs_count': pos_stats.longs_count,
            'shorts_count': pos_stats.shorts_count,
        }

        return rval

    def to_dict(self, dt=None):
        """
        Creates a dictionary representing the state of this performance
        period. See header comments for a detailed description.

        Kwargs:
            dt (datetime): If present, only return transactions for the dt.
        """
        rval = self.__core_dict()

        if self.serialize_positions:
            positions = self.position_tracker.get_positions_list()
            rval['positions'] = positions

        # we want the key to be absent, not just empty
        if self.keep_transactions:
            if dt:
                # Only include transactions for given dt
                try:
                    transactions = [x.to_dict()
                                    for x in self.processed_transactions[dt]]
                except KeyError:
                    transactions = []
            else:
                transactions = \
                    [y.to_dict()
                     for x in itervalues(self.processed_transactions)
                     for y in x]
            rval['transactions'] = transactions

        if self.keep_orders:
            if dt:
                # only include orders modified as of the given dt.
                try:
                    orders = [x.to_dict()
                              for x in itervalues(self.orders_by_modified[dt])]
                except KeyError:
                    orders = []
            else:
                orders = [x.to_dict() for x in itervalues(self.orders_by_id)]
            rval['orders'] = orders

        return rval

    def as_portfolio(self):
        """
        The purpose of this method is to provide a portfolio
        object to algorithms running inside the same trading
        client. The data needed is captured raw in a
        PerformancePeriod, and in this method we rename some
        fields for usability and remove extraneous fields.
        """
        # Recycles containing objects' Portfolio object
        # which is used for returning values.
        # as_portfolio is called in an inner loop,
        # so repeated object creation becomes too expensive
        portfolio = self._portfolio_store
        # maintaining the old name for the portfolio field for
        # backward compatibility
        portfolio.capital_used = self.period_cash_flow
        portfolio.starting_cash = self.starting_cash
        portfolio.portfolio_value = self.ending_cash + self.ending_value
        portfolio.pnl = self.pnl
        portfolio.returns = self.returns
        portfolio.cash = self.ending_cash
        portfolio.start_date = self.period_open
        portfolio.positions = self.position_tracker.get_positions()
        portfolio.positions_value = self.ending_value
        portfolio.positions_exposure = self.ending_exposure
        return portfolio

    def as_account(self):
        account = self._account_store

        pt = self.position_tracker
        pos_stats = pt.stats()
        period_stats = calc_period_stats(pos_stats, self.ending_cash)

        # If no attribute is found on the PerformancePeriod resort to the
        # following default values. If an attribute is found use the existing
        # value. For instance, a broker may provide updates to these
        # attributes. In this case we do not want to over write the broker
        # values with the default values.
        account.settled_cash = \
            getattr(self, 'settled_cash', self.ending_cash)
        account.accrued_interest = \
            getattr(self, 'accrued_interest', 0.0)
        account.buying_power = \
            getattr(self, 'buying_power', float('inf'))
        account.equity_with_loan = \
            getattr(self, 'equity_with_loan',
                    self.ending_cash + self.ending_value)
        account.total_positions_value = \
            getattr(self, 'total_positions_value', self.ending_value)
        account.total_positions_value = \
            getattr(self, 'total_positions_exposure', self.ending_exposure)
        account.regt_equity = \
            getattr(self, 'regt_equity', self.ending_cash)
        account.regt_margin = \
            getattr(self, 'regt_margin', float('inf'))
        account.initial_margin_requirement = \
            getattr(self, 'initial_margin_requirement', 0.0)
        account.maintenance_margin_requirement = \
            getattr(self, 'maintenance_margin_requirement', 0.0)
        account.available_funds = \
            getattr(self, 'available_funds', self.ending_cash)
        account.excess_liquidity = \
            getattr(self, 'excess_liquidity', self.ending_cash)
        account.cushion = \
            getattr(self, 'cushion',
                    self.ending_cash / (self.ending_cash + self.ending_value))
        account.day_trades_remaining = \
            getattr(self, 'day_trades_remaining', float('inf'))
        account.leverage = getattr(self, 'leverage',
                                   period_stats.gross_leverage)
        account.net_leverage = period_stats.net_leverage

        account.net_liquidation = getattr(self, 'net_liquidation',
                                          period_stats.net_liquidation)
        return account

    def __getstate__(self):
        state_dict = {k: v for k, v in iteritems(self.__dict__)
                      if not k.startswith('_')}

        state_dict['_portfolio_store'] = self._portfolio_store
        state_dict['_account_store'] = self._account_store

        state_dict['processed_transactions'] = \
            dict(self.processed_transactions)
        state_dict['orders_by_id'] = \
            dict(self.orders_by_id)
        state_dict['orders_by_modified'] = \
            dict(self.orders_by_modified)
        state_dict['_payout_last_sale_prices'] = \
            self._payout_last_sale_prices

        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("PerformancePeriod saved state is too old.")

        processed_transactions = {}
        processed_transactions.update(state.pop('processed_transactions'))

        orders_by_id = OrderedDict()
        orders_by_id.update(state.pop('orders_by_id'))

        orders_by_modified = {}
        orders_by_modified.update(state.pop('orders_by_modified'))
        self.processed_transactions = processed_transactions
        self.orders_by_id = orders_by_id
        self.orders_by_modified = orders_by_modified

        self._execution_cash_flow_multipliers = {}

        self.__dict__.update(state)


1			#
2			# Copyright 2014 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			"""
17
18			Performance Period
19			==================
20
21			Performance Periods are updated with every trade. When calling
22			code needs a portfolio object that fulfills the algorithm
23			protocol, use the PerformancePeriod.as_portfolio method. See that
24			method for comments on the specific fields provided (and
25			omitted).
26
27			+---------------+------------------------------------------------------+
28			\| key \| value \|
29			+===============+======================================================+
30			\| ending_value \| the total market value of the positions held at the \|
31			\| \| end of the period \|
32			+---------------+------------------------------------------------------+
33			\| cash_flow \| the cash flow in the period (negative means spent) \|
34			\| \| from buying and selling assets in the period. \|
35			\| \| Includes dividend payments in the period as well. \|
36			+---------------+------------------------------------------------------+
37			\| starting_value\| the total market value of the positions held at the \|
38			\| \| start of the period \|
39			+---------------+------------------------------------------------------+
40			\| starting_cash \| cash on hand at the beginning of the period \|
41			+---------------+------------------------------------------------------+
42			\| ending_cash \| cash on hand at the end of the period \|
43			+---------------+------------------------------------------------------+
44			\| positions \| a list of dicts representing positions, see \|
45			\| \| :py:meth:`Position.to_dict()` \|
46			\| \| for details on the contents of the dict \|
47			+---------------+------------------------------------------------------+
48			\| pnl \| Dollar value profit and loss, for both realized and \|
49			\| \| unrealized gains. \|
50			+---------------+------------------------------------------------------+
51			\| returns \| percentage returns for the entire portfolio over the \|
52			\| \| period \|
53			+---------------+------------------------------------------------------+
54			\| cumulative\ \| The net capital used (positive is spent) during \|
55			\| _capital_used \| the period \|
56			+---------------+------------------------------------------------------+
57			\| max_capital\ \| The maximum amount of capital deployed during the \|
58			\| _used \| period. \|
59			+---------------+------------------------------------------------------+
60			\| period_close \| The last close of the market in period. datetime in \|
61			\| \| pytz.utc timezone. \|
62			+---------------+------------------------------------------------------+
63			\| period_open \| The first open of the market in period. datetime in \|
64			\| \| pytz.utc timezone. \|
65			+---------------+------------------------------------------------------+
66			\| transactions \| all the transactions that were acrued during this \|
67			\| \| period. Unset/missing for cumulative periods. \|
68			+---------------+------------------------------------------------------+
69
70
71			"""
72
73			from __future__ import division
74			import logbook
75
76			import numpy as np
77
78			from collections import namedtuple
79			from zipline.assets import Future
80
81			try:
82			# optional cython based OrderedDict
83			from cyordereddict import OrderedDict
84			except ImportError:
85			from collections import OrderedDict
86
87			from six import itervalues, iteritems
88
89			import zipline.protocol as zp
90
91			from zipline.utils.serialization_utils import (
92			VERSION_LABEL
93			)
94
95			log = logbook.Logger('Performance')
96			TRADE_TYPE = zp.DATASOURCE_TYPE.TRADE
97
98
99			PeriodStats = namedtuple('PeriodStats',
100			['net_liquidation',
101			'gross_leverage',
102			'net_leverage'])
103
104
105			def calc_net_liquidation(ending_cash, long_value, short_value):
106			return ending_cash + long_value + short_value
107
108
109			def calc_leverage(exposure, net_liq):
110			if net_liq != 0:
111			return exposure / net_liq
112
113			return np.inf
114
115
116			def calc_period_stats(pos_stats, ending_cash):
117			net_liq = calc_net_liquidation(ending_cash,
118			pos_stats.long_value,
119			pos_stats.short_value)
120			gross_leverage = calc_leverage(pos_stats.gross_exposure, net_liq)
121			net_leverage = calc_leverage(pos_stats.net_exposure, net_liq)
122
123			return PeriodStats(
124			net_liquidation=net_liq,
125			gross_leverage=gross_leverage,
126			net_leverage=net_leverage)
127
128
129			class PerformancePeriod(object):
130
131			def __init__(
132			self,
133			starting_cash,
134			asset_finder,
135			period_open=None,
136			period_close=None,
137			keep_transactions=True,
138			keep_orders=False,
139			serialize_positions=True):
140
141			self.asset_finder = asset_finder
142
143			self.period_open = period_open
144			self.period_close = period_close
145
146			self.ending_value = 0.0
147			self.ending_exposure = 0.0
148			self.period_cash_flow = 0.0
149			self.pnl = 0.0
150
151			self.ending_cash = starting_cash
152
153			# Keyed by asset, the previous last sale price of positions with
154			# payouts on price differences, e.g. Futures.
155			#
156			# This dt is not the previous minute to the minute for which the
157			# calculation is done, but the last sale price either before the period
158			# start, or when the price at execution.
159			self._payout_last_sale_prices = {}
160
161			# rollover initializes a number of self's attributes:
162			self.rollover()
163			self.keep_transactions = keep_transactions
164			self.keep_orders = keep_orders
165
166			# An object to recycle via assigning new values
167			# when returning portfolio information.
168			# So as not to avoid creating a new object for each event
169			self._portfolio_store = zp.Portfolio()
170			self._account_store = zp.Account()
171			self.serialize_positions = serialize_positions
172
173			# This dict contains the known cash flow multipliers for sids and is
174			# keyed on sid
175			self._execution_cash_flow_multipliers = {}
176
177			_position_tracker = None
178
179			@property
180			def position_tracker(self):
181			return self._position_tracker
182
183			@position_tracker.setter
184			def position_tracker(self, obj):
185			if obj is None:
186			raise ValueError("position_tracker can not be None")
187			self._position_tracker = obj
188			# we only calculate perf once we inject PositionTracker
189			self.calculate_performance()
190
191			def rollover(self):
192			self.starting_value = self.ending_value
193			self.starting_exposure = self.ending_exposure
194			self.starting_cash = self.ending_cash
195			self.period_cash_flow = 0.0
196			self.pnl = 0.0
197			self.processed_transactions = {}
198			self.orders_by_modified = {}
199			self.orders_by_id = OrderedDict()
200
201			payout_assets = self._payout_last_sale_prices.keys()
202
203			for asset in payout_assets:
204			if asset in self._payout_last_sale_prices:
205			self._payout_last_sale_prices[asset] = \
206			self.position_tracker.positions[asset].last_sale_price
207			else:
208			del self._payout_last_sale_prices[asset]
209
210			def handle_dividends_paid(self, net_cash_payment):
211			if net_cash_payment:
212			self.handle_cash_payment(net_cash_payment)
213			self.calculate_performance()
214
215			def handle_cash_payment(self, payment_amount):
216			self.adjust_cash(payment_amount)
217
218			def handle_commission(self, cost):
219			# Deduct from our total cash pool.
220			self.adjust_cash(-cost)
221
222			def adjust_cash(self, amount):
223			self.period_cash_flow += amount
224
225			def adjust_field(self, field, value):
226			setattr(self, field, value)
227
228			def _get_payout_total(self, positions):
229			payouts = []
230			for asset, old_price in iteritems(self._payout_last_sale_prices):
231			pos = positions[asset]
232			price = pos.last_sale_price
233
234			payout = (
235			(price - old_price)
236			*
237			asset.contract_multiplier
238			*
239			pos.amount
240			)
241			payouts.append(payout)
242
243			return sum(payouts)
244
245			def calculate_performance(self):
246			pt = self.position_tracker
247			pos_stats = pt.stats()
248			self.ending_value = pos_stats.net_value
249			self.ending_exposure = pos_stats.net_exposure
250
251			payout = self._get_payout_total(pt.positions)
252
253			total_at_start = self.starting_cash + self.starting_value
254			self.ending_cash = self.starting_cash + self.period_cash_flow
255			total_at_end = self.ending_cash + self.ending_value + payout
256
257			self.pnl = total_at_end - total_at_start
258			if total_at_start != 0:
259			self.returns = self.pnl / total_at_start
260			else:
261			self.returns = 0.0
262
263			def record_order(self, order):
264			if self.keep_orders:
265			try:
266			dt_orders = self.orders_by_modified[order.dt]
267			if order.id in dt_orders:
268			del dt_orders[order.id]
269			except KeyError:
270			self.orders_by_modified[order.dt] = dt_orders = OrderedDict()
271			dt_orders[order.id] = order
272			# to preserve the order of the orders by modified date
273			# we delete and add back. (ordered dictionary is sorted by
274			# first insertion date).
275			if order.id in self.orders_by_id:
276			del self.orders_by_id[order.id]
277			self.orders_by_id[order.id] = order
278
279			def handle_execution(self, txn):
280			self.period_cash_flow += self._calculate_execution_cash_flow(txn)
281
282			asset = self.asset_finder.retrieve_asset(txn.sid)
283			if isinstance(asset, Future):
284			try:
285			old_price = self._payout_last_sale_prices[asset]
286			amount = self.position_tracker.positions[asset].amount
287			price = txn.price
288			cash_adj = (price - old_price) * asset.contract_multiplier * \
289			amount
290			self.adjust_cash(cash_adj)
291			if amount + txn.amount == 0:
292			del self._payout_last_sale_prices[asset]
293			else:
294			self._payout_last_sale_prices[asset] = price
295			except KeyError:
296			self._payout_last_sale_prices[asset] = txn.price
297
298			if self.keep_transactions:
299			try:
300			self.processed_transactions[txn.dt].append(txn)
301			except KeyError:
302			self.processed_transactions[txn.dt] = [txn]
303
304			def _calculate_execution_cash_flow(self, txn):
305			"""
306			Calculates the cash flow from executing the given transaction
307			"""
308			# Check if the multiplier is cached. If it is not, look up the asset
309			# and cache the multiplier.
310			try:
311			multiplier = self._execution_cash_flow_multipliers[txn.sid]
312			except KeyError:
313			asset = self.asset_finder.retrieve_asset(txn.sid)
314			# Futures experience no cash flow on transactions
315			if isinstance(asset, Future):
316			multiplier = 0
317			else:
318			multiplier = 1
319			self._execution_cash_flow_multipliers[txn.sid] = multiplier
320
321			# Calculate and return the cash flow given the multiplier
322			return -1 * txn.price * txn.amount * multiplier
323
324			# backwards compat. TODO: remove?
325			@property
326			def positions(self):
327			return self.position_tracker.positions
328
329			@property
330			def position_amounts(self):
331			return self.position_tracker.position_amounts
332
333			def __core_dict(self):
334			pos_stats = self.position_tracker.stats()
335			period_stats = calc_period_stats(pos_stats, self.ending_cash)
336
337			rval = {
338			'ending_value': self.ending_value,
339			'ending_exposure': self.ending_exposure,
340			# this field is renamed to capital_used for backward
341			# compatibility.
342			'capital_used': self.period_cash_flow,
343			'starting_value': self.starting_value,
344			'starting_exposure': self.starting_exposure,
345			'starting_cash': self.starting_cash,
346			'ending_cash': self.ending_cash,
347			'portfolio_value': self.ending_cash + self.ending_value,
348			'pnl': self.pnl,
349			'returns': self.returns,
350			'period_open': self.period_open,
351			'period_close': self.period_close,
352			'gross_leverage': period_stats.gross_leverage,
353			'net_leverage': period_stats.net_leverage,
354			'short_exposure': pos_stats.short_exposure,
355			'long_exposure': pos_stats.long_exposure,
356			'short_value': pos_stats.short_value,
357			'long_value': pos_stats.long_value,
358			'longs_count': pos_stats.longs_count,
359			'shorts_count': pos_stats.shorts_count,
360			}
361
362			return rval
363
364			def to_dict(self, dt=None):
365			"""
366			Creates a dictionary representing the state of this performance
367			period. See header comments for a detailed description.
368
369			Kwargs:
370			dt (datetime): If present, only return transactions for the dt.
371			"""
372			rval = self.__core_dict()
373
374			if self.serialize_positions:
375			positions = self.position_tracker.get_positions_list()
376			rval['positions'] = positions
377
378			# we want the key to be absent, not just empty
379			if self.keep_transactions:
380			if dt:
381			# Only include transactions for given dt
382			try:
383			transactions = [x.to_dict()
384			for x in self.processed_transactions[dt]]
385			except KeyError:
386			transactions = []
387			else:
388			transactions = \
389			[y.to_dict()
390			for x in itervalues(self.processed_transactions)
391			for y in x]
392			rval['transactions'] = transactions
393
394			if self.keep_orders:
395			if dt:
396			# only include orders modified as of the given dt.
397			try:
398			orders = [x.to_dict()
399			for x in itervalues(self.orders_by_modified[dt])]
400			except KeyError:
401			orders = []
402			else:
403			orders = [x.to_dict() for x in itervalues(self.orders_by_id)]
404			rval['orders'] = orders
405
406			return rval
407
408			def as_portfolio(self):
409			"""
410			The purpose of this method is to provide a portfolio
411			object to algorithms running inside the same trading
412			client. The data needed is captured raw in a
413			PerformancePeriod, and in this method we rename some
414			fields for usability and remove extraneous fields.
415			"""
416			# Recycles containing objects' Portfolio object
417			# which is used for returning values.
418			# as_portfolio is called in an inner loop,
419			# so repeated object creation becomes too expensive
420			portfolio = self._portfolio_store
421			# maintaining the old name for the portfolio field for
422			# backward compatibility
423			portfolio.capital_used = self.period_cash_flow
424			portfolio.starting_cash = self.starting_cash
425			portfolio.portfolio_value = self.ending_cash + self.ending_value
426			portfolio.pnl = self.pnl
427			portfolio.returns = self.returns
428			portfolio.cash = self.ending_cash
429			portfolio.start_date = self.period_open
430			portfolio.positions = self.position_tracker.get_positions()
431			portfolio.positions_value = self.ending_value
432			portfolio.positions_exposure = self.ending_exposure
433			return portfolio
434
435			def as_account(self):
436			account = self._account_store
437
438			pt = self.position_tracker
439			pos_stats = pt.stats()
440			period_stats = calc_period_stats(pos_stats, self.ending_cash)
441
442			# If no attribute is found on the PerformancePeriod resort to the
443			# following default values. If an attribute is found use the existing
444			# value. For instance, a broker may provide updates to these
445			# attributes. In this case we do not want to over write the broker
446			# values with the default values.
447			account.settled_cash = \
448			getattr(self, 'settled_cash', self.ending_cash)
449			account.accrued_interest = \
450			getattr(self, 'accrued_interest', 0.0)
451			account.buying_power = \
452			getattr(self, 'buying_power', float('inf'))
453			account.equity_with_loan = \
454			getattr(self, 'equity_with_loan',
455			self.ending_cash + self.ending_value)
456			account.total_positions_value = \
457			getattr(self, 'total_positions_value', self.ending_value)
458			account.total_positions_value = \
459			getattr(self, 'total_positions_exposure', self.ending_exposure)
460			account.regt_equity = \
461			getattr(self, 'regt_equity', self.ending_cash)
462			account.regt_margin = \
463			getattr(self, 'regt_margin', float('inf'))
464			account.initial_margin_requirement = \
465			getattr(self, 'initial_margin_requirement', 0.0)
466			account.maintenance_margin_requirement = \
467			getattr(self, 'maintenance_margin_requirement', 0.0)
468			account.available_funds = \
469			getattr(self, 'available_funds', self.ending_cash)
470			account.excess_liquidity = \
471			getattr(self, 'excess_liquidity', self.ending_cash)
472			account.cushion = \
473			getattr(self, 'cushion',
474			self.ending_cash / (self.ending_cash + self.ending_value))
475			account.day_trades_remaining = \
476			getattr(self, 'day_trades_remaining', float('inf'))
477			account.leverage = getattr(self, 'leverage',
478			period_stats.gross_leverage)
479			account.net_leverage = period_stats.net_leverage
480
481			account.net_liquidation = getattr(self, 'net_liquidation',
482			period_stats.net_liquidation)
483			return account
484
485			def __getstate__(self):
486			state_dict = {k: v for k, v in iteritems(self.__dict__)
487			if not k.startswith('_')}
488
489			state_dict['_portfolio_store'] = self._portfolio_store
490			state_dict['_account_store'] = self._account_store
491
492			state_dict['processed_transactions'] = \
493			dict(self.processed_transactions)
494			state_dict['orders_by_id'] = \
495			dict(self.orders_by_id)
496			state_dict['orders_by_modified'] = \
497			dict(self.orders_by_modified)
498			state_dict['_payout_last_sale_prices'] = \
499			self._payout_last_sale_prices
500
501			STATE_VERSION = 3
502			state_dict[VERSION_LABEL] = STATE_VERSION
503			return state_dict
504
505			def __setstate__(self, state):
506
507			OLDEST_SUPPORTED_STATE = 3
508			version = state.pop(VERSION_LABEL)
509
510			if version < OLDEST_SUPPORTED_STATE:
511			raise BaseException("PerformancePeriod saved state is too old.")
512
513			processed_transactions = {}
514			processed_transactions.update(state.pop('processed_transactions'))
515
516			orders_by_id = OrderedDict()
517			orders_by_id.update(state.pop('orders_by_id'))
518
519			orders_by_modified = {}
520			orders_by_modified.update(state.pop('orders_by_modified'))
521			self.processed_transactions = processed_transactions
522			self.orders_by_id = orders_by_id
523			self.orders_by_modified = orders_by_modified
524
525			self._execution_cash_flow_multipliers = {}
526
527			self.__dict__.update(state)
528

quantopian / zipline

Pull Request — master (#938)

handle_execution() B

Complexity

Size

Duplication

Duplication Side-by-Side

Filter issues like