zipline.finance.performance.PerformanceTracker.process_transaction() - 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 2015-12-14 15:18 UTC

process_transaction() A

↳ Parent: zipline.finance.performance.PerformanceTracker

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	1
dl	0
loc	5
rs	9.4286

#
# 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.

"""

Performance Tracking
====================

    +-----------------+----------------------------------------------------+
    | key             | value                                              |
    +=================+====================================================+
    | period_start    | The beginning of the period to be tracked. datetime|
    |                 | in pytz.utc timezone. Will always be 0:00 on the   |
    |                 | date in UTC. The fact that the time may be on the  |
    |                 | prior day in the exchange's local time is ignored  |
    +-----------------+----------------------------------------------------+
    | period_end      | The end of the period to be tracked. datetime      |
    |                 | in pytz.utc timezone. Will always be 23:59 on the  |
    |                 | date in UTC. The fact that the time may be on the  |
    |                 | next day in the exchange's local time is ignored   |
    +-----------------+----------------------------------------------------+
    | progress        | percentage of test completed                       |
    +-----------------+----------------------------------------------------+
    | capital_base    | The initial capital assumed for this tracker.      |
    +-----------------+----------------------------------------------------+
    | cumulative_perf | A dictionary representing the cumulative           |
    |                 | performance through all the events delivered to    |
    |                 | this tracker. For details see the comments on      |
    |                 | :py:meth:`PerformancePeriod.to_dict`               |
    +-----------------+----------------------------------------------------+
    | todays_perf     | A dictionary representing the cumulative           |
    |                 | performance through all the events delivered to    |
    |                 | this tracker with datetime stamps between last_open|
    |                 | and last_close. For details see the comments on    |
    |                 | :py:meth:`PerformancePeriod.to_dict`               |
    |                 | TODO: adding this because we calculate it. May be  |
    |                 | overkill.                                          |
    +-----------------+----------------------------------------------------+
    | cumulative_risk | A dictionary representing the risk metrics         |
    | _metrics        | calculated based on the positions aggregated       |
    |                 | through all the events delivered to this tracker.  |
    |                 | For details look at the comments for               |
    |                 | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
    +-----------------+----------------------------------------------------+

"""

from __future__ import division
import logbook
from six import iteritems
from datetime import datetime

import pandas as pd
from pandas.tseries.tools import normalize_date
from zipline.finance.performance.period import PerformancePeriod

import zipline.finance.risk as risk

from zipline.utils.serialization_utils import (
    VERSION_LABEL
)
from . position_tracker import PositionTracker

log = logbook.Logger('Performance')


class PerformanceTracker(object):
    """
    Tracks the performance of the algorithm.
    """
    def __init__(self, sim_params, env, data_portal):
        self.sim_params = sim_params
        self.env = env

        self.period_start = self.sim_params.period_start
        self.period_end = self.sim_params.period_end
        self.last_close = self.sim_params.last_close
        first_open = self.sim_params.first_open.tz_convert(
            self.env.exchange_tz
        )
        self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
                                         first_open.day), tz='UTC')
        self.market_open, self.market_close = env.get_open_and_close(self.day)
        self.total_days = self.sim_params.days_in_period
        self.capital_base = self.sim_params.capital_base
        self.emission_rate = sim_params.emission_rate

        all_trading_days = env.trading_days
        mask = ((all_trading_days >= normalize_date(self.period_start)) &
                (all_trading_days <= normalize_date(self.period_end)))

        self.trading_days = all_trading_days[mask]

        self._data_portal = data_portal
        if data_portal is not None:
            self._adjustment_reader = data_portal._adjustment_reader
        else:
            self._adjustment_reader = None

        self.position_tracker = PositionTracker(
            asset_finder=env.asset_finder,
            data_portal=data_portal,
            data_frequency=self.sim_params.data_frequency)

        if self.emission_rate == 'daily':
            self.all_benchmark_returns = pd.Series(
                index=self.trading_days)
            self.cumulative_risk_metrics = \
                risk.RiskMetricsCumulative(self.sim_params, self.env)
        elif self.emission_rate == 'minute':
            self.all_benchmark_returns = pd.Series(index=pd.date_range(
                self.sim_params.first_open, self.sim_params.last_close,
                freq='Min'))

            self.cumulative_risk_metrics = \
                risk.RiskMetricsCumulative(self.sim_params, self.env,
                                           create_first_day_stats=True)

        # this performance period will span the entire simulation from
        # inception.
        self.cumulative_performance = PerformancePeriod(
            # initial cash is your capital base.
            starting_cash=self.capital_base,
            data_frequency=self.sim_params.data_frequency,
            # the cumulative period will be calculated over the entire test.
            period_open=self.period_start,
            period_close=self.period_end,
            # don't save the transactions for the cumulative
            # period
            keep_transactions=False,
            keep_orders=False,
            # don't serialize positions for cumulative period
            serialize_positions=False,
            asset_finder=self.env.asset_finder,
            name="Cumulative"
        )

        # this performance period will span just the current market day
        self.todays_performance = PerformancePeriod(
            # initial cash is your capital base.
            starting_cash=self.capital_base,
            data_frequency=self.sim_params.data_frequency,
            # the daily period will be calculated for the market day
            period_open=self.market_open,
            period_close=self.market_close,
            keep_transactions=True,
            keep_orders=True,
            serialize_positions=True,
            asset_finder=self.env.asset_finder,
            name="Daily"
        )

        self.saved_dt = self.period_start
        # one indexed so that we reach 100%
        self.day_count = 0.0
        self.txn_count = 0

        self.account_needs_update = True
        self._account = None

        self._perf_periods = [self.cumulative_performance,
                              self.todays_performance]

    @property
    def perf_periods(self):
        return self._perf_periods

    def __repr__(self):
        return "%s(%r)" % (
            self.__class__.__name__,
            {'simulation parameters': self.sim_params})

    @property
    def progress(self):
        if self.emission_rate == 'minute':
            # Fake a value
            return 1.0
        elif self.emission_rate == 'daily':
            return self.day_count / self.total_days

    def set_date(self, date):
        if self.emission_rate == 'minute':
            self.saved_dt = date
            self.todays_performance.period_close = self.saved_dt

    def get_portfolio(self, dt):
        position_tracker = self.position_tracker
        position_tracker.sync_last_sale_prices(dt)
        pos_stats = position_tracker.stats()
        period_stats = self.cumulative_performance.stats(
            position_tracker.positions, pos_stats, self._data_portal)
        return self.cumulative_performance.as_portfolio(
            pos_stats,
            period_stats,
            position_tracker,
            dt)

    def get_account(self, dt):
        self.position_tracker.sync_last_sale_prices(dt)
        pos_stats = self.position_tracker.stats()
        period_stats = self.cumulative_performance.stats(
            self.position_tracker.positions, pos_stats, self._data_portal)
        self._account = self.cumulative_performance.as_account(
            pos_stats, period_stats)
        return self._account

    def to_dict(self, emission_type=None):
        """
        Wrapper for serialization compatibility.
        """
        pos_stats = self.position_tracker.stats()
        cumulative_stats = self.cumulative_performance.stats(
            self.position_tracker.positions, pos_stats, self._data_portal)
        todays_stats = self.todays_performance.stats(
            self.position_tracker.positions, pos_stats, self._data_portal)

        return self._to_dict(pos_stats,
                             cumulative_stats,
                             todays_stats,
                             emission_type)

    def _to_dict(self, pos_stats, cumulative_stats, todays_stats,
                 emission_type=None):
        """
        Creates a dictionary representing the state of this tracker.
        Returns a dict object of the form described in header comments.

        Use this method internally, when stats are available.
        """
        # Default to the emission rate of this tracker if no type is provided
        if emission_type is None:
            emission_type = self.emission_rate

        position_tracker = self.position_tracker

        _dict = {
            'period_start': self.period_start,
            'period_end': self.period_end,
            'capital_base': self.capital_base,
            'cumulative_perf': self.cumulative_performance.to_dict(
                pos_stats, cumulative_stats, position_tracker,
            ),
            'progress': self.progress,
            'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
        }
        if emission_type == 'daily':
            _dict['daily_perf'] = self.todays_performance.to_dict(
                pos_stats,
                todays_stats,
                position_tracker)
        elif emission_type == 'minute':
            _dict['minute_perf'] = self.todays_performance.to_dict(
                pos_stats,
                todays_stats,
                position_tracker,
                self.saved_dt)
        else:
            raise ValueError("Invalid emission type: %s" % emission_type)

        return _dict

    def copy_state_from(self, other_perf_tracker):
        self.all_benchmark_returns = other_perf_tracker.all_benchmark_returns

        if other_perf_tracker.position_tracker:
            self.position_tracker._unpaid_dividends = \
                other_perf_tracker.position_tracker._unpaid_dividends

            self.position_tracker._unpaid_stock_dividends = \
                other_perf_tracker.position_tracker._unpaid_stock_dividends

    def process_transaction(self, transaction):
        self.txn_count += 1
        self.position_tracker.execute_transaction(transaction)
        self.cumulative_performance.handle_execution(transaction)
        self.todays_performance.handle_execution(transaction)

    def handle_splits(self, splits):
        leftover_cash = self.position_tracker.handle_splits(splits)
        if leftover_cash > 0:
            self.cumulative_performance.handle_cash_payment(leftover_cash)
            self.todays_performance.handle_cash_payment(leftover_cash)

    def process_order(self, event):
        self.cumulative_performance.record_order(event)
        self.todays_performance.record_order(event)

    def process_commission(self, commission):
        sid = commission["sid"]
        cost = commission["cost"]

        self.position_tracker.handle_commission(sid, cost)
        self.cumulative_performance.handle_commission(cost)
        self.todays_performance.handle_commission(cost)

    def process_close_position(self, event):
        txn = self.position_tracker.\
            maybe_create_close_position_transaction(event)
        if txn:
            self.process_transaction(txn)

    def check_upcoming_dividends(self, next_trading_day):
        """
        Check if we currently own any stocks with dividends whose ex_date is
        the next trading day.  Track how much we should be payed on those
        dividends' pay dates.

        Then check if we are owed cash/stock for any dividends whose pay date
        is the next trading day.  Apply all such benefits, then recalculate
        performance.
        """
        if self._adjustment_reader is None:
            return
        position_tracker = self.position_tracker
        held_sids = set(position_tracker.positions)
        # Dividends whose ex_date is the next trading day.  We need to check if
        # we own any of these stocks so we know to pay them out when the pay
        # date comes.
        if held_sids:
            dividends_earnable = self._adjustment_reader.\
                get_dividends_with_ex_date(held_sids, next_trading_day)
            stock_dividends = self._adjustment_reader.\
                get_stock_dividends_with_ex_date(held_sids, next_trading_day)
            position_tracker.earn_dividends(dividends_earnable,
                                            stock_dividends)

        net_cash_payment = position_tracker.pay_dividends(next_trading_day)
        if not net_cash_payment:
            return

        self.cumulative_performance.handle_dividends_paid(net_cash_payment)
        self.todays_performance.handle_dividends_paid(net_cash_payment)

    def check_asset_auto_closes(self, next_trading_day):
        """
        Check if the position tracker currently owns any Assets with an
        auto-close date that is the next trading day.  Close those positions.

        Parameters
        ----------
        next_trading_day : pandas.Timestamp
            The next trading day of the simulation
        """
        auto_close_events = self.position_tracker.auto_close_position_events(
            next_trading_day=next_trading_day
        )
        for event in auto_close_events:
            self.process_close_position(event)

    def handle_minute_close(self, dt):
        """
        Handles the close of the given minute. This includes handling
        market-close functions if the given minute is the end of the market
        day.

        Parameters
        __________
        dt : Timestamp
            The minute that is ending

        Returns
        _______
        (dict, dict/None)
            A tuple of the minute perf packet and daily perf packet.
            If the market day has not ended, the daily perf packet is None.
        """
        todays_date = normalize_date(dt)
        account = self.get_account(dt)

        bench_returns = self.all_benchmark_returns.loc[todays_date:dt]
        # cumulative returns
        bench_since_open = (1. + bench_returns).prod() - 1

        self.position_tracker.sync_last_sale_prices(dt)
        pos_stats = self.position_tracker.stats()
        cumulative_stats = self.cumulative_performance.stats(
            self.position_tracker.positions, pos_stats, self._data_portal
        )
        todays_stats = self.todays_performance.stats(
            self.position_tracker.positions, pos_stats, self._data_portal
        )
        self.cumulative_risk_metrics.update(todays_date,
                                            todays_stats.returns,
                                            bench_since_open,
                                            account)

        minute_packet = self._to_dict(pos_stats,
                                      cumulative_stats,
                                      todays_stats,
                                      emission_type='minute')

        if dt == self.market_close:
            # if this is the last minute of the day, we also want to
            # emit a daily packet.
            return minute_packet, self._handle_market_close(todays_date,
                                                            pos_stats,
                                                            todays_stats)
        else:
            return minute_packet, None

    def handle_market_close_daily(self, dt):
        """
        Function called after handle_data when running with daily emission
        rate.
        """
        completed_date = normalize_date(dt)

        self.position_tracker.sync_last_sale_prices(dt)

        pos_stats = self.position_tracker.stats()
        todays_stats = self.todays_performance.stats(
            self.position_tracker.positions, pos_stats, self._data_portal
        )
        account = self.get_account(completed_date)

        # update risk metrics for cumulative performance
        benchmark_value = self.all_benchmark_returns[completed_date]

        self.cumulative_risk_metrics.update(
            completed_date,
            todays_stats.returns,
            benchmark_value,
            account)

        daily_packet = self._handle_market_close(completed_date,
                                                 pos_stats,
                                                 todays_stats)

        return daily_packet

    def _handle_market_close(self, completed_date, pos_stats, todays_stats):

        # increment the day counter before we move markers forward.
        self.day_count += 1.0

        # Get the next trading day and, if it is past the bounds of this
        # simulation, return the daily perf packet
        next_trading_day = self.env.next_trading_day(completed_date)

        # Check if any assets need to be auto-closed before generating today's
        # perf period
        if next_trading_day:
            self.check_asset_auto_closes(next_trading_day=next_trading_day)

        # Take a snapshot of our current performance to return to the
        # browser.
        cumulative_stats = self.cumulative_performance.stats(
            self.position_tracker.positions,
            pos_stats, self._data_portal)
        daily_update = self._to_dict(pos_stats,
                                     cumulative_stats,
                                     todays_stats,
                                     emission_type='daily')

        # On the last day of the test, don't create tomorrow's performance
        # period.  We may not be able to find the next trading day if we're at
        # the end of our historical data
        if self.market_close >= self.last_close:
            return daily_update

        # move the market day markers forward
        self.market_open, self.market_close = \
            self.env.next_open_and_close(self.day)
        self.day = self.env.next_trading_day(self.day)

        # Roll over positions to current day.
        self.todays_performance.rollover(pos_stats, todays_stats)
        self.todays_performance.period_open = self.market_open
        self.todays_performance.period_close = self.market_close

        # If the next trading day is irrelevant, then return the daily packet
        if (next_trading_day is None) or (next_trading_day >= self.last_close):
            return daily_update

        # Check for any dividends and auto-closes, then return the daily perf
        # packet
        self.check_upcoming_dividends(next_trading_day=next_trading_day)
        return daily_update

    def handle_simulation_end(self):
        """
        When the simulation is complete, run the full period risk report
        and send it out on the results socket.
        """

        log_msg = "Simulated {n} trading days out of {m}."
        log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
        log.info("first open: {d}".format(
            d=self.sim_params.first_open))
        log.info("last close: {d}".format(
            d=self.sim_params.last_close))

        bms = pd.Series(
            index=self.cumulative_risk_metrics.cont_index,
            data=self.cumulative_risk_metrics.benchmark_returns_cont)
        ars = pd.Series(
            index=self.cumulative_risk_metrics.cont_index,
            data=self.cumulative_risk_metrics.algorithm_returns_cont)
        acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
        self.risk_report = risk.RiskReport(
            ars,
            self.sim_params,
            benchmark_returns=bms,
            algorithm_leverages=acl,
            env=self.env)

        risk_dict = self.risk_report.to_dict()
        return risk_dict

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

        STATE_VERSION = 4
        state_dict[VERSION_LABEL] = STATE_VERSION

        return state_dict

    def __setstate__(self, state):

        OLDEST_SUPPORTED_STATE = 4
        version = state.pop(VERSION_LABEL)

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

        self.__dict__.update(state)


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			"""
17
18			Performance Tracking
19			====================
20
21			+-----------------+----------------------------------------------------+
22			\| key \| value \|
23			+=================+====================================================+
24			\| period_start \| The beginning of the period to be tracked. datetime\|
25			\| \| in pytz.utc timezone. Will always be 0:00 on the \|
26			\| \| date in UTC. The fact that the time may be on the \|
27			\| \| prior day in the exchange's local time is ignored \|
28			+-----------------+----------------------------------------------------+
29			\| period_end \| The end of the period to be tracked. datetime \|
30			\| \| in pytz.utc timezone. Will always be 23:59 on the \|
31			\| \| date in UTC. The fact that the time may be on the \|
32			\| \| next day in the exchange's local time is ignored \|
33			+-----------------+----------------------------------------------------+
34			\| progress \| percentage of test completed \|
35			+-----------------+----------------------------------------------------+
36			\| capital_base \| The initial capital assumed for this tracker. \|
37			+-----------------+----------------------------------------------------+
38			\| cumulative_perf \| A dictionary representing the cumulative \|
39			\| \| performance through all the events delivered to \|
40			\| \| this tracker. For details see the comments on \|
41			\| \| :py:meth:`PerformancePeriod.to_dict` \|
42			+-----------------+----------------------------------------------------+
43			\| todays_perf \| A dictionary representing the cumulative \|
44			\| \| performance through all the events delivered to \|
45			\| \| this tracker with datetime stamps between last_open\|
46			\| \| and last_close. For details see the comments on \|
47			\| \| :py:meth:`PerformancePeriod.to_dict` \|
48			\| \| TODO: adding this because we calculate it. May be \|
49			\| \| overkill. \|
50			+-----------------+----------------------------------------------------+
51			\| cumulative_risk \| A dictionary representing the risk metrics \|
52			\| _metrics \| calculated based on the positions aggregated \|
53			\| \| through all the events delivered to this tracker. \|
54			\| \| For details look at the comments for \|
55			\| \| :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`\|
56			+-----------------+----------------------------------------------------+
57
58			"""
59
60			from __future__ import division
61			import logbook
62			from six import iteritems
63			from datetime import datetime
64
65			import pandas as pd
66			from pandas.tseries.tools import normalize_date
67			from zipline.finance.performance.period import PerformancePeriod
68
69			import zipline.finance.risk as risk
70
71			from zipline.utils.serialization_utils import (
72			VERSION_LABEL
73			)
74			from . position_tracker import PositionTracker
75
76			log = logbook.Logger('Performance')
77
78
79			class PerformanceTracker(object):
80			"""
81			Tracks the performance of the algorithm.
82			"""
83			def __init__(self, sim_params, env, data_portal):
84			self.sim_params = sim_params
85			self.env = env
86
87			self.period_start = self.sim_params.period_start
88			self.period_end = self.sim_params.period_end
89			self.last_close = self.sim_params.last_close
90			first_open = self.sim_params.first_open.tz_convert(
91			self.env.exchange_tz
92			)
93			self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
94			first_open.day), tz='UTC')
95			self.market_open, self.market_close = env.get_open_and_close(self.day)
96			self.total_days = self.sim_params.days_in_period
97			self.capital_base = self.sim_params.capital_base
98			self.emission_rate = sim_params.emission_rate
99
100			all_trading_days = env.trading_days
101			mask = ((all_trading_days >= normalize_date(self.period_start)) &
102			(all_trading_days <= normalize_date(self.period_end)))
103
104			self.trading_days = all_trading_days[mask]
105
106			self._data_portal = data_portal
107			if data_portal is not None:
108			self._adjustment_reader = data_portal._adjustment_reader
109			else:
110			self._adjustment_reader = None
111
112			self.position_tracker = PositionTracker(
113			asset_finder=env.asset_finder,
114			data_portal=data_portal,
115			data_frequency=self.sim_params.data_frequency)
116
117			if self.emission_rate == 'daily':
118			self.all_benchmark_returns = pd.Series(
119			index=self.trading_days)
120			self.cumulative_risk_metrics = \
121			risk.RiskMetricsCumulative(self.sim_params, self.env)
122			elif self.emission_rate == 'minute':
123			self.all_benchmark_returns = pd.Series(index=pd.date_range(
124			self.sim_params.first_open, self.sim_params.last_close,
125			freq='Min'))
126
127			self.cumulative_risk_metrics = \
128			risk.RiskMetricsCumulative(self.sim_params, self.env,
129			create_first_day_stats=True)
130
131			# this performance period will span the entire simulation from
132			# inception.
133			self.cumulative_performance = PerformancePeriod(
134			# initial cash is your capital base.
135			starting_cash=self.capital_base,
136			data_frequency=self.sim_params.data_frequency,
137			# the cumulative period will be calculated over the entire test.
138			period_open=self.period_start,
139			period_close=self.period_end,
140			# don't save the transactions for the cumulative
141			# period
142			keep_transactions=False,
143			keep_orders=False,
144			# don't serialize positions for cumulative period
145			serialize_positions=False,
146			asset_finder=self.env.asset_finder,
147			name="Cumulative"
148			)
149
150			# this performance period will span just the current market day
151			self.todays_performance = PerformancePeriod(
152			# initial cash is your capital base.
153			starting_cash=self.capital_base,
154			data_frequency=self.sim_params.data_frequency,
155			# the daily period will be calculated for the market day
156			period_open=self.market_open,
157			period_close=self.market_close,
158			keep_transactions=True,
159			keep_orders=True,
160			serialize_positions=True,
161			asset_finder=self.env.asset_finder,
162			name="Daily"
163			)
164
165			self.saved_dt = self.period_start
166			# one indexed so that we reach 100%
167			self.day_count = 0.0
168			self.txn_count = 0
169
170			self.account_needs_update = True
171			self._account = None
172
173			self._perf_periods = [self.cumulative_performance,
174			self.todays_performance]
175
176			@property
177			def perf_periods(self):
178			return self._perf_periods
179
180			def __repr__(self):
181			return "%s(%r)" % (
182			self.__class__.__name__,
183			{'simulation parameters': self.sim_params})
184
185			@property
186			def progress(self):
187			if self.emission_rate == 'minute':
188			# Fake a value
189			return 1.0
190			elif self.emission_rate == 'daily':
191			return self.day_count / self.total_days
192
193			def set_date(self, date):
194			if self.emission_rate == 'minute':
195			self.saved_dt = date
196			self.todays_performance.period_close = self.saved_dt
197
198			def get_portfolio(self, dt):
199			position_tracker = self.position_tracker
200			position_tracker.sync_last_sale_prices(dt)
201			pos_stats = position_tracker.stats()
202			period_stats = self.cumulative_performance.stats(
203			position_tracker.positions, pos_stats, self._data_portal)
204			return self.cumulative_performance.as_portfolio(
205			pos_stats,
206			period_stats,
207			position_tracker,
208			dt)
209
210			def get_account(self, dt):
211			self.position_tracker.sync_last_sale_prices(dt)
212			pos_stats = self.position_tracker.stats()
213			period_stats = self.cumulative_performance.stats(
214			self.position_tracker.positions, pos_stats, self._data_portal)
215			self._account = self.cumulative_performance.as_account(
216			pos_stats, period_stats)
217			return self._account
218
219			def to_dict(self, emission_type=None):
220			"""
221			Wrapper for serialization compatibility.
222			"""
223			pos_stats = self.position_tracker.stats()
224			cumulative_stats = self.cumulative_performance.stats(
225			self.position_tracker.positions, pos_stats, self._data_portal)
226			todays_stats = self.todays_performance.stats(
227			self.position_tracker.positions, pos_stats, self._data_portal)
228
229			return self._to_dict(pos_stats,
230			cumulative_stats,
231			todays_stats,
232			emission_type)
233
234			def _to_dict(self, pos_stats, cumulative_stats, todays_stats,
235			emission_type=None):
236			"""
237			Creates a dictionary representing the state of this tracker.
238			Returns a dict object of the form described in header comments.
239
240			Use this method internally, when stats are available.
241			"""
242			# Default to the emission rate of this tracker if no type is provided
243			if emission_type is None:
244			emission_type = self.emission_rate
245
246			position_tracker = self.position_tracker
247
248			_dict = {
249			'period_start': self.period_start,
250			'period_end': self.period_end,
251			'capital_base': self.capital_base,
252			'cumulative_perf': self.cumulative_performance.to_dict(
253			pos_stats, cumulative_stats, position_tracker,
254			),
255			'progress': self.progress,
256			'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
257			}
258			if emission_type == 'daily':
259			_dict['daily_perf'] = self.todays_performance.to_dict(
260			pos_stats,
261			todays_stats,
262			position_tracker)
263			elif emission_type == 'minute':
264			_dict['minute_perf'] = self.todays_performance.to_dict(
265			pos_stats,
266			todays_stats,
267			position_tracker,
268			self.saved_dt)
269			else:
270			raise ValueError("Invalid emission type: %s" % emission_type)
271
272			return _dict
273
274			def copy_state_from(self, other_perf_tracker):
275			self.all_benchmark_returns = other_perf_tracker.all_benchmark_returns
276
277			if other_perf_tracker.position_tracker:
278			self.position_tracker._unpaid_dividends = \
279			other_perf_tracker.position_tracker._unpaid_dividends
280
281			self.position_tracker._unpaid_stock_dividends = \
282			other_perf_tracker.position_tracker._unpaid_stock_dividends
283
284			def process_transaction(self, transaction):
285			self.txn_count += 1
286			self.position_tracker.execute_transaction(transaction)
287			self.cumulative_performance.handle_execution(transaction)
288			self.todays_performance.handle_execution(transaction)
289
290			def handle_splits(self, splits):
291			leftover_cash = self.position_tracker.handle_splits(splits)
292			if leftover_cash > 0:
293			self.cumulative_performance.handle_cash_payment(leftover_cash)
294			self.todays_performance.handle_cash_payment(leftover_cash)
295
296			def process_order(self, event):
297			self.cumulative_performance.record_order(event)
298			self.todays_performance.record_order(event)
299
300			def process_commission(self, commission):
301			sid = commission["sid"]
302			cost = commission["cost"]
303
304			self.position_tracker.handle_commission(sid, cost)
305			self.cumulative_performance.handle_commission(cost)
306			self.todays_performance.handle_commission(cost)
307
308			def process_close_position(self, event):
309			txn = self.position_tracker.\
310			maybe_create_close_position_transaction(event)
311			if txn:
312			self.process_transaction(txn)
313
314			def check_upcoming_dividends(self, next_trading_day):
315			"""
316			Check if we currently own any stocks with dividends whose ex_date is
317			the next trading day. Track how much we should be payed on those
318			dividends' pay dates.
319
320			Then check if we are owed cash/stock for any dividends whose pay date
321			is the next trading day. Apply all such benefits, then recalculate
322			performance.
323			"""
324			if self._adjustment_reader is None:
325			return
326			position_tracker = self.position_tracker
327			held_sids = set(position_tracker.positions)
328			# Dividends whose ex_date is the next trading day. We need to check if
329			# we own any of these stocks so we know to pay them out when the pay
330			# date comes.
331			if held_sids:
332			dividends_earnable = self._adjustment_reader.\
333			get_dividends_with_ex_date(held_sids, next_trading_day)
334			stock_dividends = self._adjustment_reader.\
335			get_stock_dividends_with_ex_date(held_sids, next_trading_day)
336			position_tracker.earn_dividends(dividends_earnable,
337			stock_dividends)
338
339			net_cash_payment = position_tracker.pay_dividends(next_trading_day)
340			if not net_cash_payment:
341			return
342
343			self.cumulative_performance.handle_dividends_paid(net_cash_payment)
344			self.todays_performance.handle_dividends_paid(net_cash_payment)
345
346			def check_asset_auto_closes(self, next_trading_day):
347			"""
348			Check if the position tracker currently owns any Assets with an
349			auto-close date that is the next trading day. Close those positions.
350
351			Parameters
352			----------
353			next_trading_day : pandas.Timestamp
354			The next trading day of the simulation
355			"""
356			auto_close_events = self.position_tracker.auto_close_position_events(
357			next_trading_day=next_trading_day
358			)
359			for event in auto_close_events:
360			self.process_close_position(event)
361
362			def handle_minute_close(self, dt):
363			"""
364			Handles the close of the given minute. This includes handling
365			market-close functions if the given minute is the end of the market
366			day.
367
368			Parameters
369			__________
370			dt : Timestamp
371			The minute that is ending
372
373			Returns
374			_______
375			(dict, dict/None)
376			A tuple of the minute perf packet and daily perf packet.
377			If the market day has not ended, the daily perf packet is None.
378			"""
379			todays_date = normalize_date(dt)
380			account = self.get_account(dt)
381
382			bench_returns = self.all_benchmark_returns.loc[todays_date:dt]
383			# cumulative returns
384			bench_since_open = (1. + bench_returns).prod() - 1
385
386			self.position_tracker.sync_last_sale_prices(dt)
387			pos_stats = self.position_tracker.stats()
388			cumulative_stats = self.cumulative_performance.stats(
389			self.position_tracker.positions, pos_stats, self._data_portal
390			)
391			todays_stats = self.todays_performance.stats(
392			self.position_tracker.positions, pos_stats, self._data_portal
393			)
394			self.cumulative_risk_metrics.update(todays_date,
395			todays_stats.returns,
396			bench_since_open,
397			account)
398
399			minute_packet = self._to_dict(pos_stats,
400			cumulative_stats,
401			todays_stats,
402			emission_type='minute')
403
404			if dt == self.market_close:
405			# if this is the last minute of the day, we also want to
406			# emit a daily packet.
407			return minute_packet, self._handle_market_close(todays_date,
408			pos_stats,
409			todays_stats)
410			else:
411			return minute_packet, None
412
413			def handle_market_close_daily(self, dt):
414			"""
415			Function called after handle_data when running with daily emission
416			rate.
417			"""
418			completed_date = normalize_date(dt)
419
420			self.position_tracker.sync_last_sale_prices(dt)
421
422			pos_stats = self.position_tracker.stats()
423			todays_stats = self.todays_performance.stats(
424			self.position_tracker.positions, pos_stats, self._data_portal
425			)
426			account = self.get_account(completed_date)
427
428			# update risk metrics for cumulative performance
429			benchmark_value = self.all_benchmark_returns[completed_date]
430
431			self.cumulative_risk_metrics.update(
432			completed_date,
433			todays_stats.returns,
434			benchmark_value,
435			account)
436
437			daily_packet = self._handle_market_close(completed_date,
438			pos_stats,
439			todays_stats)
440
441			return daily_packet
442
443			def _handle_market_close(self, completed_date, pos_stats, todays_stats):
444
445			# increment the day counter before we move markers forward.
446			self.day_count += 1.0
447
448			# Get the next trading day and, if it is past the bounds of this
449			# simulation, return the daily perf packet
450			next_trading_day = self.env.next_trading_day(completed_date)
451
452			# Check if any assets need to be auto-closed before generating today's
453			# perf period
454			if next_trading_day:
455			self.check_asset_auto_closes(next_trading_day=next_trading_day)
456
457			# Take a snapshot of our current performance to return to the
458			# browser.
459			cumulative_stats = self.cumulative_performance.stats(
460			self.position_tracker.positions,
461			pos_stats, self._data_portal)
462			daily_update = self._to_dict(pos_stats,
463			cumulative_stats,
464			todays_stats,
465			emission_type='daily')
466
467			# On the last day of the test, don't create tomorrow's performance
468			# period. We may not be able to find the next trading day if we're at
469			# the end of our historical data
470			if self.market_close >= self.last_close:
471			return daily_update
472
473			# move the market day markers forward
474			self.market_open, self.market_close = \
475			self.env.next_open_and_close(self.day)
476			self.day = self.env.next_trading_day(self.day)
477
478			# Roll over positions to current day.
479			self.todays_performance.rollover(pos_stats, todays_stats)
480			self.todays_performance.period_open = self.market_open
481			self.todays_performance.period_close = self.market_close
482
483			# If the next trading day is irrelevant, then return the daily packet
484			if (next_trading_day is None) or (next_trading_day >= self.last_close):
485			return daily_update
486
487			# Check for any dividends and auto-closes, then return the daily perf
488			# packet
489			self.check_upcoming_dividends(next_trading_day=next_trading_day)
490			return daily_update
491
492			def handle_simulation_end(self):
493			"""
494			When the simulation is complete, run the full period risk report
495			and send it out on the results socket.
496			"""
497
498			log_msg = "Simulated {n} trading days out of {m}."
499			log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
500			log.info("first open: {d}".format(
501			d=self.sim_params.first_open))
502			log.info("last close: {d}".format(
503			d=self.sim_params.last_close))
504
505			bms = pd.Series(
506			index=self.cumulative_risk_metrics.cont_index,
507			data=self.cumulative_risk_metrics.benchmark_returns_cont)
508			ars = pd.Series(
509			index=self.cumulative_risk_metrics.cont_index,
510			data=self.cumulative_risk_metrics.algorithm_returns_cont)
511			acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
512			self.risk_report = risk.RiskReport(
513			ars,
514			self.sim_params,
515			benchmark_returns=bms,
516			algorithm_leverages=acl,
517			env=self.env)
518
519			risk_dict = self.risk_report.to_dict()
520			return risk_dict
521
522			def __getstate__(self):
523			state_dict = \
524			{k: v for k, v in iteritems(self.__dict__)
525			if not k.startswith('_')}
526
527			STATE_VERSION = 4
528			state_dict[VERSION_LABEL] = STATE_VERSION
529
530			return state_dict
531
532			def __setstate__(self, state):
533
534			OLDEST_SUPPORTED_STATE = 4
535			version = state.pop(VERSION_LABEL)
536
537			if version < OLDEST_SUPPORTED_STATE:
538			raise BaseException("PerformanceTracker saved state is too old.")
539
540			self.__dict__.update(state)
541

quantopian / zipline

Pull Request — master (#858)

process_transaction() A

Complexity

Size

Duplication

Duplication Side-by-Side

Filter issues like