zipline.finance.performance.PerformanceTracker

Complexity

Total Complexity

47

Size/Duplication

Total Lines	462
Duplicated Lines	0 %

23 Methods

Rating	Name	Duplication	Size	Complexity
B	__init__()	0	92	4
A	process_transaction()	0	5	1
B	handle_simulation_end()	0	29	1
A	__getstate__()	0	9	3
B	handle_market_close_daily()	0	29	1
B	check_upcoming_dividends()	0	31	4
A	check_asset_auto_closes()	0	15	2
B	_handle_market_close()	0	48	5
A	get_portfolio()	0	11	1
A	__setstate__()	0	9	2
A	perf_periods()	0	3	1
A	handle_minute_close()	0	50	2
A	copy_state_from()	0	9	2
A	handle_splits()	0	5	2
A	process_commission()	0	7	1
A	to_dict()	0	14	1
B	_to_dict()	0	39	4
A	progress()	0	7	3
A	get_account()	0	8	1
A	process_close_position()	0	5	2
A	set_date()	0	4	2
A	process_order()	0	3	1
A	__repr__()	0	4	1

#
# 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)