<gh_stars>1-10
#
# Copyright 2013 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.

import bisect
import pytz
import logbook
import datetime

from functools import wraps
from delorean import Delorean
import pandas as pd
from pandas import DatetimeIndex

from collections import OrderedDict
from zipline.data.loader import load_market_data


log = logbook.Logger('Transaction Simulator')


# The financial simulations in zipline depend on information
# about the benchmark index and the risk free rates of return.
# The benchmark index defines the benchmark returns used in
# the calculation of performance metrics such as alpha/beta. Many
# components, including risk, performance, transforms, and
# batch_transforms, need access to a calendar of trading days and
# market hours. The TradingEnvironment maintains two time keeping
# facilities:
#   - a DatetimeIndex of trading days for calendar calculations
#   - a timezone name, which should be local to the exchange
#   hosting the benchmark index. All dates are normalized to UTC
#   for serialization and storage, and the timezone is used to
#   ensure proper rollover through daylight savings and so on.
#
# This module maintains a global variable, environment, which is
# subsequently referenced directly by zipline financial
# components. To set the environment, you can set the property on
# the module directly:
#       import zipline.finance.trading as trading
#       trading.environment = TradingEnvironment()
#
# or if you want to switch the environment for a limited context
# you can use a TradingEnvironment in a with clause:
#       lse = TradingEnvironment(bm_index="^FTSE", exchange_tz="Europe/London")
#       with lse:
#           # the code here will have lse as the global trading.environment
#           algo.run(start, end)
#
# User code will not normally need to use TradingEnvironment
# directly. If you are extending zipline's core financial
# compponents and need to use the environment, you must import the module
# NOT the variable. If you import the module, you will get a
# reference to the environment at import time, which will prevent
# your code from responding to user code that changes the global
# state.

environment = None


class TradingEnvironment(object):

    def __init__(
        self,
        load=None,
        bm_symbol='^GSPC',
        exchange_tz="US/Eastern"
    ):
        self.prev_environment = self
        self.trading_day_map = OrderedDict()
        self.bm_symbol = bm_symbol
        if not load:
            load = load_market_data

        self.benchmark_returns, treasury_curves_map = \
            load(self.bm_symbol)

        self.treasury_curves = pd.Series(treasury_curves_map)

        self._period_trading_days = None
        self._trading_days_series = None
        self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
        self.exchange_tz = exchange_tz

        for bm in self.benchmark_returns:
            self.trading_day_map[bm.date] = bm

        self.first_trading_day = next(self.trading_day_map.iterkeys())
        self.last_trading_day = next(reversed(self.trading_day_map))

    def __enter__(self, *args, **kwargs):
        global environment
        self.prev_environment = environment
        environment = self
        # return value here is associated with "as such_and_such" on the
        # with clause.
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        global environment
        environment = self.prev_environment
        # signal that any exceptions need to be propagated up the
        # stack.
        return False

    def normalize_date(self, test_date):
        return datetime.datetime(
            year=test_date.year,
            month=test_date.month,
            day=test_date.day,
            tzinfo=pytz.utc
        )

    def exchange_dt_in_utc(self, dt):
        delorean = Delorean(dt, self.exchange_tz)
        return delorean.shift(pytz.utc.zone).datetime

    @property
    def period_trading_days(self):
        if self._period_trading_days is None:
            self._period_trading_days = []
            for date in self.trading_day_map.iterkeys():
                if date > self.period_end:
                    break
                if date >= self.period_start:
                    self.period_trading_days.append(date)
        return self._period_trading_days

    @property
    def trading_days(self):
        if self._trading_days_series is None:
            self._trading_days_series = \
                DatetimeIndex(self.trading_day_map.iterkeys())
        return self._trading_days_series

    def is_market_hours(self, test_date):
        if not self.is_trading_day(test_date):
            return False

        mkt_open, mkt_close = self.get_open_and_close(test_date)
        return test_date >= mkt_open and test_date <= mkt_close

    def is_trading_day(self, test_date):
        dt = self.normalize_date(test_date)
        return (dt in self.trading_day_map)

    def next_trading_day(self, test_date):
        dt = self.normalize_date(test_date)
        delta = datetime.timedelta(days=1)

        while dt <= self.last_trading_day:
            dt += delta
            if dt in self.trading_day_map:
                return dt

        return None

    def next_open_and_close(self, start_date):
        """
        Given the start_date, returns the next open and close of
        the market.
        """
        next_open = self.next_trading_day(start_date)

        if next_open is None:
            raise Exception(
                "Attempt to backtest beyond available history. \
Last successful date: %s" % self.market_open)

        return self.get_open_and_close(next_open)

    def get_open_and_close(self, next_open):

        # creating a naive datetime with the correct hour,
        # minute, and date. this will allow us to use Delorean to
        # shift the time between EST and UTC.
        next_open = next_open.replace(
            hour=9,
            minute=31,
            second=0,
            microsecond=0,
            tzinfo=None
        )
        # create a new Delorean with the next_open naive date and
        # the correct timezone for the exchange.
        open_utc = self.exchange_dt_in_utc(next_open)

        market_open = open_utc
        market_close = (market_open
                        + self.get_trading_day_duration(open_utc)
                        - datetime.timedelta(minutes=1))

        return market_open, market_close

    def get_trading_day_duration(self, trading_day):
        # TODO: make a list of half-days and modify the
        # calculation of market close to reflect them.
        return self.full_trading_day

    def trading_day_distance(self, first_date, second_date):
        first_date = self.normalize_date(first_date)
        second_date = self.normalize_date(second_date)

        trading_days = self.trading_day_map.keys()
        # Find leftmost item greater than or equal to day
        i = bisect.bisect_left(trading_days, first_date)
        if i == len(trading_days):  # nothing found
            return None
        j = bisect.bisect_left(trading_days, second_date)
        if j == len(trading_days):
            return None

        return j - i

    def get_index(self, dt):
        ndt = self.normalize_date(dt)
        return self.trading_days.searchsorted(ndt)


class SimulationParameters(object):
    def __init__(self, period_start, period_end,
                 capital_base=10e3,
                 emission_rate='daily'):

        global environment
        if not environment:
            # This is the global environment for trading simulation.
            environment = TradingEnvironment()

        self.period_start = period_start
        self.period_end = period_end
        self.capital_base = capital_base

        self.emission_rate = emission_rate

        assert self.period_start <= self.period_end, \
            "Period start falls after period end."

        assert self.period_start <= environment.last_trading_day, \
            "Period start falls after the last known trading day."
        assert self.period_end >= environment.first_trading_day, \
            "Period end falls before the first known trading day."

        self.first_open = self.calculate_first_open()
        self.last_close = self.calculate_last_close()
        start_index = \
            environment.get_index(self.first_open)
        end_index = environment.get_index(self.last_close)

        # take an inclusive slice of the environment's
        # trading_days.
        self.trading_days = \
            environment.trading_days[start_index:end_index + 1]

    def calculate_first_open(self):
        """
        Finds the first trading day on or after self.period_start.
        """
        first_open = self.period_start
        one_day = datetime.timedelta(days=1)

        while not environment.is_trading_day(first_open):
            first_open = first_open + one_day

        mkt_open, _ = environment.get_open_and_close(first_open)
        return mkt_open

    def calculate_last_close(self):
        """
        Finds the last trading day on or before self.period_end
        """
        last_close = self.period_end
        one_day = datetime.timedelta(days=1)

        while not environment.is_trading_day(last_close):
            last_close = last_close - one_day

        _, mkt_close = environment.get_open_and_close(last_close)
        return mkt_close

    @property
    def days_in_period(self):
        """return the number of trading days within the period [start, end)"""
        return len(self.trading_days)

    def __repr__(self):
        return "%s(%r)" % (
            self.__class__.__name__,
            {'first_open': self.first_open,
             'last_close': self.last_close
             })


class use_environment(object):
    """A decorator to wrap a method in a particular
    trading environment."""

    def __init__(self, environment):
        self.env = environment

    def __call__(self, func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            with self.env:
                return func(*args, **kwargs)
        return wrapper

#
# Copyright 2013 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.

import bisect
import pytz
import logbook
import datetime

from functools import wraps
from delorean import Delorean
import pandas as pd
from pandas import DatetimeIndex

from collections import OrderedDict
from zipline.data.loader import load_market_data


log = logbook.Logger('Transaction Simulator')


# The financial simulations in zipline depend on information
# about the benchmark index and the risk free rates of return.
# The benchmark index defines the benchmark returns used in
# the calculation of performance metrics such as alpha/beta. Many
# components, including risk, performance, transforms, and
# batch_transforms, need access to a calendar of trading days and
# market hours. The TradingEnvironment maintains two time keeping
# facilities:
#   - a DatetimeIndex of trading days for calendar calculations
#   - a timezone name, which should be local to the exchange
#   hosting the benchmark index. All dates are normalized to UTC
#   for serialization and storage, and the timezone is used to
#   ensure proper rollover through daylight savings and so on.
#
# This module maintains a global variable, environment, which is
# subsequently referenced directly by zipline financial
# components. To set the environment, you can set the property on
# the module directly:
#       import zipline.finance.trading as trading
#       trading.environment = TradingEnvironment()
#
# or if you want to switch the environment for a limited context
# you can use a TradingEnvironment in a with clause:
#       lse = TradingEnvironment(bm_index="^FTSE", exchange_tz="Europe/London")
#       with lse:
#           # the code here will have lse as the global trading.environment
#           algo.run(start, end)
#
# User code will not normally need to use TradingEnvironment
# directly. If you are extending zipline's core financial
# compponents and need to use the environment, you must import the module
# NOT the variable. If you import the module, you will get a
# reference to the environment at import time, which will prevent
# your code from responding to user code that changes the global
# state.

environment = None


class TradingEnvironment(object):

    def __init__(
        self,
        load=None,
        bm_symbol='^GSPC',
        exchange_tz="US/Eastern"
    ):
        self.prev_environment = self
        self.trading_day_map = OrderedDict()
        self.bm_symbol = bm_symbol
        if not load:
            load = load_market_data

        self.benchmark_returns, treasury_curves_map = \
            load(self.bm_symbol)

        self.treasury_curves = pd.Series(treasury_curves_map)

        self._period_trading_days = None
        self._trading_days_series = None
        self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
        self.exchange_tz = exchange_tz

        for bm in self.benchmark_returns:
            self.trading_day_map[bm.date] = bm

        self.first_trading_day = next(self.trading_day_map.iterkeys())
        self.last_trading_day = next(reversed(self.trading_day_map))

    def __enter__(self, *args, **kwargs):
        global environment
        self.prev_environment = environment
        environment = self
        # return value here is associated with "as such_and_such" on the
        # with clause.
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        global environment
        environment = self.prev_environment
        # signal that any exceptions need to be propagated up the
        # stack.
        return False

    def normalize_date(self, test_date):
        return datetime.datetime(
            year=test_date.year,
            month=test_date.month,
            day=test_date.day,
            tzinfo=pytz.utc
        )

    def exchange_dt_in_utc(self, dt):
        delorean = Delorean(dt, self.exchange_tz)
        return delorean.shift(pytz.utc.zone).datetime

    @property
    def period_trading_days(self):
        if self._period_trading_days is None:
            self._period_trading_days = []
            for date in self.trading_day_map.iterkeys():
                if date > self.period_end:
                    break
                if date >= self.period_start:
                    self.period_trading_days.append(date)
        return self._period_trading_days

    @property
    def trading_days(self):
        if self._trading_days_series is None:
            self._trading_days_series = \
                DatetimeIndex(self.trading_day_map.iterkeys())
        return self._trading_days_series

    def is_market_hours(self, test_date):
        if not self.is_trading_day(test_date):
            return False

        mkt_open, mkt_close = self.get_open_and_close(test_date)
        return test_date >= mkt_open and test_date <= mkt_close

    def is_trading_day(self, test_date):
        dt = self.normalize_date(test_date)
        return (dt in self.trading_day_map)

    def next_trading_day(self, test_date):
        dt = self.normalize_date(test_date)
        delta = datetime.timedelta(days=1)

        while dt <= self.last_trading_day:
            dt += delta
            if dt in self.trading_day_map:
                return dt

        return None

    def next_open_and_close(self, start_date):
        """
        Given the start_date, returns the next open and close of
        the market.
        """
        next_open = self.next_trading_day(start_date)

        if next_open is None:
            raise Exception(
                "Attempt to backtest beyond available history. \
Last successful date: %s" % self.market_open)

        return self.get_open_and_close(next_open)

    def get_open_and_close(self, next_open):

        # creating a naive datetime with the correct hour,
        # minute, and date. this will allow us to use Delorean to
        # shift the time between EST and UTC.
        next_open = next_open.replace(
            hour=9,
            minute=31,
            second=0,
            microsecond=0,
            tzinfo=None
        )
        # create a new Delorean with the next_open naive date and
        # the correct timezone for the exchange.
        open_utc = self.exchange_dt_in_utc(next_open)

        market_open = open_utc
        market_close = (market_open
                        + self.get_trading_day_duration(open_utc)
                        - datetime.timedelta(minutes=1))

        return market_open, market_close

    def get_trading_day_duration(self, trading_day):
        # TODO: make a list of half-days and modify the
        # calculation of market close to reflect them.
        return self.full_trading_day

    def trading_day_distance(self, first_date, second_date):
        first_date = self.normalize_date(first_date)
        second_date = self.normalize_date(second_date)

        trading_days = self.trading_day_map.keys()
        # Find leftmost item greater than or equal to day
        i = bisect.bisect_left(trading_days, first_date)
        if i == len(trading_days):  # nothing found
            return None
        j = bisect.bisect_left(trading_days, second_date)
        if j == len(trading_days):
            return None

        return j - i

    def get_index(self, dt):
        ndt = self.normalize_date(dt)
        return self.trading_days.searchsorted(ndt)


class SimulationParameters(object):
    def __init__(self, period_start, period_end,
                 capital_base=10e3,
                 emission_rate='daily'):

        global environment
        if not environment:
            # This is the global environment for trading simulation.
            environment = TradingEnvironment()

        self.period_start = period_start
        self.period_end = period_end
        self.capital_base = capital_base

        self.emission_rate = emission_rate

        assert self.period_start <= self.period_end, \
            "Period start falls after period end."

        assert self.period_start <= environment.last_trading_day, \
            "Period start falls after the last known trading day."
        assert self.period_end >= environment.first_trading_day, \
            "Period end falls before the first known trading day."

        self.first_open = self.calculate_first_open()
        self.last_close = self.calculate_last_close()
        start_index = \
            environment.get_index(self.first_open)
        end_index = environment.get_index(self.last_close)

        # take an inclusive slice of the environment's
        # trading_days.
        self.trading_days = \
            environment.trading_days[start_index:end_index + 1]

    def calculate_first_open(self):
        """
        Finds the first trading day on or after self.period_start.
        """
        first_open = self.period_start
        one_day = datetime.timedelta(days=1)

        while not environment.is_trading_day(first_open):
            first_open = first_open + one_day

        mkt_open, _ = environment.get_open_and_close(first_open)
        return mkt_open

    def calculate_last_close(self):
        """
        Finds the last trading day on or before self.period_end
        """
        last_close = self.period_end
        one_day = datetime.timedelta(days=1)

        while not environment.is_trading_day(last_close):
            last_close = last_close - one_day

        _, mkt_close = environment.get_open_and_close(last_close)
        return mkt_close

    @property
    def days_in_period(self):
        """return the number of trading days within the period [start, end)"""
        return len(self.trading_days)

    def __repr__(self):
        return "%s(%r)" % (
            self.__class__.__name__,
            {'first_open': self.first_open,
             'last_close': self.last_close
             })


class use_environment(object):
    """A decorator to wrap a method in a particular
    trading environment."""

    def __init__(self, environment):
        self.env = environment

    def __call__(self, func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            with self.env:
                return func(*args, **kwargs)
        return wrapper
# # Copyright 2013 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. # The financial simulations in zipline depend on information # about the benchmark index and the risk free rates of return. # The benchmark index defines the benchmark returns used in # the calculation of performance metrics such as alpha/beta. Many # components, including risk, performance, transforms, and # batch_transforms, need access to a calendar of trading days and # market hours. The TradingEnvironment maintains two time keeping # facilities: #   - a DatetimeIndex of trading days for calendar calculations #   - a timezone name, which should be local to the exchange #   hosting the benchmark index. All dates are normalized to UTC #   for serialization and storage, and the timezone is used to #   ensure proper rollover through daylight savings and so on. # # This module maintains a global variable, environment, which is # subsequently referenced directly by zipline financial # components. To set the environment, you can set the property on # the module directly: #       import zipline.finance.trading as trading #       trading.environment = TradingEnvironment() # # or if you want to switch the environment for a limited context # you can use a TradingEnvironment in a with clause: #       lse = TradingEnvironment(bm_index="^FTSE", exchange_tz="Europe/London") #       with lse: #           # the code here will have lse as the global trading.environment #           algo.run(start, end) # # User code will not normally need to use TradingEnvironment # directly. If you are extending zipline's core financial # compponents and need to use the environment, you must import the module # NOT the variable. If you import the module, you will get a # reference to the environment at import time, which will prevent # your code from responding to user code that changes the global # state. # return value here is associated with "as such_and_such" on the # with clause. # signal that any exceptions need to be propagated up the # stack. Given the start_date, returns the next open and close of         the market. # creating a naive datetime with the correct hour, # minute, and date. this will allow us to use Delorean to # shift the time between EST and UTC. # create a new Delorean with the next_open naive date and # the correct timezone for the exchange. # TODO: make a list of half-days and modify the # calculation of market close to reflect them. # Find leftmost item greater than or equal to day # nothing found # This is the global environment for trading simulation. # take an inclusive slice of the environment's # trading_days. Finds the first trading day on or after self.period_start. Finds the last trading day on or before self.period_end return the number of trading days within the period [start, end) A decorator to wrap a method in a particular     trading environment.