#
# Copyright 2016 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial

import logbook
import pandas as pd
from pandas.tslib import normalize_date
from six import string_types
from sqlalchemy import create_engine

from catalyst.assets import AssetDBWriter, AssetFinder
from catalyst.assets.continuous_futures import CHAIN_PREDICATES
from catalyst.data.loader import load_market_data
from catalyst.utils.calendars import get_calendar
from catalyst.utils.memoize import remember_last

from catalyst.constants import LOG_LEVEL

log = logbook.Logger('Trading', level=LOG_LEVEL)


DEFAULT_CAPITAL_BASE = 1e5


class TradingEnvironment(object):
    """
    The financial simulations in catalyst 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.

    User code will not normally need to use TradingEnvironment
    directly. If you are extending catalyst's core financial
    components and need to use the environment, you must import the module and
    build a new TradingEnvironment object, then pass that TradingEnvironment as
    the 'env' arg to your TradingAlgorithm.

    Parameters
    ----------
    load : callable, optional
        The function that returns benchmark returns and treasury curves.
        The treasury curves are expected to be a DataFrame with an index of
        dates and columns of the curve names, e.g. '10year', '1month', etc.
    bm_symbol : str, optional
        The benchmark symbol
    exchange_tz : tz-coercable, optional
        The timezone of the exchange.
    trading_calendar : TradingCalendar, optional
        The trading calendar to work with in this environment.
    asset_db_path : str or sa.engine.Engine, optional
        The path to the assets db or sqlalchemy Engine object to use to
        construct an AssetFinder.
    """

    # Token used as a substitute for pickling objects that contain a
    # reference to a TradingEnvironment
    PERSISTENT_TOKEN = "<TradingEnvironment>"

    def __init__(
        self,
        load=None,
        bm_symbol='SPY',
        exchange_tz="US/Eastern",
        trading_calendar=None,
        asset_db_path=':memory:',
        future_chain_predicates=CHAIN_PREDICATES,
        environ=None,
    ):

        self.bm_symbol = bm_symbol
        if not load:
            load = partial(load_market_data, environ=environ)

        if not trading_calendar:
            trading_calendar = get_calendar("NYSE")

        # todo: uncomment and add a well defined benchmark
        # self.benchmark_returns, self.treasury_curves = load(
        #     trading_calendar.day,
        #     trading_calendar.schedule.index,
        #     self.bm_symbol,
        #     exchange=exchange,
        # )

        start_data = get_calendar('OPEN').first_trading_session
        end_data = pd.Timestamp.utcnow()
        treasure_cols = ['1month', '3month', '6month', '1year', '2year',
                         '3year', '5year', '7year', '10year', '20year', '30year']
        self.benchmark_returns = pd.DataFrame(data=0.001,
                                              index=pd.date_range(start_data, end_data),
                                              columns=['close'])
        self.treasury_curves = pd.DataFrame(data=0.001,
                                            index=pd.date_range(start_data, end_data),
                                            columns=treasure_cols)

        self.exchange_tz = exchange_tz

        if isinstance(asset_db_path, string_types):
            asset_db_path = 'sqlite:///' + asset_db_path
            self.engine = engine = create_engine(asset_db_path)
        else:
            self.engine = engine = asset_db_path

        if engine is not None:
            AssetDBWriter(engine).init_db()
            self.asset_finder = AssetFinder(
                engine,
                future_chain_predicates=future_chain_predicates)
        else:
            self.asset_finder = None

    def write_data(self, **kwargs):
        """Write data into the asset_db.

        Parameters
        ----------
        **kwargs
            Forwarded to AssetDBWriter.write
        """
        AssetDBWriter(self.engine).write(**kwargs)


class SimulationParameters(object):
    def __init__(self, start_session, end_session,
                 trading_calendar,
                 capital_base=DEFAULT_CAPITAL_BASE,
                 emission_rate='daily',
                 data_frequency='daily',
                 arena='backtest'):

        assert type(start_session) == pd.Timestamp
        assert type(end_session) == pd.Timestamp

        assert trading_calendar is not None, \
            "Must pass in trading calendar!"
        assert start_session <= end_session, \
            "Period start falls after period end."
        assert start_session <= trading_calendar.last_trading_session, \
            "Period start falls after the last known trading day."
        assert end_session >= trading_calendar.first_trading_session, \
            "Period end falls before the first known trading day."

        # chop off any minutes or hours on the given start and end dates,
        # as we only support session labels here (and we represent session
        # labels as midnight UTC).
        self._start_session = normalize_date(start_session)
        self._end_session = normalize_date(end_session)
        self._capital_base = capital_base

        self._emission_rate = emission_rate
        self._data_frequency = data_frequency

        # copied to algorithm's environment for runtime access
        self._arena = arena

        self._trading_calendar = trading_calendar

        if not trading_calendar.is_session(self._start_session):
            # if the start date is not a valid session in this calendar,
            # push it forward to the first valid session
            self._start_session = trading_calendar.minute_to_session_label(
                self._start_session
            )

        if not trading_calendar.is_session(self._end_session):
            # if the end date is not a valid session in this calendar,
            # pull it backward to the last valid session before the given
            # end date.
            self._end_session = trading_calendar.minute_to_session_label(
                self._end_session, direction="previous"
            )

        self._first_open = trading_calendar.open_and_close_for_session(
            self._start_session
        )[0]
        self._last_close = trading_calendar.open_and_close_for_session(
            self._end_session
        )[1]

    @property
    def capital_base(self):
        return self._capital_base

    @property
    def emission_rate(self):
        return self._emission_rate

    @property
    def data_frequency(self):
        return self._data_frequency

    @data_frequency.setter
    def data_frequency(self, val):
        self._data_frequency = val

    @property
    def arena(self):
        return self._arena

    @arena.setter
    def arena(self, val):
        self._arena = val

    @property
    def start_session(self):
        return self._start_session

    @property
    def end_session(self):
        return self._end_session

    @property
    def first_open(self):
        return self._first_open

    @property
    def last_close(self):
        return self._last_close

    @property
    @remember_last
    def sessions(self):
        return self._trading_calendar.sessions_in_range(
            self.start_session,
            self.end_session
        )

    def create_new(self, start_session, end_session):
        return SimulationParameters(
            start_session,
            end_session,
            self._trading_calendar,
            capital_base=self.capital_base,
            emission_rate=self.emission_rate,
            data_frequency=self.data_frequency,
            arena=self.arena
        )

    def __repr__(self):
        return """
{class_name}(
    start_session={start_session},
    end_session={end_session},
    capital_base={capital_base},
    data_frequency={data_frequency},
    emission_rate={emission_rate},
    first_open={first_open},
    last_close={last_close})\
""".format(class_name=self.__class__.__name__,
           start_session=self.start_session,
           end_session=self.end_session,
           capital_base=self.capital_base,
           data_frequency=self.data_frequency,
           emission_rate=self.emission_rate,
           first_open=self.first_open,
           last_close=self.last_close)


def noop_load(*args, **kwargs):
    """
    A method that can be substituted in as the load method in a
    TradingEnvironment to prevent it from loading benchmarks.

    Accepts any arguments, but returns only a tuple of Nones regardless
    of input.
    """
    return None, None
