#
# Copyright 2012 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.

"""

Risk Report
===========

    +-----------------+----------------------------------------------------+
    | key             | value                                              |
    +=================+====================================================+
    | trading_days    | The number of trading days between self.start_date |
    |                 | and self.end_date                                  |
    +-----------------+----------------------------------------------------+
    | benchmark_volat\| The volatility of the benchmark between            |
    | ility           | self.start_date and self.end_date.                 |
    +-----------------+----------------------------------------------------+
    | algo_volatility | The volatility of the algo between self.start_date |
    |                 | and self.end_date.                                 |
    +-----------------+----------------------------------------------------+
    | treasury_period\| The return of treasuries over the period. Treasury |
    | _return         | maturity is chosen to match the duration of the    |
    |                 | test period.                                       |
    +-----------------+----------------------------------------------------+
    | sharpe          | The sharpe ratio based on the _algorithm_ (rather  |
    |                 | than the static portfolio) returns.                |
    +-----------------+----------------------------------------------------+
    | information     | The information ratio based on the _algorithm_     |
    |                 | (rather than the static portfolio) returns.        |
    +-----------------+----------------------------------------------------+
    | beta            | The _algorithm_ beta to the benchmark.             |
    +-----------------+----------------------------------------------------+
    | alpha           | The _algorithm_ alpha to the benchmark.            |
    +-----------------+----------------------------------------------------+
    | excess_return   | The excess return of the algorithm over the        |
    |                 | treasuries.                                        |
    +-----------------+----------------------------------------------------+
    | max_drawdown    | The largest relative peak to relative trough move  |
    |                 | for the portfolio returns between self.start_date  |
    |                 | and self.end_date.                                 |
    +-----------------+----------------------------------------------------+


"""

import logbook
import datetime
import math
import itertools
from collections import OrderedDict
import bisect
import numpy as np
import numpy.linalg as la

import zipline.finance.trading as trading
from zipline.utils.date_utils import epoch_now


log = logbook.Logger('Risk')


TREASURY_DURATIONS = [
    '1month', '3month', '6month',
    '1year', '2year', '3year', '5year',
    '7year', '10year', '30year'
]


def advance_by_months(dt, jump_in_months):
    month = dt.month + jump_in_months
    years = month / 12
    month = month % 12

    # no remainder means that we are landing in december.
    # modulo is, in a way, a zero indexed circular array.
    # this is a way of converting to 1 indexed months.
    # (in our modulo index, december is zeroth)
    if(month == 0):
        month = 12
        years = years - 1

    return dt.replace(year=dt.year + years, month=month)


class RiskMetricsBase(object):
    def __init__(self, start_date, end_date, returns):

        self.treasury_curves = trading.environment.treasury_curves
        assert isinstance(self.treasury_curves, OrderedDict), \
            "Treasury curves must be an OrderedDict"

        self.start_date = start_date
        self.end_date = end_date
        self.algorithm_period_returns, self.algorithm_returns = \
            self.calculate_period_returns(returns)

        benchmark_returns = [
            x for x in trading.environment.benchmark_returns
            if x.date >= returns[0].date and x.date <= returns[-1].date
        ]

        self.benchmark_period_returns, self.benchmark_returns = \
            self.calculate_period_returns(benchmark_returns)

        if(len(self.benchmark_returns) != len(self.algorithm_returns)):
            message = "Mismatch between benchmark_returns ({bm_count}) and \
            algorithm_returns ({algo_count}) in range {start} : {end}"
            message = message.format(
                bm_count=len(self.benchmark_returns),
                algo_count=len(self.algorithm_returns),
                start=start_date,
                end=end_date
            )
            raise Exception(message)

        self.trading_days = len(self.benchmark_returns)
        self.benchmark_volatility = self.calculate_volatility(
            self.benchmark_returns)
        self.algorithm_volatility = self.calculate_volatility(
            self.algorithm_returns)
        self.treasury_period_return = self.choose_treasury()
        self.sharpe = self.calculate_sharpe()
        self.sortino = self.calculate_sortino()
        self.information = self.calculate_information()
        self.beta, self.algorithm_covariance, self.benchmark_variance, \
            self.condition_number, self.eigen_values = self.calculate_beta()
        self.alpha = self.calculate_alpha()
        self.excess_return = self.algorithm_period_returns - \
            self.treasury_period_return
        self.max_drawdown = self.calculate_max_drawdown()

    def to_dict(self):
        """
        Creates a dictionary representing the state of the risk report.
        Returns a dict object of the form:
        """
        period_label = self.end_date.strftime("%Y-%m")
        rval = {
            'trading_days': self.trading_days,
            'benchmark_volatility': self.benchmark_volatility,
            'algo_volatility': self.algorithm_volatility,
            'treasury_period_return': self.treasury_period_return,
            'algorithm_period_return': self.algorithm_period_returns,
            'benchmark_period_return': self.benchmark_period_returns,
            'sharpe': self.sharpe,
            'sortino': self.sortino,
            'information': self.information,
            'beta': self.beta,
            'alpha': self.alpha,
            'excess_return': self.excess_return,
            'max_drawdown': self.max_drawdown,
            'period_label': period_label
        }

        # check if a field in rval is nan, and replace it with
        # None.
        def check_entry(key, value):
            if key != 'period_label':
                return np.isnan(value)
            else:
                return False

        return {k: None if check_entry(k, v) else v
                for k, v in rval.iteritems()}

    def __repr__(self):
        statements = []
        metrics = [
            "algorithm_period_returns",
            "benchmark_period_returns",
            "excess_return",
            "trading_days",
            "benchmark_volatility",
            "algorithm_volatility",
            "sharpe",
            "sortino",
            "information",
            "algorithm_covariance",
            "benchmark_variance",
            "beta",
            "alpha",
            "max_drawdown",
            "algorithm_returns",
            "benchmark_returns",
            "condition_number",
            "eigen_values"
        ]

        for metric in metrics:
            value = getattr(self, metric)
            statements.append("{m}:{v}".format(m=metric, v=value))

        return '\n'.join(statements)

    def calculate_period_returns(self, daily_returns):

        #TODO: replace this with pandas.
        returns = [
            x.returns for x in daily_returns
            if x.date >= self.start_date and
            x.date <= self.end_date and
            trading.environment.is_trading_day(x.date)
        ]

        period_returns = 1.0

        for r in returns:
            period_returns = period_returns * (1.0 + r)

        period_returns = period_returns - 1.0
        return period_returns, returns

    def calculate_volatility(self, daily_returns):
        return np.std(daily_returns, ddof=1) * math.sqrt(self.trading_days)

    def calculate_sharpe(self):
        """
        http://en.wikipedia.org/wiki/Sharpe_ratio
        """
        if self.algorithm_volatility == 0:
            return 0.0

        return ((self.algorithm_period_returns - self.treasury_period_return) /
                self.algorithm_volatility)

    def calculate_sortino(self, mar=None):
        """
        http://en.wikipedia.org/wiki/Sortino_ratio
        """
        if len(self.algorithm_returns) == 0:
            return 0.0

        if mar is None:
            mar = self.treasury_period_return

        downside = [
            (x - mar)**2
            for x in self.algorithm_returns
            if x < mar]
        dr = float(math.sqrt(sum(downside) / len(self.algorithm_returns)))

        if dr < 0.000001:
            return 0.0

        return ((self.algorithm_period_returns - mar) / dr)

    def calculate_information(self):
        """
        http://en.wikipedia.org/wiki/Information_ratio
        """

        relative_returns = [
            r - b
            for r, b
            in itertools.izip(self.algorithm_returns, self.benchmark_returns)]

        relative_deviation = np.std(relative_returns, ddof=1)

        if relative_deviation < 0.000001 or np.isnan(relative_deviation):
            return 0.0

        return np.mean(relative_returns) / relative_deviation

    def calculate_beta(self):
        """

        .. math::

            \\beta_a = \\frac{\mathrm{Cov}(r_a,r_p)}{\mathrm{Var}(r_p)}

        http://en.wikipedia.org/wiki/Beta_(finance)
        """

        #it doesn't make much sense to calculate beta for less than two days,
        #so return none.
        if len(self.algorithm_returns) < 2:
            return 0.0, 0.0, 0.0, 0.0, []

        returns_matrix = np.vstack([self.algorithm_returns,
                                    self.benchmark_returns])
        C = np.cov(returns_matrix)
        eigen_values = la.eigvals(C)
        condition_number = max(eigen_values) / min(eigen_values)
        algorithm_covariance = C[0][1]
        benchmark_variance = C[1][1]
        beta = C[0][1] / C[1][1]

        return (
            beta,
            algorithm_covariance,
            benchmark_variance,
            condition_number,
            eigen_values
        )

    def calculate_alpha(self):
        """
        http://en.wikipedia.org/wiki/Alpha_(investment)
        """
        return self.algorithm_period_returns - \
            (self.treasury_period_return + self.beta *
             (self.benchmark_period_returns - self.treasury_period_return))

    def calculate_max_drawdown(self):
        compounded_returns = []
        cur_return = 0.0
        for r in self.algorithm_returns:
            try:
                cur_return += math.log(1.0 + r)
            #this is a guard for a single day returning -100%
            except ValueError:
                log.debug("{cur} return, zeroing the returns".format(
                    cur=cur_return))
                cur_return = 0.0
                # BUG? Shouldn't this be set to log(1.0 + 0) ?
            compounded_returns.append(cur_return)

        cur_max = None
        max_drawdown = None
        for cur in compounded_returns:
            if cur_max is None or cur > cur_max:
                cur_max = cur

            drawdown = (cur - cur_max)
            if max_drawdown is None or drawdown < max_drawdown:
                max_drawdown = drawdown

        if max_drawdown is None:
            return 0.0

        return 1.0 - math.exp(max_drawdown)

    def choose_treasury(self):
        td = self.end_date - self.start_date
        if td.days <= 31:
            self.treasury_duration = '1month'
        elif td.days <= 93:
            self.treasury_duration = '3month'
        elif td.days <= 186:
            self.treasury_duration = '6month'
        elif td.days <= 366:
            self.treasury_duration = '1year'
        elif td.days <= 365 * 2 + 1:
            self.treasury_duration = '2year'
        elif td.days <= 365 * 3 + 1:
            self.treasury_duration = '3year'
        elif td.days <= 365 * 5 + 2:
            self.treasury_duration = '5year'
        elif td.days <= 365 * 7 + 2:
            self.treasury_duration = '7year'
        elif td.days <= 365 * 10 + 2:
            self.treasury_duration = '10year'
        else:
            self.treasury_duration = '30year'

        end_day = self.end_date.replace(hour=0, minute=0, second=0)
        search_day = None

        if end_day in self.treasury_curves:
            rate = self.get_treasury_rate(end_day)
            if rate is not None:
                search_day = end_day

        if not search_day:
            # in case end date is not a trading day or there is no treasury
            # data, search for the previous day with an interest rate.
            search_days = self.treasury_curves.keys()

            # Find rightmost value less than or equal to end_day
            i = bisect.bisect_right(search_days, end_day)
            for prev_day in search_days[i - 1::-1]:
                rate = self.get_treasury_rate(prev_day)
                if rate is not None:
                    search_day = prev_day
                    search_dist = self.search_day_distance(prev_day)
                    break

            if search_day:
                if (search_dist is None or search_dist > 1) and \
                        search_days[0] <= end_day <= search_days[-1]:
                    message = "No rate within 1 trading day of end date = \
{dt} and term = {term}. Using {search_day}. Check that date doesn't exceed \
treasury history range."
                    message = message.format(dt=self.end_date,
                                             term=self.treasury_duration,
                                             search_day=search_day)
                    log.warn(message)

        if search_day:
            self.treasury_curve = self.treasury_curves[search_day]
            return rate * (td.days + 1) / 365

        message = "No rate for end date = {dt} and term = {term}. Check \
that date doesn't exceed treasury history range."
        message = message.format(
            dt=self.end_date,
            term=self.treasury_duration
        )
        raise Exception(message)

    def search_day_distance(self, dt):
        tdd = trading.environment.trading_day_distance(dt, self.end_date)
        if tdd is None:
            return None
        assert tdd >= 0
        return tdd

    def get_treasury_rate(self, day):
        rate = None

        curve = self.treasury_curves[day]
        # 1month note data begins in 8/2001,
        # so we can use 3month instead.
        idx = TREASURY_DURATIONS.index(self.treasury_duration)
        for duration in TREASURY_DURATIONS[idx:]:
            rate = curve[duration]
            if rate is not None:
                break

        return rate


class RiskMetricsIterative(RiskMetricsBase):
    """Iterative version of RiskMetrics.
    Should behave exaclty like RiskMetricsBatch.

    :Usage:
        Instantiate RiskMetricsIterative once.
        Call update() method on each dt to update the metrics.
    """

    def __init__(self, start_date):
        self.treasury_curves = trading.environment.treasury_curves
        self.start_date = start_date
        self.end_date = start_date

        self.compounded_log_returns = []
        self.moving_avg = []

        self.algorithm_returns = []
        self.benchmark_returns = []
        self.algorithm_volatility = []
        self.benchmark_volatility = []
        self.algorithm_period_returns = []
        self.benchmark_period_returns = []
        self.sharpe = []
        self.sortino = []
        self.information = []
        self.beta = []
        self.alpha = []
        self.max_drawdown = 0
        self.current_max = -np.inf
        self.excess_returns = []
        self.last_dt = start_date
        self.trading_days = 0

        self.all_benchmark_returns = [
            x for x in trading.environment.benchmark_returns
            if x.date >= self.start_date
        ]

    def update(self, market_close, returns_in_period):
        if trading.environment.is_trading_day(self.end_date):
            self.algorithm_returns.append(returns_in_period)
            self.benchmark_returns.append(
                self.all_benchmark_returns.pop(0).returns)
            self.trading_days += 1
            self.update_compounded_log_returns()

        self.end_date = market_close

        self.algorithm_period_returns.append(
            self.calculate_period_returns(self.algorithm_returns))
        self.benchmark_period_returns.append(
            self.calculate_period_returns(self.benchmark_returns))

        if(len(self.benchmark_returns) != len(self.algorithm_returns)):
            message = "Mismatch between benchmark_returns ({bm_count}) and \
algorithm_returns ({algo_count}) in range {start} : {end}"
            message = message.format(
                bm_count=len(self.benchmark_returns),
                algo_count=len(self.algorithm_returns),
                start=self.start_date,
                end=self.end_date
            )
            raise Exception(message)

        self.update_current_max()
        self.benchmark_volatility.append(
            self.calculate_volatility(self.benchmark_returns))
        self.algorithm_volatility.append(
            self.calculate_volatility(self.algorithm_returns))
        self.treasury_period_return = self.choose_treasury()
        self.excess_returns.append(
            self.algorithm_period_returns[-1] - self.treasury_period_return)
        self.beta.append(self.calculate_beta()[0])
        self.alpha.append(self.calculate_alpha())
        self.sharpe.append(self.calculate_sharpe())
        self.sortino.append(self.calculate_sortino())
        self.information.append(self.calculate_information())
        self.max_drawdown = self.calculate_max_drawdown()

    def to_dict(self):
        """
        Creates a dictionary representing the state of the risk report.
        Returns a dict object of the form:
        """
        period_label = self.end_date.strftime("%Y-%m")
        rval = {
            'trading_days': self.trading_days,
            'benchmark_volatility': self.benchmark_volatility[-1],
            'algo_volatility': self.algorithm_volatility[-1],
            'treasury_period_return': self.treasury_period_return,
            'algorithm_period_return': self.algorithm_period_returns[-1],
            'benchmark_period_return': self.benchmark_period_returns[-1],
            'sharpe': self.sharpe[-1],
            'sortino': self.sortino[-1],
            'information': self.information[-1],
            'beta': self.beta[-1],
            'alpha': self.alpha[-1],
            'excess_return': self.excess_returns[-1],
            'max_drawdown': self.max_drawdown,
            'period_label': period_label
        }

        # check if a field in rval is nan, and replace it with
        # None.
        def check_entry(key, value):
            if key != 'period_label':
                return np.isnan(value)
            else:
                return False

        return {k: None
                if check_entry(k, v)
                else v for k, v in rval.iteritems()}

    def __repr__(self):
        statements = []
        metrics = [
            "algorithm_period_returns",
            "benchmark_period_returns",
            "excess_returns",
            "trading_days",
            "benchmark_volatility",
            "algorithm_volatility",
            "sharpe",
            "sortino",
            "information",
            "algorithm_covariance",
            "benchmark_variance",
            "beta",
            "alpha",
            "max_drawdown",
            "algorithm_returns",
            "benchmark_returns",
            "condition_number",
            "eigen_values"
        ]

        for metric in metrics:
            value = getattr(self, metric)
            if isinstance(value, list):
                if len(value) == 0:
                    value = np.nan
                else:
                    value = value[-1]
            statements.append("{m}:{v}".format(m=metric, v=value))

        return '\n'.join(statements)

    def update_compounded_log_returns(self):
        if len(self.algorithm_returns) == 0:
            return

        try:
            compound = math.log(1 + self.algorithm_returns[-1])
        except ValueError:
            compound = 0.0
            # BUG? Shouldn't this be set to log(1.0 + 0) ?

        if len(self.compounded_log_returns) == 0:
            self.compounded_log_returns.append(compound)
        else:
            self.compounded_log_returns.append(
                self.compounded_log_returns[-1] +
                compound
            )

    def calculate_period_returns(self, returns):
        period_returns = 1.0

        for r in returns:
            period_returns *= (1.0 + r)

        period_returns -= 1.0
        return period_returns

    def update_current_max(self):
        if len(self.compounded_log_returns) == 0:
            return
        if self.current_max < self.compounded_log_returns[-1]:
            self.current_max = self.compounded_log_returns[-1]

    def calculate_max_drawdown(self):
        if len(self.compounded_log_returns) == 0:
            return self.max_drawdown

        cur_drawdown = 1.0 - math.exp(
            self.compounded_log_returns[-1] -
            self.current_max)

        if self.max_drawdown < cur_drawdown:
            return cur_drawdown
        else:
            return self.max_drawdown

    def calculate_sharpe(self):
        """
        http://en.wikipedia.org/wiki/Sharpe_ratio
        """
        if self.algorithm_volatility[-1] == 0:
            return 0.0

        return (self.algorithm_period_returns[-1] -
                self.treasury_period_return) / self.algorithm_volatility[-1]

    def calculate_sortino(self, mar=None):
        """
        http://en.wikipedia.org/wiki/Sortino_ratio
        """
        if len(self.algorithm_returns) == 0:
            return 0.0

        if mar is None:
            mar = self.treasury_period_return

        downside = [
            (x - mar)**2
            for x in self.algorithm_returns
            if x < mar]
        dr = float(math.sqrt(sum(downside) / len(self.algorithm_returns)))

        if dr < 0.000001:
            return 0.0

        return ((self.algorithm_period_returns[-1] - mar) /
                dr)

    def calculate_information(self):
        """
        http://en.wikipedia.org/wiki/Information_ratio
        """

        relative_returns = [
            r - b
            for r, b
            in itertools.izip(self.algorithm_returns, self.benchmark_returns)]

        relative_deviation = np.std(relative_returns, ddof=1)

        if relative_deviation < 0.000001 or np.isnan(relative_deviation):
            return 0.0

        return np.mean(relative_returns) / relative_deviation

    def calculate_alpha(self):
        """
        http://en.wikipedia.org/wiki/Alpha_(investment)
        """
        return (self.algorithm_period_returns[-1] -
                (self.treasury_period_return + self.beta[-1] *
                 (self.benchmark_period_returns[-1] -
                  self.treasury_period_return)))


class RiskMetricsBatch(RiskMetricsBase):
    pass


class RiskReport(object):
    def __init__(
        self,
        algorithm_returns,
        sim_params
    ):
        """
        algorithm_returns needs to be a list of daily_return objects
        sorted in date ascending order
        """

        self.algorithm_returns = algorithm_returns
        self.sim_params = sim_params
        self.created = epoch_now()

        if len(self.algorithm_returns) == 0:
            start_date = self.sim_params.period_start
            end_date = self.sim_params.period_end
        else:
            start_date = self.algorithm_returns[0].date
            end_date = self.algorithm_returns[-1].date

        self.month_periods = self.periods_in_range(1, start_date, end_date)
        self.three_month_periods = self.periods_in_range(
            3, start_date, end_date)
        self.six_month_periods = self.periods_in_range(6, start_date, end_date)
        self.year_periods = self.periods_in_range(12, start_date, end_date)

    def to_dict(self):
        """
        RiskMetrics are calculated for rolling windows in four lengths::
            - 1_month
            - 3_month
            - 6_month
            - 12_month

        The return value of this funciton is a dictionary keyed by the above
        list of durations. The value of each entry is a list of RiskMetric
        dicts of the same duration as denoted by the top_level key.

        See :py:meth:`RiskMetrics.to_dict` for the detailed list of fields
        provided for each period.
        """
        return {
            'one_month': [x.to_dict() for x in self.month_periods],
            'three_month': [x.to_dict() for x in self.three_month_periods],
            'six_month': [x.to_dict() for x in self.six_month_periods],
            'twelve_month': [x.to_dict() for x in self.year_periods],
            'created': self.created
        }

    def periods_in_range(self, months_per, start, end):
        one_day = datetime.timedelta(days=1)
        ends = []
        cur_start = start.replace(day=1)

        # in edge cases (all sids filtered out, start/end are adjacent)
        # a test will not generate any returns data
        if len(self.algorithm_returns) == 0:
            return ends

        #ensure that we have an end at the end of a calendar month, in case
        #the return series ends mid-month...
        the_end = advance_by_months(end.replace(day=1), 1) - one_day
        while True:
            cur_end = advance_by_months(cur_start, months_per) - one_day
            if(cur_end > the_end):
                break
            cur_period_metrics = RiskMetricsBatch(
                start_date=cur_start,
                end_date=cur_end,
                returns=self.algorithm_returns
            )

            ends.append(cur_period_metrics)
            cur_start = advance_by_months(cur_start, 1)

        return ends

    def find_metric_by_end(self, end_date, duration, metric):
        col = getattr(self, duration + "_periods")
        col = [getattr(x, metric) for x in col if x.end_date == end_date]
        if len(col) == 1:
            return col[0]
        return None