"""
Factory functions to prepare useful data for tests.
"""
import os
import pytz
import msgpack
import random
from os.path import join, abspath, dirname
from operator import attrgetter

from datetime import datetime, timedelta
import zipline.finance.risk as risk
import zipline.protocol as zp

from zipline.gens.tradegens import RandomEquityTrades
from zipline.gens.tradegens import SpecificEquityTrades
from zipline.gens.utils import create_trade
from zipline.finance.trading import TradingEnvironment

# TODO
def data_path():
    from zipline import data
    data_path = dirname(abspath(data.__file__))
    return data_path

def logger_path():
    import zipline
    log_path = dirname(abspath(zipline.__file__))
    return os.join(log_path, 'logging.cfg')

def load_market_data():
    fp_bm = open(join(data_path(), "benchmark.msgpack"), "rb")
    bm_list = msgpack.loads(fp_bm.read())
    bm_returns = []
    for packed_date, returns in bm_list:
        event_dt = zp.tuple_to_date(packed_date)
        #event_dt = event_dt.replace(
        #    hour=0,
        #    minute=0,
        #    second=0,
        #    tzinfo=pytz.utc
        #)

        daily_return = risk.DailyReturn(date=event_dt, returns=returns)
        bm_returns.append(daily_return)

    bm_returns = sorted(bm_returns, key=attrgetter('date'))
    fp_tr = open(join(data_path(), "treasury_curves.msgpack"), "rb")
    tr_list = msgpack.loads(fp_tr.read())
    tr_curves = {}
    for packed_date, curve in tr_list:
        tr_dt = zp.tuple_to_date(packed_date)
        #tr_dt = tr_dt.replace(hour=0, minute=0, second=0, tzinfo=pytz.utc)
        tr_curves[tr_dt] = curve

    return bm_returns, tr_curves

def create_trading_environment(year=2006):
    """Construct a complete environment with reasonable defaults"""
    benchmark_returns, treasury_curves = load_market_data()

    start = datetime(year, 1, 1, tzinfo=pytz.utc)
    end   = datetime(year, 12, 31, tzinfo=pytz.utc)
    trading_environment = TradingEnvironment(
        benchmark_returns,
        treasury_curves,
        period_start = start,
        period_end = end,
        capital_base = 100000.0
    )

    return trading_environment


def get_next_trading_dt(current, interval, trading_calendar):
    next = current
    while True:
        next = next + interval
        if trading_calendar.is_market_hours(next):
            break

    return next

def create_trade_history(sid, prices, amounts, interval, trading_calendar):
    trades = []
    current = trading_calendar.first_open

    for price, amount in zip(prices, amounts):

        trade = create_trade(sid, price, amount, current)
        trades.append(trade)
        current = get_next_trading_dt(current, interval, trading_calendar)

    assert len(trades) == len(prices)
    return trades

def create_txn(sid, price, amount, datetime, btrid=None):
    txn = zp.ndict({
        'sid'    : sid,
        'amount' : amount,
        'dt'     : datetime,
        'price'  : price,
    })
    return txn

def create_txn_history(sid, priceList, amtList, interval, trading_calendar):
    txns = []
    current = trading_calendar.first_open

    for price, amount in zip(priceList, amtList):
        current = get_next_trading_dt(current, interval, trading_calendar)

        txns.append(create_txn(sid, price, amount, current))
        current = current + interval
    return txns


def create_returns(daycount, trading_calendar):
    """
    For the given number of calendar (not trading) days return all the trading
    days between start and start + daycount.
    """
    test_range = []
    current = trading_calendar.first_open
    one_day = timedelta(days = 1)

    for day in range(daycount):
        current = current + one_day
        if trading_calendar.is_trading_day(current):
            r = risk.DailyReturn(current, random.random())
            test_range.append(r)

    return test_range


def create_returns_from_range(trading_calendar):
    current = trading_calendar.first_open
    end = trading_calendar.last_close
    one_day = timedelta(days = 1)
    test_range = []
    while current <= end:
        r = risk.DailyReturn(current, random.random())
        test_range.append(r)
        current = get_next_trading_dt(current, one_day, trading_calendar)

    return test_range

def create_returns_from_list(returns, trading_calendar):
    current = trading_calendar.first_open
    one_day = timedelta(days = 1)
    test_range = []

    #sometimes the range starts with a non-trading day.
    if not trading_calendar.is_trading_day(current):
        current = get_next_trading_dt(current, one_day, trading_calendar)

    for return_val in returns:
        r = risk.DailyReturn(current, return_val)
        test_range.append(r)
        current = get_next_trading_dt(current, one_day, trading_calendar)

    return test_range

def create_random_trade_source(sid, trade_count, trading_environment):
    # create the source
    source = RandomEquityTrades(sid, trade_count)

    # make the period_end of trading_environment match
    cur = trading_environment.first_open
    one_day = timedelta(days = 1)
    for i in range(trade_count + 2):
       cur = get_next_trading_dt(cur, one_day, trading_environment)
    trading_environment.period_end = cur

    return source

def create_daily_trade_source(sids, trade_count, trading_environment):

    """
    creates trade_count trades for each sid in sids list.
    first trade will be on trading_environment.period_start, and daily
    thereafter for each sid. Thus, two sids should result in two trades per
    day.

    Important side-effect: trading_environment.period_end will be modified
    to match the day of the final trade.
    """
    return create_trade_source(
        sids,
        trade_count,
        timedelta(days=1),
        trading_environment
    )


def create_minutely_trade_source(sids, trade_count, trading_environment):

    """
    creates trade_count trades for each sid in sids list.
    first trade will be on trading_environment.period_start, and every minute
    thereafter for each sid. Thus, two sids should result in two trades per
    minute.

    Important side-effect: trading_environment.period_end will be modified
    to match the day of the final trade.
    """
    return create_trade_source(
        sids,
        trade_count,
        timedelta(minutes=1),
        trading_environment
    )

def create_trade_source(sids, trade_count, trade_time_increment, trading_environment):

    #Set up source a. One minute between events.
    args = tuple()
    kwargs = {
        'count'  : trade_count,
        'sids'   : sids,
        'start'  : trading_environment.first_open,
        'delta'  : trade_time_increment,
        'filter' : sids
    }
    source = SpecificEquityTrades(*args, **kwargs)

    # TODO: do we need to set the trading environment's end to same dt as
    # the last trade in the history?
    #trading_environment.period_end = trade_history[-1].dt

    return source