import numpy as np
import pandas as pd
from QuantConnect.Securities import Future

class GlobalMarketsMomentum(QCAlgorithm):

    def Initialize(self):
        # Set the cash we'd like to use for our backtest
        # This is ignored in live trading 
        self.SetCash(100000)
        
        # Start and end dates for the backtest.
        # These are ignored in live trading.
        self.SetStartDate(2018, 11, 1)
        self.SetEndDate(2019, 1, 2)
        
        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)
        
        self.etfs = ['SPY', 'VEU', 'TLT', 'IJR', 'SPYG']
        
        for etf in self.etfs:
            self.AddEquity(etf, Resolution.Daily)
            #self.Securities[etf].FeeModel = ConstantFeeTransactionModel(0)
            #self.Securities[etf].SlippageModel = ConstantSlippageModel(0)
        
        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(0, 0), Action(self.ScheduleEndOfMonthRebalance))
        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.BeforeMarketClose('SPY', 1), Action(self.Rebalance))
        
        # parameters
        self.momentum_period = 164


    def OnData(self, slice):
        pass
    
    
    def ScheduleEndOfMonthRebalance(self):
        
        month_last_day = DateTime(self.Time.year, self.Time.month, DateTime.DaysInMonth(self.Time.year, self.Time.month))
        trading_days = self.TradingCalendar.GetDaysByType(TradingDayType.BusinessDay, self.Time, month_last_day)
        
        # get the last trading day of the month
        for x in trading_days:
            self.month_last_trading_day = x.Date.date()
            

    def Rebalance(self):
        if self.Time.date() != self.month_last_trading_day:
            return
            
        # uncomment to rebalance every Fridayy
        # if self.Time.date().weekday() != 4:  
        #     return

        # get prices from momentum_period calendar days (not trading days) ago, note that the most recent price this will return is from the previous day
        # specify symbols as a list so that a DataFrame is returned
        historical_prices = self.History(self.etfs, TimeSpan.FromDays(self.momentum_period))
        historical_prices = historical_prices.sort_index(ascending=True).loc[(slice(None), historical_prices.index.get_level_values(1)[0]), 'close'].unstack(0)  # this gets close data for all symbols for the first date in the dataframe

        # get the most recent prices
        last_prices = self.History(self.etfs, 1, Resolution.Minute)['close'].unstack(0)