from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from System.Collections.Generic import List
import decimal as d
import numpy as np
import time
from datetime import timedelta
### <summary>
##Remvoing an if statement increased the returns by 4 %
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
    '''Basic template algorithm simply initializes the date range and cash'''

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2010,10,07)  #Set Start Date
        self.SetEndDate(2012,10,01)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        ## N=Normal (Manufacturing), M=Mining, U=Utility, T=Transportation, B=Bank, I=Insurance
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.bond = self.AddEquity('TLT',Resolution.Daily).Symbol
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.spy_sma = self.SMA('SPY',200,Resolution.Daily)
        self.first = 0 
        self.month = 0 
        self.Top_sector = ''
        self.The_sectors = {"XME|M":0,"KIE|I":0,"XLU|U":0,"XLF|B":0,"IYT|T":0,}
        for symbol in self.The_sectors:
            self.AddEquity(symbol.split('|')[0],Resolution.Daily)
            self.The_sectors[symbol]= self.MOM(symbol.split('|')[0],30,Resolution.Daily)
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(3,45),Action(self.rebalance))
        self.SetWarmUp(200)
            
    def CoarseSelectionFunction(self, coarse):
        filtered_stocks = filter(lambda x: x.HasFundamentalData,coarse)
        filtered_stocks = list(filter(lambda x: x.DollarVolume >250000,filtered_stocks))
        filtered_stocks.sort(key = lambda x: x.DollarVolume)
        return [stock.Symbol for stock in filtered_stocks[:50]]
    def FineSelectionFunction(self, fine):
        ready = [indicator.IsReady for indicator in self.The_sectors.values()]
        if not ready:
            return
        if not self.first:
            self.Top_sector = max(self.The_sectors,key = self.The_sectors.get).split('|')[-1]
            self.first +=1
        filtered_stocks = list(filter(lambda x: self.Top_sector in str(x.CompanyReference.IndustryTemplateCode),fine))
        return [stock.Symbol for stock in filtered_stocks[:5]]
    def OnSecuritiesChanged(self, changes):
        if self.spy_sma.IsReady:
            if self.Securities[self.spy].Price >self.spy_sma.Current.Value:
                for stock in self.Portfolio.Values:
                    bond =[stock.Symbol for stock in self.Portfolio.Values if stock.Invested and str(stock.Symbol).split(' ')[0] == 'TLT']
                    if bond:
                        self.Liquidate('TLT')
                        self.Log("Bonds were here")
                    else:
                        for stock in changes.AddedSecurities:
                            oo = len(self.Transactions.GetOpenOrders(stock.Symbol))
                            cash = float(self.Portfolio.Cash)
                            if not oo and cash >0 and str(stock.Symbol).split(' ')[0] != 'SPY':
                                self.SetHoldings(stock.Symbol,0.10)
            
    def rebalance(self):
        self.month+=1
        if self.month % 25 == 0:
            curr_best_sector = max(self.The_sectors,key = self.The_sectors.get).split('|')[-1]
            if self.Top_sector!= curr_best_sector:
                self.Top_sector= curr_best_sector
                bond =[stock.Symbol for stock in self.Portfolio.Values if stock.Invested and str(stock.Symbol).split(' ')[0] == 'TLT']
                if not bond:
                    self.Liquidate()
                    time.sleep(20)
            if self.spy_sma.IsReady and not self.Portfolio.HoldStock:
                if self.Securities[self.spy].Price < self.spy_sma.Current.Value:
                    self.SetHoldings(self.bond,1)
                    self.Log('Should buy TLT')