from System import *
from System.Collections.Generic import List
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import *

class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):

    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(2017,07,01)  #Set Start Date
        #self.SetEndDate(2015,01,01)    #Set End Date
        self.SetCash(10000)            #Set Strategy Cash

        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Daily
        
        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        # - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        self.__numberOfSymbols = 5
        self.__numberOfSymbolsFine = 2
        self._changes = SecurityChanges.None
        
    ################### UNIVERSE #####################
    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        # sort descending by daily dollar volume
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)

        # return the symbol objects of the top entries from our sorted collection
        return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]

    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)

        # take the top entries from our sorted collection
        return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]


    def OnData(self, data):
        # if we have no changes, do nothing
        if self._changes == SecurityChanges.None: return

        # liquidate removed securities
        for security in self._changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)
                self.Log('Exit: '.format(security))

        # we want 20% allocation in each security in our universe
        for security in self._changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.2)
            self.Log('Long: '.format(security))

        self._changes = SecurityChanges.None


    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        self._changes = changes