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(2020,1,1)    #Set Start Date
        self.SetEndDate(2020,1,31)    #Set End Date
        self.SetCash(50000)            #Set Strategy Cash

        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Minute

        # 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 = None


    # 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] ]
    '''
    def CoarseSelectionFunction(self, coarse):
        
    #    return [c.Symbol for c in coarse if c.HasFundamentalData and c.Price > 10 and 
    #           c.DollarVolume > 10000000] 
        coarse_WO_fundamental = [x for x in coarse if x.HasFundamentalData]
        
        sortedByVolume = sorted(coarse_WO_fundamental, key=lambda x: x.DollarVolume, reverse=True)
        
        top = sortedByVolume[:20]
        return [i.Symbol for i in top]
    
    # 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 is None: return

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

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

        self._changes = None


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