#This is a Template of dynamic stock selection.
#You can try your own fundamental factor and ranking method by editing the CoarseSelectionFunction and FineSelectionFunction



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

class BasicTemplateAlgorithm(QCAlgorithm):
    
    def __init__(self):
    # set the flag for rebalance
        self.reb = 1
    # Number of stocks to pass CoarseSelection process
        self.num_coarse = 50
    # Number of stocks to long/short
        self.num_fine = 20
        self.symbols = None
        self.first_month = 0


    def Initialize(self):
        self.SetCash(100000)
        self.SetStartDate(2015,1,1)
    # if not specified, the Backtesting EndDate would be today 
        self.SetEndDate(2015,8,1)
        
        
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        
        self.UniverseSettings.Resolution = Resolution.Daily
        
        self.AddUniverse(self.CoarseSelectionFunction,self.FineSelectionFunction)
        
    # Schedule the rebalance function to execute at the begining of each month
        self.Schedule.On(self.DateRules.MonthStart(self.spy), 
        self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.Rebalance))
        
    
    def CoarseSelectionFunction(self, coarse):
        
        # Attempt to only try rebalance quarterly on the 1st day of the month
        #today = self.Time
        #self.Log("Day = {} Month = {}".format(today.day,today.month))
        if self.reb != 1:
            return
            
        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
        sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
        #result = [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
        result = [ x.Symbol for x in sortedByDollarVolume[:self.num_coarse] ]
        self.universe = result

        return self.universe
    

    def FineSelectionFunction(self, fine):
    # return null list if it's not time to rebalance
        if self.reb != 1:
            return
            
        self.reb = 0
            
    # drop stocks which don't have the information we need.
    # you can try replacing those factor with your own factors here
    
        filtered_fine = [x for x in fine if x.ValuationRatios.PriceChange1M
                                        and x.ValuationRatios.PERatio]
                                        
        self.Log('remained to select %d'%(len(filtered_fine)))
        
        # rank stocks by two factors.
        sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=False)
        sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PERatio, reverse=False)
        stock_dict = {}
        
        # assign a score to each stock, you can also change the rule of scoring here.
        for i,ele in enumerate(sortedByfactor1):
            rank1 = i
            rank2 = sortedByfactor2.index(ele)
            score = sum([rank1*0.5,rank2*0.5])
            stock_dict[ele] = score
        
        # sort the stocks by their scores
        self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
        sorted_symbol = [x[0] for x in self.sorted_stock]

        # sort the top stocks into the long_list and the bottom ones into the short_list
        self.longs = [x.Symbol for x in sorted_symbol[:self.num_fine]]
        
        #topFine = self.long

        #self.universe = [ x.Symbol for x in topFine ]
        #self.universe = self.long
        #return self.universe
        
        #return [i.Symbol for i in (self.long)]
        return self.longs
        #else:
        #    return []

    def OnData(self, data):
        pass
    
    def Rebalance(self):
        if self.first_month == 0:
            self.first_month += 1
            return
    # if this month the stock are not going to be long/short, liquidate it.
        for i in self.Portfolio.Values:
            if (i.Invested) and (i.Symbol not in self.longs):
                self.Liquidate(i.Symbol)
                
    # Alternatively, you can liquidate all the stocks at the end of each month.
    # Which method to choose depends on your investment philosiphy 
    # if you prefer to realized the gain/loss each month, you can choose this method.
    
        #self.Liquidate()
        
    # Assign each stock equally. Alternatively you can design your own portfolio construction method
        for i in self.longs:
            self.SetHoldings(i,0.99/self.num_fine)

        self.reb = 1