from AlphaModel import *

class VerticalTachyonRegulators(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(2021, 1, 1)
        self.SetCash(100000)

        # Universe selection
        self.month = 0
        self.num_coarse = 500

        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        # Alpha Model
        self.AddAlpha(FundamentalFactorAlphaModel())

        # Portfolio construction model
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(self.IsRebalanceDue))
        
        # Risk model
        self.SetRiskManagement(NullRiskManagementModel())

        # Execution model
        self.SetExecution(ImmediateExecutionModel())

    # Share the same rebalance function for Universe and PCM for clarity
    def IsRebalanceDue(self, time):
        # Rebalance on the first day of the Quarter
        if time.month == self.month or time.month not in [1, 4, 7, 10]:
            return None
            
        self.month = time.month
        return time

    def CoarseSelectionFunction(self, coarse):
        # If not time to rebalance, keep the same universe
        if not self.IsRebalanceDue(self.Time): 
            return Universe.Unchanged

        # Select only those with fundamental data and a sufficiently large price
        # Sort by top dollar volume: most liquid to least liquid
        selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5],
                            key = lambda x: x.DollarVolume, reverse=True)

        return [x.Symbol for x in selected[:self.num_coarse]]


    def FineSelectionFunction(self, fine):
        # Filter the fine data for equities that IPO'd more than 5 years ago in selected sectors
        
        sectors = [
            MorningstarSectorCode.FinancialServices,
            MorningstarSectorCode.RealEstate,
            MorningstarSectorCode.Healthcare,
            MorningstarSectorCode.Utilities,
            MorningstarSectorCode.Technology]
        
        filtered_fine = [x.Symbol for x in fine if x.SecurityReference.IPODate + timedelta(365*5) < self.Time
                                    and x.AssetClassification.MorningstarSectorCode in sectors
                                    and x.OperationRatios.ROE.Value > 0
                                    and x.OperationRatios.NetMargin.Value > 0
                                    and x.ValuationRatios.PERatio > 0]
                
        return filtered_fine

class FundamentalFactorAlphaModel(AlphaModel):
    
    def __init__(self):
        self.rebalanceTime = datetime.min
        # Dictionary containing set of securities in each sector
        # e.g. {technology: set(AAPL, TSLA, ...), healthcare: set(XYZ, ABC, ...), ... }
        self.sectors = {}

    def Update(self, algorithm, data):
        '''Updates this alpha model with the latest data from the algorithm.
        This is called each time the algorithm receives data for subscribed securities
        Args:
            algorithm: The algorithm instance
            data: The new data available
        Returns:
            New insights'''

        if algorithm.Time <= self.rebalanceTime:
            return []
        
        # Set the rebalance time to match the insight expiry
        self.rebalanceTime = Expiry.EndOfQuarter(algorithm.Time)
        
        insights = []
        
        for sector in self.sectors:
            securities = self.sectors[sector]
            sortedByROE = sorted(securities, key=lambda x: x.Fundamentals.OperationRatios.ROE.Value, reverse=True)
            sortedByPM = sorted(securities, key=lambda x: x.Fundamentals.OperationRatios.NetMargin.Value, reverse=True)
            sortedByPE = sorted(securities, key=lambda x: x.Fundamentals.ValuationRatios.PERatio, reverse=False)

            # Dictionary holding a dictionary of scores for each security in the sector
            scores = {}
            for security in securities:
                score = sum([sortedByROE.index(security), sortedByPM.index(security), sortedByPE.index(security)])
                scores[security] = score
                
            # Add best 20% of each sector to longs set (minimum 1)
            length = max(int(len(scores)/5), 1)
            for security in sorted(scores.items(), key=lambda x: x[1], reverse=False)[:length]:
                symbol = security[0].Symbol
                # Use Expiry.EndOfQuarter in this case to match Universe, Alpha and PCM
                insights.append(Insight.Price(symbol, Expiry.EndOfQuarter, InsightDirection.Up))
        
        return insights

    def OnSecuritiesChanged(self, algorithm, changes):
        '''Event fired each time the we add/remove securities from the data feed
        Args:
            algorithm: The algorithm instance that experienced the change in securities
            changes: The security additions and removals from the algorithm'''
        
        # Remove security from sector set
        for security in changes.RemovedSecurities:
            for sector in self.sectors:
                if security in self.sectors[sector]:
                    self.sectors[sector].remove(security)
        
        # Add security to corresponding sector set
        for security in changes.AddedSecurities:
            sector = security.Fundamentals.AssetClassification.MorningstarSectorCode
            if sector not in self.sectors:
                self.sectors[sector] = set()
            self.sectors[sector].add(security)