from Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel

class NetNet(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 1, 1)  # Set Start Date
        self.SetEndDate(2020, 1, 31)
        self.SetCash(100000)  # Set Strategy Cash
        
        self.SetAlpha(NetNetAlpha())
        
        self.SetExecution(ImmediateExecutionModel())

        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(lambda time: None))
        self.Settings.RebalancePortfolioOnInsightChanges = False
        self.Settings.RebalancePortfolioOnSecurityChanges = True

        self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None))
        self.UniverseSettings.Resolution = Resolution.Daily
        self.SetSecurityInitializer(lambda x: x.SetDataNormalizationMode(DataNormalizationMode.Raw))


    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
            Arguments:
                data: Slice object keyed by symbol containing the stock data
        '''

        # if not self.Portfolio.Invested:
        #    self.SetHoldings("SPY", 1)


    # on 15 Jan, filter for securities with fundamental data
    def CoarseSelectionFunction(self, coarse):
        if self.Time.month == 1 and self.Time.day == 15:
            filtered = [ x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 1 and x.DollarVolume > 1e5]
            return filtered
        else:
            return Universe.Unchanged
    
    
    # on 15 Jan, filter first for securities with shares and then filter a second time for net net stocks
    def FineSelectionFunction(self, fine):
        # filtered = [ x.Symbol for x in fine if (x.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths - x.FinancialStatements.BalanceSheet.TotalLiabilitiesAsReported.ThreeMonths) > 0 ]
        filtered = [ x for x in fine if x.FinancialStatements.BalanceSheet.OrdinarySharesNumber.ThreeMonths > 0  and (x.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths - x.FinancialStatements.BalanceSheet.TotalLiabilitiesAsReported.ThreeMonths) > 0]   
        
        filtered2 = [ x.Symbol for x in filtered if (x.Price / ((x.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths - x.FinancialStatements.BalanceSheet.TotalLiabilitiesAsReported.ThreeMonths) / x.FinancialStatements.BalanceSheet.OrdinarySharesNumber.ThreeMonths)) <= 0.66 ]
        return filtered2


class NetNetAlpha(AlphaModel):
    
    def __init__(self):
        pass
        # self.lastMonth = -1
    
    def OnSecuritiesChanged(self, algorithm, changes):
        pass
    
    def Update(self, algorithm, data):
        insights = []
        
        if algorithm.Time.month == 1 and algorithm.Time.day == 15:
            for security in algorithm.ActiveSecurities.Values:
                # price = security.Price
                # currentAssets = security.Fundamentals.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths
                # totalLiabilities = security.Fundamentals.FinancialStatements.BalanceSheet.TotalLiabilitiesAsReported.ThreeMonths
                # shares = security.Fundamentals.FinancialStatements.BalanceSheet.OrdinarySharesNumber.ThreeMonths
                
                # if ( price / ( (currentAssets - totalLiabilities) / shares ) <= 0.66 ):
                #     insights.append(Insight.Price(security.Symbol, timedelta(days = 366), InsightDirection.Up))
                
                insights.append(Insight.Price(security.Symbol, timedelta(days=366), InsightDirection.Up))
                
            return insights
        else:
            return insights