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.RebalanacePortfolioOnInsightChanges = 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))

    # on 15 Jan, filter for securities with fundamental data
    def CoarseSelectionFunction(self, coarse):
        if not (self.Time.month == 1 and self.Time.day == 15):
            return Universe.Unchanged
        filtered = [ x.Symbol for x in coarse if x.HasFundamentalData ]
        return filtered
    
    # on 15 Jan, filter for securities with price above 1000
    def FineSelectionFunction(self, fine):
        filtered = [ x.Symbol for x in fine if x.Price > 1000 ]
        return filtered


class NetNetAlpha(AlphaModel):
    
    def __init__(self):
        pass
    
    def Update(self, algorithm, data):
        insights = []
        if not (algorithm.Time.month == 1 and algorithm.Time.day == 15):
            return insights
 
        for security in algorithm.ActiveSecurities.Values:
            insights.append(Insight.Price(security.Symbol, timedelta(days=366), InsightDirection.Up))
                
        return insights