Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
class MultidimensionalHorizontalCompensator(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2018, 12, 3)  # Set Start Date
        self.SetEndDate(2019, 1, 2)
        self.SetCash(100000)  # Set Strategy Cash
        # self.AddEquity("SPY", Resolution.Minute)
        self.UniverseSettings.Resolution = Resolution.Daily
        self.__numberOfSymbols = 100
        self.__numberOfSymbolsFine = 5
        self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None))
        
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday, DayOfWeek.Tuesday, DayOfWeek.Wednesday, DayOfWeek.Thursday, DayOfWeek.Friday), \
                 self.TimeRules.At(12, 0), \
                 self.Reselect)
        self.Select = False
        self.symbols = []


    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)

    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        if not self.Select: return self.symbols
        
        # 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] ]
    
    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        if not self.Select: return self.symbols
        
        # 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
        self.symbols = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]
        self.Select = False
        self.Log(f"selection complete. Time:{self.Time}. Symbol selected:{[x.Value for x in self.symbols]}")
        return self.symbols
        
    def Reselect(self):
        self.Select = True