Overall Statistics
Total Trades
9425
Average Win
0.07%
Average Loss
-0.07%
Compounding Annual Return
8.490%
Drawdown
19.800%
Expectancy
-0.013
Net Profit
13.316%
Sharpe Ratio
0.407
Loss Rate
52%
Win Rate
48%
Profit-Loss Ratio
1.05
Alpha
0.448
Beta
-21.17
Annual Standard Deviation
0.235
Annual Variance
0.055
Information Ratio
0.336
Tracking Error
0.235
Treynor Ratio
-0.005
Total Fees
$10200.09
### <summary>
### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
### </summary>
class CoarseFundamentalTop5Algorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2014,01,01)  #Set Start Date
        self.SetEndDate(2016,01,01)    #Set End Date
        self.SetCash(100000)            #Set Strategy Cash

        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Daily

        # this add universe method accepts a single parameter that is a function that
        # accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction)

        self.__numberOfSymbols = 200
        self._changes = SecurityChanges.None
        self.fineSymbols = 50
        self.fineSymbols2 = 25


    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        # 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] ]
        
    def FineSelectionFunction(self, fine):
        sortedByEVEBITDA = sorted(fine, key=lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)
        sortedByPricetoBook = sorted(sortedByEVEBITDA[:self.fineSymbols], key = lambda x: x.ValuationRatios.PriceToBook, reverse=True)
        return [x.Symbol for x in sortedByPricetoBook[:self.fineSymbols2]]

    def OnData(self, data):
        # if we have no changes, do nothing
        if self._changes == SecurityChanges.None: return

        # liquidate removed securities
        for security in self._changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)

        # we want 20% allocation in each security in our universe
        for security in self._changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.039)

        self._changes = SecurityChanges.None;


    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        self._changes = changes