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
Probabilistic 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
-0.604
Tracking Error
0.278
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
class MeasuredRedOrangeBat(QCAlgorithm):

    '''
    This strategy will:
        -Create a universe using a linear regression of price data to determine the slope of the trend. It will then sort these stocks in the universe by the slope value.
        OR
        -Create a universe using sci-pi to determine higher highs and higher lows / lower highs and lower lows to return a list of long and short stocks. 
        -Wait for for a price breakout in the direction of the trend using a donchian channel. (Possibility to include a slow and a channel)
    '''
    
    def Initialize(self):
        self.SetStartDate(2020, 1, 1)  # Set Start Date
        self.SetEndDate(2021, 1, 1)
        self.SetCash(100000)  # Set Strategy Cash
        #self.AddUniverse(self.CoarseFilter, self.FineFilter)
        self.UniverseSettings.Resolution = Resolution.Daily
        
        self.numOfCoarseSymbols = 100
        self.numOfFineSymbols = 10
        
        self.longSymbols = []
        self.shortSymbols = []
        


    def CoarseFilter(self, coarse):
        selected = sorted([x for x in coarse if x.HasFundamentalData and (x.Price > 1 < 50)],
        key = lambda x: x.DollarVolume, reverse=True)
        
        coarseSymbols = [i.Symbol for i in selected[:self.numOfCoarseSymbols]]
        
        history = self.History(coarseSymbols, 20, Resolution.Weekly)