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
-2.436
Tracking Error
0.13
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
class PensiveBlackViper(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 1, 1)
        self.SetEndDate(2021, 2, 10)
        self.SetCash(100000)
        
        # grabs the minute data for securities
        self.UniverseSettings.Reloution = Resolution.Minute
        
        # grabs extended market data
        self.UniverseSettings.ExtendedMarketHours = True
        
        # Adds the universe of stocks
        self.AddUniverse(self.CoarseSelectionFilter)
        
        
    
    def CoarseSelectionFilter(self, coarse):
        
        # sort by price highest to lowest
        # c.DollarVolume <-- looks at coarse object dollar volume and sorts it highest to lowest
        sortedByDollarVolume = sorted(coarse, key=lambda c: c.DollarVolume, reverse=True)
        
        # saves ~8000 stocks currently trading
        self.coarse = coarse
        return Universe.Unchanged
        
        # selects symbol if price is more than $15
        symbols_by_price = [c.Symbol for c in sortedByDollarVolume if c.Price > 15]
        
        # returns the 8 most liquid symbols from the filteredByPrice list
        self.filteredByPrice = symbols_by_price[:10]
        
        fml = self.filteredByPrice
        
        self.Debug(f"CoarseSelectionFilter({self.Time}):: {fml}")
        
        return self.filteredByPrice
        
    def OnSecuritiesChanged(self, changes):
        
        # saves securities changed as self.changes
        self.changes = changes
        
        # log the changes that were made
        self.Log(f"OnSecuritiesChanged({self.Time}):: {changes}")