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
1.539
Tracking Error
0.143
Treynor Ratio
0
Total Fees
$0.00
 
import pandas as pd
class MyAlgo(QCAlgorithm):
    
    def Initialize(self):
        self.SetCash(100000)
        self.SetStartDate(2020, 11, 13)
        
        columns = ['SYMBOL', 'VOL', 'SIZE', 'RATING']
        self.data = pd.DataFrame(columns=columns)
        
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

    def CoarseSelectionFunction(self, coarse):
        selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5) and x.DollarVolume][:10]
        for x in selected:
            v = x.DollarVolume
            z = x.Symbol.Value
        
            if v > 20000000:
                self.data.append(pd.DataFrame({'VOL': [1], 'SYMBOL': z, 'SIZE':0, 'RATING':0}, index=['SYMBOL']))
            else:
                self.data.append(pd.DataFrame({'VOL': [v/20000000], 'SYMBOL': z, 'SIZE':0, 'RATING':0}, index=['SYMBOL']))
     
        return [x.Symbol for x in selected]

    def FineSelectionFunction(self, fine):
        for x in fine:
            if not x.MarketCap:
                continue
            m = x.MarketCap
            z = x.Symbol.Value
            
            if m >= 10000000000:
                self.data.loc[z, "SIZE"] = 1
            elif m <= 1000000000:
                self.data.loc[z, "SIZE"] = 0
            else:
                self.data.loc[z, "SIZE"] = ((m - 1000000000) / 9000000000)
                
        return [x.Symbol for x in fine]