| 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 0.706 Tracking Error 0.108 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |
class UniverseSelection(QCAlgorithm):
sortedFilteredStocks = None
def Initialize(self):
self.SetStartDate(2021, 11, 1) # Set Start Date
self.SetEndDate(2021, 11, 30) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.AddUniverse(self.CoarseSelectionFilter, self.FineSelectionFilter)
self.UniverseSettings.Resolution = Resolution.Minute
#self.UniverseSettings.ExtendedMarketHours = ExtendedMarketHours.Yes
self.UniverseSettings.Leverage = 2
def CoarseSelectionFilter(self, coarse):
sortedStocks = sorted(coarse, key=lambda c: c.DollarVolume, reverse=True)
# options to sort, or filter by are DollarVolume, Volume, Price vs. SMA/EMA, relative volume "ref. examples"
sortedFilteredStocks = [
c.Symbol for c in sortedStocks
if
(
c.HasFundamentalData and
c.Price > 5 and c.Price < 500
)]
selectedStocks = sortedFilteredStocks[:50] #top 10 in the list
return selectedStocks
def FineSelectionFilter(self, fine): #uses fundmental data
sortedStocks = sorted(fine, key=lambda c: c.ValuationRatios.PERatio, reverse=False)
# options to sort, or filter by are "ref. examples"
sortedFilteredStocks = [
c.Symbol for c in sortedStocks
if
(
c.OperationRatios.ROE.OneYear > 0 and
c.OperationRatios.ROA.OneYear > 0
)]
selectedStocks = sortedFilteredStocks[:10] #top 10 in the list
return selectedStocks
def OnSecuritiesChanged(self, changes): #triggered when securities are added/removed from universe
#for security in changes.AddedSecurities:
# self.Debug(security.Symbol)
for security in self.ActiveSecurities:
self.Debug(security)